bump 279 (#10098)

This adds sqlite-vss as an option for a vector database. Contains the
code and a few tests. Tests are passing and the library sqlite-vss is
added as optional as explained in the contributing guidelines. I
adjusted the code for lint/black/ and mypy. It looks that everything is
currently passing.

Adding sqlite-vss was mentioned in this issue:
https://github.com/langchain-ai/langchain/issues/1019.
Also mentioned here in the sqlite-vss repo for the curious:
https://github.com/asg017/sqlite-vss/issues/66

Maintainer tag: @baskaryan

---------

Co-authored-by: Philippe Oger <philippe.oger@adevinta.com>

.github/CONTRIBUTING.md

@@ -33,7 +33,7 @@ best way to get our attention.
 ### 🚩GitHub Issues
 
 Our [issues](https://github.com/hwchase17/langchain/issues) page is kept up to date
-with bugs, improvements, and feature requests. 
+with bugs, improvements, and feature requests.
 
 There is a taxonomy of labels to help with sorting and discovery of issues of interest. Please use these to help
 organize issues.
@@ -44,7 +44,7 @@ If you are adding an issue, please try to keep it focused on a single, modular b
 If two issues are related, or blocking, please link them rather than combining them.
 
 We will try to keep these issues as up to date as possible, though
-with the rapid rate of develop in this field some may get out of date.
+with the rapid rate of development in this field some may get out of date.
 If you notice this happening, please let us know.
 
 ### 🙋Getting Help
@@ -61,11 +61,11 @@ we do not want these to get in the way of getting good code into the codebase.
 
 > **Note:** You can run this repository locally (which is described below) or in a [development container](https://containers.dev/) (which is described in the [.devcontainer folder](https://github.com/hwchase17/langchain/tree/master/.devcontainer)).
 
-This project uses [Poetry](https://python-poetry.org/) as a dependency manager. Check out Poetry's [documentation on how to install it](https://python-poetry.org/docs/#installation) on your system before proceeding.
+This project uses [Poetry](https://python-poetry.org/) v1.5.1 as a dependency manager. Check out Poetry's [documentation on how to install it](https://python-poetry.org/docs/#installation) on your system before proceeding.
 
 ❗Note: If you use `Conda` or `Pyenv` as your environment / package manager, avoid dependency conflicts by doing the following first:
 1. *Before installing Poetry*, create and activate a new Conda env (e.g. `conda create -n langchain python=3.9`)
-2. Install Poetry (see above)
+2. Install Poetry v1.5.1 (see above)
 3. Tell Poetry to use the virtualenv python environment (`poetry config virtualenvs.prefer-active-python true`)
 4. Continue with the following steps.
 
@@ -73,21 +73,21 @@ 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. 
+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
-poetry install -E all
+poetry install --with test
 ```
 
-This will install all requirements for running the package, examples, linting, formatting, tests, and coverage. Note the `-E all` flag will install all optional dependencies necessary for integration testing.
+This will install all requirements for running the package, examples, linting, formatting, tests, and coverage.
 
-❗Note: If you're running Poetry 1.4.1 and receive a `WheelFileValidationError` for `debugpy` during installation, you can try either downgrading to Poetry 1.4.0 or disabling "modern installation" (`poetry config installer.modern-installation false`) and re-install requirements. See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
+❗Note: If during installation you receive a `WheelFileValidationError` for `debugpy`, please make sure you are running Poetry v1.5.1. This bug was present in older versions of Poetry (e.g. 1.4.1) and has been resolved in newer releases. If you are still seeing this bug on v1.5.1, you may also try disabling "modern installation" (`poetry config installer.modern-installation false`) and re-installing requirements. See [this `debugpy` issue](https://github.com/microsoft/debugpy/issues/1246) for more details.
 
-Now, you should be able to run the common tasks in the following section. To double check, run `make test`, all tests should pass. If they don't you may need to pip install additional dependencies, such as `numexpr` and `openapi_schema_pydantic`.
+Now assuming `make` and `pytest` are installed, you should be able to run the common tasks in the following section. To double check, run `make test` under `libs/langchain`, all tests should pass. If they don't, you may need to pip install additional dependencies, such as `numexpr` and `openapi_schema_pydantic`.
 
 ## ✅ Common Tasks
 
@@ -134,7 +134,7 @@ We recognize linting can be annoying - if you do not want to do it, please conta
 ### 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.
+Note that `codespell` finds common typos, so it could have false-positive (correctly spelled but rarely used) and false-negatives (not finding misspelled) words.
 
 To check spelling for this project:
 
@@ -175,9 +175,9 @@ If you're adding a new dependency to Langchain, assume that it will be an option
 that most users won't have it installed.
 
 Users that do not have the dependency installed should be able to **import** your code without
-any side effects (no warnings, no errors, no exceptions). 
+any side effects (no warnings, no errors, no exceptions).
 
-To introduce the dependency to the pyproject.toml file correctly, please do the following: 
+To introduce the dependency to the pyproject.toml file correctly, please do the following:
 
 1. Add the dependency to the main group as an optional dependency
   ```bash
@@ -220,7 +220,7 @@ If you add new logic, please add a unit test.
 
 Integration tests cover logic that requires making calls to outside APIs (often integration with other services).
 
-**warning** Almost no tests should be integration tests. 
+**warning** Almost no tests should be integration tests.
 
   Tests that require making network connections make it difficult for other
   developers to test the code.
@@ -307,4 +307,3 @@ even patch releases may contain [non-backwards-compatible changes](https://semve
 
 If your contribution has made its way into a release, we will want to give you credit on Twitter (only if you want though)!
 If you have a Twitter account you would like us to mention, please let us know in the PR or in another manner.
-

.github/ISSUE_TEMPLATE/bug-report.yml

@@ -1,5 +1,5 @@
 name: "\U0001F41B Bug Report"
-description: Submit a bug report to help us improve LangChain
+description: Submit a bug report to help us improve LangChain. To report a security issue, please instead use the security option below.
 labels: ["02 Bug Report"]
 body:
   - type: markdown

.github/PULL_REQUEST_TEMPLATE.md

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

.github/actions/poetry_setup/action.yml

@@ -15,19 +15,13 @@ inputs:
     description: Poetry version
     required: true
 
-  install-command:
-    description: Command run for installing dependencies
-    required: false
-    default: poetry install
-
   cache-key:
     description: Cache key to use for manual handling of caching
     required: true
 
   working-directory:
-    description: Directory to run install-command in
-    required: false
-    default: ""
+    description: Directory whose poetry.lock file should be cached
+    required: true
 
 runs:
   using: composite
@@ -38,41 +32,35 @@ runs:
         python-version: ${{ inputs.python-version }}
 
     - uses: actions/cache@v3
-      id: cache-pip
-      name: Cache Pip ${{ inputs.python-version }}
+      id: cache-bin-poetry
+      name: Cache Poetry binary - Python ${{ inputs.python-version }}
       env:
-        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "15"
+        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "1"
+      with:
+        path: |
+          /opt/pipx/venvs/poetry
+          /opt/pipx_bin/poetry
+        # This step caches the poetry installation, so make sure it's keyed on the poetry version as well.
+        key: bin-poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-${{ inputs.poetry-version }}
+
+    - name: Install poetry
+      if: steps.cache-bin-poetry.outputs.cache-hit != 'true'
+      shell: bash
+      env:
+        POETRY_VERSION: ${{ inputs.poetry-version }}
+        PYTHON_VERSION: ${{ inputs.python-version }}
+      run: pipx install "poetry==$POETRY_VERSION" --python "python$PYTHON_VERSION" --verbose
+
+    - name: Restore pip and poetry cached dependencies
+      uses: actions/cache@v3
+      env:
+        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "4"
+        WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}
       with:
         path: |
           ~/.cache/pip
-        key: pip-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}
-
-    - run: pipx install poetry==${{ inputs.poetry-version }} --python python${{ inputs.python-version }}
-      shell: bash
-
-    - name: Check Poetry File
-      shell: bash
-      working-directory: ${{ inputs.working-directory }}
-      run: |
-        poetry check
-
-    - name: Check lock file
-      shell: bash
-      working-directory: ${{ inputs.working-directory }}
-      run: |
-        poetry lock --check
-
-    - uses: actions/cache@v3
-      id: cache-poetry
-      env:
-        SEGMENT_DOWNLOAD_TIMEOUT_MIN: "15"
-      with:
-        path: |
           ~/.cache/pypoetry/virtualenvs
           ~/.cache/pypoetry/cache
           ~/.cache/pypoetry/artifacts
-        key: poetry-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles('poetry.lock') }}
-
-    - run: ${{ inputs.install-command }}
-      working-directory: ${{ inputs.working-directory }}
-      shell: bash
+          ${{ env.WORKDIR }}/.venv
+        key: py-deps-${{ runner.os }}-${{ runner.arch }}-py-${{ inputs.python-version }}-poetry-${{ inputs.poetry-version }}-${{ inputs.cache-key }}-${{ hashFiles(format('{0}/**/poetry.lock', env.WORKDIR)) }}

.github/tools/git-restore-mtime

@@ -0,0 +1,606 @@
+#!/usr/bin/env python3
+#
+# git-restore-mtime - Change mtime of files based on commit date of last change
+#
+#    Copyright (C) 2012 Rodrigo Silva (MestreLion) <linux@rodrigosilva.com>
+#
+#    This program is free software: you can redistribute it and/or modify
+#    it under the terms of the GNU General Public License as published by
+#    the Free Software Foundation, either version 3 of the License, or
+#    (at your option) any later version.
+#
+#    This program is distributed in the hope that it will be useful,
+#    but WITHOUT ANY WARRANTY; without even the implied warranty of
+#    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+#    GNU General Public License for more details.
+#
+#    You should have received a copy of the GNU General Public License
+#    along with this program. See <http://www.gnu.org/licenses/gpl.html>
+#
+# Source: https://github.com/MestreLion/git-tools
+# Version: July 13, 2023 (commit hash 5f832e72453e035fccae9d63a5056918d64476a2)
+"""
+Change the modification time (mtime) of files in work tree, based on the
+date of the most recent commit that modified the file, including renames.
+
+Ignores untracked files and uncommitted deletions, additions and renames, and
+by default modifications too.
+---
+Useful prior to generating release tarballs, so each file is archived with a
+date that is similar to the date when the file was actually last modified,
+assuming the actual modification date and its commit date are close.
+"""
+
+# TODO:
+# - Add -z on git whatchanged/ls-files, so we don't deal with filename decoding
+# - When Python is bumped to 3.7, use text instead of universal_newlines on subprocess
+# - Update "Statistics for some large projects" with modern hardware and repositories.
+# - Create a README.md for git-restore-mtime alone. It deserves extensive documentation
+#   - Move Statistics there
+# - See git-extras as a good example on project structure and documentation
+
+# FIXME:
+# - When current dir is outside the worktree, e.g. using --work-tree, `git ls-files`
+#   assume any relative pathspecs are to worktree root, not the current dir. As such,
+#   relative pathspecs may not work.
+# - Renames are tricky:
+#   - R100 should not change mtime, but original name is not on filelist. Should
+#     track renames until a valid (A, M) mtime found and then set on current name.
+#   - Should set mtime for both current and original directories.
+#   - Check mode changes with unchanged blobs?
+# - Check file (A, D) for the directory mtime is not sufficient:
+#   - Renames also change dir mtime, unless rename was on a parent dir
+#   - If most recent change of all files in a dir was a Modification (M),
+#     dir might not be touched at all.
+#   - Dirs containing only subdirectories but no direct files will also
+#     not be touched. They're files' [grand]parent dir, but never their dirname().
+#   - Some solutions:
+#     - After files done, perform some dir processing for missing dirs, finding latest
+#       file (A, D, R)
+#     - Simple approach: dir mtime is the most recent child (dir or file) mtime
+#     - Use a virtual concept of "created at most at" to fill missing info, bubble up
+#       to parents and grandparents
+#   - When handling [grand]parent dirs, stay inside <pathspec>
+# - Better handling of merge commits. `-m` is plain *wrong*. `-c/--cc` is perfect, but
+#   painfully slow. First pass without merge commits is not accurate. Maybe add a new
+#   `--accurate` mode for `--cc`?
+
+if __name__ != "__main__":
+    raise ImportError("{} should not be used as a module.".format(__name__))
+
+import argparse
+import datetime
+import logging
+import os.path
+import shlex
+import signal
+import subprocess
+import sys
+import time
+
+__version__ = "2022.12+dev"
+
+# Update symlinks only if the platform supports not following them
+UPDATE_SYMLINKS = bool(os.utime in getattr(os, 'supports_follow_symlinks', []))
+
+# Call os.path.normpath() only if not in a POSIX platform (Windows)
+NORMALIZE_PATHS = (os.path.sep != '/')
+
+# How many files to process in each batch when re-trying merge commits
+STEPMISSING = 100
+
+# (Extra) keywords for the os.utime() call performed by touch()
+UTIME_KWS = {} if not UPDATE_SYMLINKS else {'follow_symlinks': False}
+
+
+# Command-line interface ######################################################
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description=__doc__.split('\n---')[0])
+
+    group = parser.add_mutually_exclusive_group()
+    group.add_argument('--quiet', '-q', dest='loglevel',
+        action="store_const", const=logging.WARNING, default=logging.INFO,
+        help="Suppress informative messages and summary statistics.")
+    group.add_argument('--verbose', '-v', action="count", help="""
+        Print additional information for each processed file.
+        Specify twice to further increase verbosity.
+        """)
+
+    parser.add_argument('--cwd', '-C', metavar="DIRECTORY", help="""
+        Run as if %(prog)s was started in directory %(metavar)s.
+        This affects how --work-tree, --git-dir and PATHSPEC arguments are handled.
+        See 'man 1 git' or 'git --help' for more information.
+        """)
+
+    parser.add_argument('--git-dir', dest='gitdir', metavar="GITDIR", help="""
+        Path to the git repository, by default auto-discovered by searching
+        the current directory and its parents for a .git/ subdirectory.
+        """)
+
+    parser.add_argument('--work-tree', dest='workdir', metavar="WORKTREE", help="""
+        Path to the work tree root, by default the parent of GITDIR if it's
+        automatically discovered, or the current directory if GITDIR is set.
+        """)
+
+    parser.add_argument('--force', '-f', default=False, action="store_true", help="""
+        Force updating files with uncommitted modifications.
+        Untracked files and uncommitted deletions, renames and additions are
+        always ignored.
+        """)
+
+    parser.add_argument('--merge', '-m', default=False, action="store_true", help="""
+        Include merge commits.
+        Leads to more recent times and more files per commit, thus with the same
+        time, which may or may not be what you want.
+        Including merge commits may lead to fewer commits being evaluated as files
+        are found sooner, which can improve performance, sometimes substantially.
+        But as merge commits are usually huge, processing them may also take longer.
+        By default, merge commits are only used for files missing from regular commits.
+        """)
+
+    parser.add_argument('--first-parent', default=False, action="store_true", help="""
+        Consider only the first parent, the "main branch", when evaluating merge commits.
+        Only effective when merge commits are processed, either when --merge is
+        used or when finding missing files after the first regular log search.
+        See --skip-missing.
+        """)
+
+    parser.add_argument('--skip-missing', '-s', dest="missing", default=True,
+        action="store_false", help="""
+        Do not try to find missing files.
+        If merge commits were not evaluated with --merge and some files were
+        not found in regular commits, by default %(prog)s searches for these
+        files again in the merge commits.
+        This option disables this retry, so files found only in merge commits
+        will not have their timestamp updated.
+        """)
+
+    parser.add_argument('--no-directories', '-D', dest='dirs', default=True,
+        action="store_false", help="""
+        Do not update directory timestamps.
+        By default, use the time of its most recently created, renamed or deleted file.
+        Note that just modifying a file will NOT update its directory time.
+        """)
+
+    parser.add_argument('--test', '-t', default=False, action="store_true",
+        help="Test run: do not actually update any file timestamp.")
+
+    parser.add_argument('--commit-time', '-c', dest='commit_time', default=False,
+        action='store_true', help="Use commit time instead of author time.")
+
+    parser.add_argument('--oldest-time', '-o', dest='reverse_order', default=False,
+        action='store_true', help="""
+        Update times based on the oldest, instead of the most recent commit of a file.
+        This reverses the order in which the git log is processed to emulate a
+        file "creation" date. Note this will be inaccurate for files deleted and
+        re-created at later dates.
+        """)
+
+    parser.add_argument('--skip-older-than', metavar='SECONDS', type=int, help="""
+        Ignore files that are currently older than %(metavar)s.
+        Useful in workflows that assume such files already have a correct timestamp,
+        as it may improve performance by processing fewer files.
+        """)
+
+    parser.add_argument('--skip-older-than-commit', '-N', default=False,
+        action='store_true', help="""
+        Ignore files older than the timestamp it would be updated to.
+        Such files may be considered "original", likely in the author's repository.
+        """)
+
+    parser.add_argument('--unique-times', default=False, action="store_true", help="""
+        Set the microseconds to a unique value per commit.
+        Allows telling apart changes that would otherwise have identical timestamps,
+        as git's time accuracy is in seconds.
+        """)
+
+    parser.add_argument('pathspec', nargs='*', metavar='PATHSPEC', help="""
+        Only modify paths matching %(metavar)s, relative to current directory.
+        By default, update all but untracked files and submodules.
+        """)
+
+    parser.add_argument('--version', '-V', action='version',
+        version='%(prog)s version {version}'.format(version=get_version()))
+
+    args_ = parser.parse_args()
+    if args_.verbose:
+        args_.loglevel = max(logging.TRACE, logging.DEBUG // args_.verbose)
+    args_.debug = args_.loglevel <= logging.DEBUG
+    return args_
+
+
+def get_version(version=__version__):
+    if not version.endswith('+dev'):
+        return version
+    try:
+        cwd = os.path.dirname(os.path.realpath(__file__))
+        return Git(cwd=cwd, errors=False).describe().lstrip('v')
+    except Git.Error:
+        return '-'.join((version, "unknown"))
+
+
+# Helper functions ############################################################
+
+def setup_logging():
+    """Add TRACE logging level and corresponding method, return the root logger"""
+    logging.TRACE = TRACE = logging.DEBUG // 2
+    logging.Logger.trace = lambda _, m, *a, **k: _.log(TRACE, m, *a, **k)
+    return logging.getLogger()
+
+
+def normalize(path):
+    r"""Normalize paths from git, handling non-ASCII characters.
+
+    Git stores paths as UTF-8 normalization form C.
+    If path contains non-ASCII or non-printable characters, git outputs the UTF-8
+    in octal-escaped notation, escaping double-quotes and backslashes, and then
+    double-quoting the whole path.
+    https://git-scm.com/docs/git-config#Documentation/git-config.txt-corequotePath
+
+    This function reverts this encoding, so:
+    normalize(r'"Back\\slash_double\"quote_a\303\247a\303\255"') =>
+        r'Back\slash_double"quote_açaí')
+
+    Paths with invalid UTF-8 encoding, such as single 0x80-0xFF bytes (e.g, from
+    Latin1/Windows-1251 encoding) are decoded using surrogate escape, the same
+    method used by Python for filesystem paths. So 0xE6 ("æ" in Latin1, r'\\346'
+    from Git) is decoded as "\udce6". See https://peps.python.org/pep-0383/ and
+    https://vstinner.github.io/painful-history-python-filesystem-encoding.html
+
+    Also see notes on `windows/non-ascii-paths.txt` about path encodings on
+    non-UTF-8 platforms and filesystems.
+    """
+    if path and path[0] == '"':
+        # Python 2: path = path[1:-1].decode("string-escape")
+        # Python 3: https://stackoverflow.com/a/46650050/624066
+        path = (path[1:-1]                 # Remove enclosing double quotes
+                .encode('latin1')          # Convert to bytes, required by 'unicode-escape'
+                .decode('unicode-escape')  # Perform the actual octal-escaping decode
+                .encode('latin1')          # 1:1 mapping to bytes, UTF-8 encoded
+                .decode('utf8', 'surrogateescape'))  # Decode from UTF-8
+    if NORMALIZE_PATHS:
+        # Make sure the slash matches the OS; for Windows we need a backslash
+        path = os.path.normpath(path)
+    return path
+
+
+def dummy(*_args, **_kwargs):
+    """No-op function used in dry-run tests"""
+
+
+def touch(path, mtime):
+    """The actual mtime update"""
+    os.utime(path, (mtime, mtime), **UTIME_KWS)
+
+
+def touch_ns(path, mtime_ns):
+    """The actual mtime update, using nanoseconds for unique timestamps"""
+    os.utime(path, None, ns=(mtime_ns, mtime_ns), **UTIME_KWS)
+
+
+def isodate(secs: int):
+    # time.localtime() accepts floats, but discards fractional part
+    return time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(secs))
+
+
+def isodate_ns(ns: int):
+    # for integers fromtimestamp() is equivalent and ~16% slower than isodate()
+    return datetime.datetime.fromtimestamp(ns / 1000000000).isoformat(sep=' ')
+
+
+def get_mtime_ns(secs: int, idx: int):
+    # Time resolution for filesystems and functions:
+    # ext-4 and other POSIX filesystems: 1 nanosecond
+    # NTFS (Windows default): 100 nanoseconds
+    # datetime.datetime() (due to 64-bit float epoch): 1 microsecond
+    us = idx % 1000000  # 10**6
+    return 1000 * (1000000 * secs + us)
+
+
+def get_mtime_path(path):
+    return os.path.getmtime(path)
+
+
+# Git class and parse_log(), the heart of the script ##########################
+
+class Git:
+    def __init__(self, workdir=None, gitdir=None, cwd=None, errors=True):
+        self.gitcmd = ['git']
+        self.errors = errors
+        self._proc = None
+        if workdir: self.gitcmd.extend(('--work-tree', workdir))
+        if gitdir:  self.gitcmd.extend(('--git-dir',   gitdir))
+        if cwd:     self.gitcmd.extend(('-C',          cwd))
+        self.workdir, self.gitdir = self._get_repo_dirs()
+
+    def ls_files(self, paths: list = None):
+        return (normalize(_) for _ in self._run('ls-files --full-name', paths))
+
+    def ls_dirty(self, force=False):
+        return (normalize(_[3:].split(' -> ', 1)[-1])
+                for _ in self._run('status --porcelain')
+                if _[:2] != '??' and (not force or (_[0] in ('R', 'A')
+                                                    or _[1] == 'D')))
+
+    def log(self, merge=False, first_parent=False, commit_time=False,
+            reverse_order=False, paths: list = None):
+        cmd = 'whatchanged --pretty={}'.format('%ct' if commit_time else '%at')
+        if merge:         cmd += ' -m'
+        if first_parent:  cmd += ' --first-parent'
+        if reverse_order: cmd += ' --reverse'
+        return self._run(cmd, paths)
+
+    def describe(self):
+        return self._run('describe --tags', check=True)[0]
+
+    def terminate(self):
+        if self._proc is None:
+            return
+        try:
+            self._proc.terminate()
+        except OSError:
+            # Avoid errors on OpenBSD
+            pass
+
+    def _get_repo_dirs(self):
+        return (os.path.normpath(_) for _ in
+            self._run('rev-parse --show-toplevel --absolute-git-dir', check=True))
+
+    def _run(self, cmdstr: str, paths: list = None, output=True, check=False):
+        cmdlist = self.gitcmd + shlex.split(cmdstr)
+        if paths:
+            cmdlist.append('--')
+            cmdlist.extend(paths)
+        popen_args = dict(universal_newlines=True, encoding='utf8')
+        if not self.errors:
+            popen_args['stderr'] = subprocess.DEVNULL
+        log.trace("Executing: %s", ' '.join(cmdlist))
+        if not output:
+            return subprocess.call(cmdlist, **popen_args)
+        if check:
+            try:
+                stdout: str = subprocess.check_output(cmdlist, **popen_args)
+                return stdout.splitlines()
+            except subprocess.CalledProcessError as e:
+                raise self.Error(e.returncode, e.cmd, e.output, e.stderr)
+        self._proc = subprocess.Popen(cmdlist, stdout=subprocess.PIPE, **popen_args)
+        return (_.rstrip() for _ in self._proc.stdout)
+
+    def __del__(self):
+        self.terminate()
+
+    class Error(subprocess.CalledProcessError):
+        """Error from git executable"""
+
+
+def parse_log(filelist, dirlist, stats, git, merge=False, filterlist=None):
+    mtime = 0
+    datestr = isodate(0)
+    for line in git.log(
+            merge,
+            args.first_parent,
+            args.commit_time,
+            args.reverse_order,
+            filterlist
+    ):
+        stats['loglines'] += 1
+
+        # Blank line between Date and list of files
+        if not line:
+            continue
+
+        # Date line
+        if line[0] != ':':  # Faster than `not line.startswith(':')`
+            stats['commits'] += 1
+            mtime = int(line)
+            if args.unique_times:
+                mtime = get_mtime_ns(mtime, stats['commits'])
+            if args.debug:
+                datestr = isodate(mtime)
+            continue
+
+        # File line: three tokens if it describes a renaming, otherwise two
+        tokens = line.split('\t')
+
+        # Possible statuses:
+        # M: Modified (content changed)
+        # A: Added (created)
+        # D: Deleted
+        # T: Type changed: to/from regular file, symlinks, submodules
+        # R099: Renamed (moved), with % of unchanged content. 100 = pure rename
+        # Not possible in log: C=Copied, U=Unmerged, X=Unknown, B=pairing Broken
+        status = tokens[0].split(' ')[-1]
+        file = tokens[-1]
+
+        # Handles non-ASCII chars and OS path separator
+        file = normalize(file)
+
+        def do_file():
+            if args.skip_older_than_commit and get_mtime_path(file) <= mtime:
+                stats['skip'] += 1
+                return
+            if args.debug:
+                log.debug("%d\t%d\t%d\t%s\t%s",
+                          stats['loglines'], stats['commits'], stats['files'],
+                          datestr, file)
+            try:
+                touch(os.path.join(git.workdir, file), mtime)
+                stats['touches'] += 1
+            except Exception as e:
+                log.error("ERROR: %s: %s", e, file)
+                stats['errors'] += 1
+
+        def do_dir():
+            if args.debug:
+                log.debug("%d\t%d\t-\t%s\t%s",
+                          stats['loglines'], stats['commits'],
+                          datestr, "{}/".format(dirname or '.'))
+            try:
+                touch(os.path.join(git.workdir, dirname), mtime)
+                stats['dirtouches'] += 1
+            except Exception as e:
+                log.error("ERROR: %s: %s", e, dirname)
+                stats['direrrors'] += 1
+
+        if file in filelist:
+            stats['files'] -= 1
+            filelist.remove(file)
+            do_file()
+
+        if args.dirs and status in ('A', 'D'):
+            dirname = os.path.dirname(file)
+            if dirname in dirlist:
+                dirlist.remove(dirname)
+                do_dir()
+
+        # All files done?
+        if not stats['files']:
+            git.terminate()
+            return
+
+
+# Main Logic ##################################################################
+
+def main():
+    start = time.time()  # yes, Wall time. CPU time is not realistic for users.
+    stats = {_: 0 for _ in ('loglines', 'commits', 'touches', 'skip', 'errors',
+                            'dirtouches', 'direrrors')}
+
+    logging.basicConfig(level=args.loglevel, format='%(message)s')
+    log.trace("Arguments: %s", args)
+
+    # First things first: Where and Who are we?
+    if args.cwd:
+        log.debug("Changing directory: %s", args.cwd)
+        try:
+            os.chdir(args.cwd)
+        except OSError as e:
+            log.critical(e)
+            return e.errno
+    # Using both os.chdir() and `git -C` is redundant, but might prevent side effects
+    # `git -C` alone could be enough if we make sure that:
+    # - all paths, including args.pathspec, are processed by git: ls-files, rev-parse
+    # - touch() / os.utime() path argument is always prepended with git.workdir
+    try:
+        git = Git(workdir=args.workdir, gitdir=args.gitdir, cwd=args.cwd)
+    except Git.Error as e:
+        # Not in a git repository, and git already informed user on stderr. So we just...
+        return e.returncode
+
+    # Get the files managed by git and build file list to be processed
+    if UPDATE_SYMLINKS and not args.skip_older_than:
+        filelist = set(git.ls_files(args.pathspec))
+    else:
+        filelist = set()
+        for path in git.ls_files(args.pathspec):
+            fullpath = os.path.join(git.workdir, path)
+
+            # Symlink (to file, to dir or broken - git handles the same way)
+            if not UPDATE_SYMLINKS and os.path.islink(fullpath):
+                log.warning("WARNING: Skipping symlink, no OS support for updates: %s",
+                            path)
+                continue
+
+            # skip files which are older than given threshold
+            if (args.skip_older_than
+                    and start - get_mtime_path(fullpath) > args.skip_older_than):
+                continue
+
+            # Always add files relative to worktree root
+            filelist.add(path)
+
+    # If --force, silently ignore uncommitted deletions (not in the filesystem)
+    # and renames / additions (will not be found in log anyway)
+    if args.force:
+        filelist -= set(git.ls_dirty(force=True))
+    # Otherwise, ignore any dirty files
+    else:
+        dirty = set(git.ls_dirty())
+        if dirty:
+            log.warning("WARNING: Modified files in the working directory were ignored."
+                "\nTo include such files, commit your changes or use --force.")
+            filelist -= dirty
+
+    # Build dir list to be processed
+    dirlist = set(os.path.dirname(_) for _ in filelist) if args.dirs else set()
+
+    stats['totalfiles'] = stats['files'] = len(filelist)
+    log.info("{0:,} files to be processed in work dir".format(stats['totalfiles']))
+
+    if not filelist:
+        # Nothing to do. Exit silently and without errors, just like git does
+        return
+
+    # Process the log until all files are 'touched'
+    log.debug("Line #\tLog #\tF.Left\tModification Time\tFile Name")
+    parse_log(filelist, dirlist, stats, git, args.merge, args.pathspec)
+
+    # Missing files
+    if filelist:
+        # Try to find them in merge logs, if not done already
+        # (usually HUGE, thus MUCH slower!)
+        if args.missing and not args.merge:
+            filterlist = list(filelist)
+            missing = len(filterlist)
+            log.info("{0:,} files not found in log, trying merge commits".format(missing))
+            for i in range(0, missing, STEPMISSING):
+                parse_log(filelist, dirlist, stats, git,
+                          merge=True, filterlist=filterlist[i:i + STEPMISSING])
+
+        # Still missing some?
+        for file in filelist:
+            log.warning("WARNING: not found in the log: %s", file)
+
+    # Final statistics
+    # Suggestion: use git-log --before=mtime to brag about skipped log entries
+    def log_info(msg, *a, width=13):
+        ifmt = '{:%d,}'    % (width,)  # not using 'n' for consistency with ffmt
+        ffmt = '{:%d,.2f}' % (width,)
+        # %-formatting lacks a thousand separator, must pre-render with .format()
+        log.info(msg.replace('%d', ifmt).replace('%f', ffmt).format(*a))
+
+    log_info(
+        "Statistics:\n"
+        "%f seconds\n"
+        "%d log lines processed\n"
+        "%d commits evaluated",
+        time.time() - start, stats['loglines'], stats['commits'])
+
+    if args.dirs:
+        if stats['direrrors']: log_info("%d directory update errors", stats['direrrors'])
+        log_info("%d directories updated", stats['dirtouches'])
+
+    if stats['touches'] != stats['totalfiles']:
+                        log_info("%d files",              stats['totalfiles'])
+    if stats['skip']:   log_info("%d files skipped",      stats['skip'])
+    if stats['files']:  log_info("%d files missing",      stats['files'])
+    if stats['errors']: log_info("%d file update errors", stats['errors'])
+
+    log_info("%d files updated", stats['touches'])
+
+    if args.test:
+        log.info("TEST RUN - No files modified!")
+
+
+# Keep only essential, global assignments here. Any other logic must be in main()
+log = setup_logging()
+args = parse_args()
+
+# Set the actual touch() and other functions based on command-line arguments
+if args.unique_times:
+    touch = touch_ns
+    isodate = isodate_ns
+
+# Make sure this is always set last to ensure --test behaves as intended
+if args.test:
+    touch = dummy
+
+# UI done, it's showtime!
+try:
+    sys.exit(main())
+except KeyboardInterrupt:
+    log.info("\nAborting")
+    signal.signal(signal.SIGINT, signal.SIG_DFL)
+    os.kill(os.getpid(), signal.SIGINT)

.github/workflows/_lint.yml

@@ -9,38 +9,134 @@ on:
         description: "From which folder this pipeline executes"
 
 env:
-  POETRY_VERSION: "1.4.2"
+  POETRY_VERSION: "1.5.1"
+  WORKDIR: ${{ inputs.working-directory == '' && '.' || inputs.working-directory }}
 
 jobs:
   build:
-    defaults:
-      run:
-        working-directory: ${{ inputs.working-directory }}
     runs-on: ubuntu-latest
+    env:
+      # This number is set "by eye": we want it to be big enough
+      # so that it's bigger than the number of commits in any reasonable PR,
+      # and also as small as possible since increasing the number makes
+      # the initial `git fetch` slower.
+      FETCH_DEPTH: 50
     strategy:
       matrix:
+        # Only lint on the min and max supported Python versions.
+        # It's extremely unlikely that there's a lint issue on any version in between
+        # that doesn't show up on the min or max versions.
+        #
+        # GitHub rate-limits how many jobs can be running at any one time.
+        # Starting new jobs is also relatively slow,
+        # so linting on fewer versions makes CI faster.
         python-version:
           - "3.8"
-          - "3.9"
-          - "3.10"
           - "3.11"
     steps:
       - uses: actions/checkout@v3
-      - name: Install poetry
+        with:
+          # Fetch the last FETCH_DEPTH commits, so the mtime-changing script
+          # can accurately set the mtimes of files modified in the last FETCH_DEPTH commits.
+          fetch-depth: ${{ env.FETCH_DEPTH }}
+      - name: Restore workdir file mtimes to last-edited commit date
+        id: restore-mtimes
+        # This is needed to make black caching work.
+        # Black's cache uses file (mtime, size) to check whether a lookup is a cache hit.
+        # Without this command, files in the repo would have the current time as the modified time,
+        # since the previous action step just created them.
+        # This command resets the mtime to the last time the files were modified in git instead,
+        # which is a high-quality and stable representation of the last modification date.
         run: |
-          pipx install poetry==$POETRY_VERSION
-      - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v4
+          # Important considerations:
+          # - These commands run at base of the repo, since we never `cd` to the `WORKDIR`.
+          # - We only want to alter mtimes for Python files, since that's all black checks.
+          # - We don't need to alter mtimes for directories, since black doesn't look at those.
+          # - We also only alter mtimes inside the `WORKDIR` since that's all we'll lint.
+          # - This should run before `poetry install`, because poetry's venv also contains
+          #   Python files, and we don't want to alter their mtimes since they aren't linted.
+
+          # Ensure we fail on non-zero exits and on undefined variables.
+          # Also print executed commands, for easier debugging.
+          set -eux
+
+          # Restore the mtimes of Python files in the workdir based on git history.
+          .github/tools/git-restore-mtime --no-directories "$WORKDIR/**/*.py"
+
+          # Since CI only does a partial fetch (to `FETCH_DEPTH`) for efficiency,
+          # the local git repo doesn't have full history. There are probably files
+          # that were last modified in a commit *older than* the oldest fetched commit.
+          # After `git-restore-mtime`, such files have a mtime set to the oldest fetched commit.
+          #
+          # As new commits get added, that timestamp will keep moving forward.
+          # If left unchanged, this will make `black` think that the files were edited
+          # more recently than its cache suggests. Instead, we can set their mtime
+          # to a fixed date in the far past that won't change and won't cause cache misses in black.
+          #
+          # For all workdir Python files modified in or before the oldest few fetched commits,
+          # make their mtime be 2000-01-01 00:00:00.
+          OLDEST_COMMIT="$(git log --reverse '--pretty=format:%H' | head -1)"
+          OLDEST_COMMIT_TIME="$(git show -s '--format=%ai' "$OLDEST_COMMIT")"
+          find "$WORKDIR" -name '*.py' -type f -not -newermt "$OLDEST_COMMIT_TIME" -exec touch -c -m -t '200001010000' '{}' '+'
+
+          echo "oldest-commit=$OLDEST_COMMIT" >> "$GITHUB_OUTPUT"
+
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ matrix.python-version }}
-          cache: poetry
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: lint
+
+      - 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
+
       - name: Install dependencies
+        working-directory: ${{ inputs.working-directory }}
         run: |
           poetry install
+
       - name: Install langchain editable
-        if: ${{ inputs.working-directory != 'langchain' }}
+        working-directory: ${{ inputs.working-directory }}
+        if: ${{ inputs.working-directory != 'libs/langchain' }}
         run: |
           pip install -e ../langchain
+
+      - name: Restore black cache
+        uses: actions/cache@v3
+        env:
+          CACHE_BASE: black-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }}
+          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "1"
+        with:
+          path: |
+            ${{ env.WORKDIR }}/.black_cache
+          key: ${{ env.CACHE_BASE }}-${{ steps.restore-mtimes.outputs.oldest-commit }}
+          restore-keys:
+            # If we can't find an exact match for our cache key, accept any with this prefix.
+            ${{ env.CACHE_BASE }}-
+
+      - name: Get .mypy_cache to speed up mypy
+        uses: actions/cache@v3
+        env:
+          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
+        with:
+          path: |
+            ${{ env.WORKDIR }}/.mypy_cache
+          key: mypy-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }}
+
       - name: Analysing the code with our lint
+        working-directory: ${{ inputs.working-directory }}
+        env:
+          BLACK_CACHE_DIR: .black_cache
         run: |
           make lint

.github/workflows/_pydantic_compatibility.yml

@@ -0,0 +1,81 @@
+name: pydantic v1/v2 compatibility
+
+on:
+  workflow_call:
+    inputs:
+      working-directory:
+        required: true
+        type: string
+        description: "From which folder this pipeline executes"
+
+env:
+  POETRY_VERSION: "1.5.1"
+
+jobs:
+  build:
+    defaults:
+      run:
+        working-directory: ${{ inputs.working-directory }}
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
+    name: Pydantic v1/v2 compatibility - Python ${{ matrix.python-version }}
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: pydantic-cross-compat
+
+      - name: Install dependencies
+        shell: bash
+        run: poetry install
+
+      - name: Install the opposite major version of pydantic
+        # If normal tests use pydantic v1, here we'll use v2, and vice versa.
+        shell: bash
+        run: |
+          # Determine the major part of pydantic version
+          REGULAR_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1)
+
+          if [[ "$REGULAR_VERSION" == "1" ]]; then
+            PYDANTIC_DEP=">=2.1,<3"
+            TEST_WITH_VERSION="2"
+          elif [[ "$REGULAR_VERSION" == "2" ]]; then
+            PYDANTIC_DEP="<2"
+            TEST_WITH_VERSION="1"
+          else
+            echo "Unexpected pydantic major version '$REGULAR_VERSION', cannot determine which version to use for cross-compatibility test."
+            exit 1
+          fi
+
+          # Install via `pip` instead of `poetry add` to avoid changing lockfile,
+          # which would prevent caching from working: the cache would get saved
+          # to a different key than where it gets loaded from.
+          poetry run pip install "pydantic${PYDANTIC_DEP}"
+
+          # Ensure that the correct pydantic is installed now.
+          echo "Checking pydantic version... Expecting ${TEST_WITH_VERSION}"
+
+          # Determine the major part of pydantic version
+          CURRENT_VERSION=$(poetry run python -c "import pydantic; print(pydantic.__version__)" | cut -d. -f1)
+
+          # Check that the major part of pydantic version is as expected, if not
+          # raise an error
+          if [[ "$CURRENT_VERSION" != "$TEST_WITH_VERSION" ]]; then
+            echo "Error: expected pydantic version ${CURRENT_VERSION} to have been installed, but found: ${TEST_WITH_VERSION}"
+            exit 1
+          fi
+          echo "Found pydantic version ${CURRENT_VERSION}, as expected"
+      - name: Run pydantic compatibility tests
+        shell: bash
+        run: make test

.github/workflows/_release.yml

@@ -9,26 +9,37 @@ on:
         description: "From which folder this pipeline executes"
 
 env:
-  POETRY_VERSION: "1.4.2"
+  POETRY_VERSION: "1.5.1"
 
 jobs:
   if_release:
-    if: |
-        ${{ github.event.pull_request.merged == true }}
-        && ${{ contains(github.event.pull_request.labels.*.name, 'release') }}
+    # Disallow publishing from branches that aren't `master`.
+    if: github.ref == 'refs/heads/master'
     runs-on: ubuntu-latest
+    permissions:
+      # This permission is used for trusted publishing:
+      # https://blog.pypi.org/posts/2023-04-20-introducing-trusted-publishers/
+      #
+      # Trusted publishing has to also be configured on PyPI for each package:
+      # https://docs.pypi.org/trusted-publishers/adding-a-publisher/
+      id-token: write
+
+      # This permission is needed by `ncipollo/release-action` to create the GitHub release.
+      contents: write
     defaults:
       run:
         working-directory: ${{ inputs.working-directory }}
     steps:
       - uses: actions/checkout@v3
-      - name: Install poetry
-        run: pipx install poetry==$POETRY_VERSION
-      - name: Set up Python 3.10
-        uses: actions/setup-python@v4
+
+      - name: Set up Python + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
         with:
           python-version: "3.10"
-          cache: "poetry"
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ inputs.working-directory }}
+          cache-key: release
+
       - name: Build project for distribution
         run: poetry build
       - name: Check Version
@@ -45,8 +56,9 @@ jobs:
           generateReleaseNotes: true
           tag: v${{ steps.check-version.outputs.version }}
           commit: master
-      - name: Publish to PyPI
-        env:
-          POETRY_PYPI_TOKEN_PYPI: ${{ secrets.PYPI_API_TOKEN }}
-        run: |
-          poetry publish
+      - name: Publish package distributions to PyPI
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages-dir: ${{ inputs.working-directory }}/dist/
+          verbose: true
+          print-hash: true

.github/workflows/_test.yml

@@ -7,13 +7,9 @@ on:
         required: true
         type: string
         description: "From which folder this pipeline executes"
-      test_type:
-        type: string
-        description: "Test types to run"
-        default: '["core", "extended"]'
 
 env:
-  POETRY_VERSION: "1.4.2"
+  POETRY_VERSION: "1.5.1"
 
 jobs:
   build:
@@ -28,34 +24,22 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
-        test_type: ${{ fromJSON(inputs.test_type) }}
-    name: Python ${{ matrix.python-version }} ${{ matrix.test_type }}
+    name: Python ${{ matrix.python-version }}
     steps:
       - uses: actions/checkout@v3
-      - name: Set up Python ${{ matrix.python-version }}
+
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
         uses: "./.github/actions/poetry_setup"
         with:
           python-version: ${{ matrix.python-version }}
+          poetry-version: ${{ env.POETRY_VERSION }}
           working-directory: ${{ inputs.working-directory }}
-          poetry-version: "1.4.2"
-          cache-key: ${{ matrix.test_type }}
-          install-command: |
-              if [ "${{ matrix.test_type }}" == "core" ]; then
-                echo "Running core tests, installing dependencies with poetry..."
-                poetry install
-              else
-                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
-            make test
-          else
-            make extended_tests
-          fi
+          cache-key: core
+
+      - name: Install dependencies
         shell: bash
+        run: poetry install
+
+      - name: Run core tests
+        shell: bash
+        run: make test

.github/workflows/langchain_ci.yml

@@ -8,10 +8,25 @@ on:
     paths:
       - '.github/workflows/_lint.yml'
       - '.github/workflows/_test.yml'
+      - '.github/workflows/_pydantic_compatibility.yml'
       - '.github/workflows/langchain_ci.yml'
       - 'libs/langchain/**'
   workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
 
+# If another push to the same PR or branch happens while this workflow is still running,
+# cancel the earlier run in favor of the next run.
+#
+# There's no point in testing an outdated version of the code. GitHub only allows
+# a limited number of job runners to be active at the same time, so it's better to cancel
+# pointless jobs early so that more useful jobs can run sooner.
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+env:
+  POETRY_VERSION: "1.5.1"
+  WORKDIR: "libs/langchain"
+
 jobs:
   lint:
     uses:
@@ -19,9 +34,50 @@ jobs:
     with:
       working-directory: libs/langchain
     secrets: inherit
+
   test:
     uses:
       ./.github/workflows/_test.yml
     with:
       working-directory: libs/langchain
-    secrets: inherit
+    secrets: inherit
+
+  pydantic-compatibility:
+    uses:
+      ./.github/workflows/_pydantic_compatibility.yml
+    with:
+      working-directory: libs/langchain
+    secrets: inherit
+
+  extended-tests:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: ${{ env.WORKDIR }}
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
+    name: Python ${{ matrix.python-version }} extended tests
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: libs/langchain
+          cache-key: extended
+
+      - name: Install dependencies
+        shell: bash
+        run: |
+          echo "Running extended tests, installing dependencies with poetry..."
+          poetry install -E extended_testing
+
+      - name: Run extended tests
+        run: make extended_tests

.github/workflows/langchain_experimental_ci.yml

@@ -1,5 +1,5 @@
 ---
-name: libs/langchain-experimental CI
+name: libs/experimental CI
 
 on:
   push:
@@ -13,6 +13,20 @@ on:
       - 'libs/experimental/**'
   workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
 
+# If another push to the same PR or branch happens while this workflow is still running,
+# cancel the earlier run in favor of the next run.
+#
+# There's no point in testing an outdated version of the code. GitHub only allows
+# a limited number of job runners to be active at the same time, so it's better to cancel
+# pointless jobs early so that more useful jobs can run sooner.
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+env:
+  POETRY_VERSION: "1.5.1"
+  WORKDIR: "libs/experimental"
+
 jobs:
   lint:
     uses:
@@ -20,10 +34,82 @@ jobs:
     with:
       working-directory: libs/experimental
     secrets: inherit
+
   test:
     uses:
       ./.github/workflows/_test.yml
     with:
       working-directory: libs/experimental
-      test_type: '["core"]'
-    secrets: inherit
+    secrets: inherit
+
+  # It's possible that langchain-experimental works fine with the latest *published* langchain,
+  # but is broken with the langchain on `master`.
+  #
+  # We want to catch situations like that *before* releasing a new langchain, hence this test.
+  test-with-latest-langchain:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: ${{ env.WORKDIR }}
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
+    name: test with unpublished langchain - Python ${{ matrix.python-version }}
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: ${{ env.WORKDIR }}
+          cache-key: unpublished-langchain
+
+      - name: Install dependencies
+        shell: bash
+        run: |
+          echo "Running tests with unpublished langchain, installing dependencies with poetry..."
+          poetry install
+
+          echo "Editably installing langchain outside of poetry, to avoid messing up lockfile..."
+          poetry run pip install -e ../langchain
+
+      - name: Run tests
+        run: make test
+  extended-tests:
+    runs-on: ubuntu-latest
+    defaults:
+      run:
+        working-directory: ${{ env.WORKDIR }}
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
+    name: Python ${{ matrix.python-version }} extended tests
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: libs/experimental
+          cache-key: extended
+
+      - name: Install dependencies
+        shell: bash
+        run: |
+          echo "Running extended tests, installing dependencies with poetry..."
+          poetry install -E extended_testing
+
+      - name: Run extended tests
+        run: make extended_tests

.github/workflows/langchain_experimental_release.yml

@@ -1,14 +1,7 @@
 ---
-name: libs/langchain-experimental Release
+name: libs/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:
@@ -17,4 +10,4 @@ jobs:
       ./.github/workflows/_release.yml
     with:
       working-directory: libs/experimental
-    secrets: inherit
+    secrets: inherit

.github/workflows/langchain_release.yml

@@ -2,13 +2,6 @@
 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:
@@ -17,4 +10,4 @@ jobs:
       ./.github/workflows/_release.yml
     with:
       working-directory: libs/langchain
-    secrets: inherit
+    secrets: inherit

.github/workflows/scheduled_test.yml

@@ -0,0 +1,49 @@
+name: Scheduled tests
+
+on:
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+  schedule:
+    - cron:  '0 13 * * *'
+
+env:
+  POETRY_VERSION: "1.5.1"
+
+jobs:
+  build:
+    defaults:
+      run:
+        working-directory: libs/langchain
+    runs-on: ubuntu-latest
+    environment: Scheduled testing
+    strategy:
+      matrix:
+        python-version:
+          - "3.8"
+          - "3.9"
+          - "3.10"
+          - "3.11"
+    name: Python ${{ matrix.python-version }}
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: "./.github/actions/poetry_setup"
+        with:
+          python-version: ${{ matrix.python-version }}
+          poetry-version: ${{ env.POETRY_VERSION }}
+          working-directory: libs/langchain
+          cache-key: scheduled
+
+      - name: Install dependencies
+        working-directory: libs/langchain
+        shell: bash
+        run: |
+          echo "Running scheduled tests, installing dependencies with poetry..."
+          poetry install --with=test_integration
+
+      - name: Run tests
+        shell: bash
+        env:
+          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
+        run: |
+          make scheduled_tests

README.md

@@ -2,18 +2,18 @@
 
 ⚡ 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)
+[![Release Notes](https://img.shields.io/github/release/langchain-ai/langchain)](https://github.com/langchain-ai/langchain/releases)
+[![CI](https://github.com/langchain-ai/langchain/actions/workflows/langchain_ci.yml/badge.svg)](https://github.com/langchain-ai/langchain/actions/workflows/langchain_ci.yml)
+[![Experimental CI](https://github.com/langchain-ai/langchain/actions/workflows/langchain_experimental_ci.yml/badge.svg)](https://github.com/langchain-ai/langchain/actions/workflows/langchain_experimental_ci.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)
 [![](https://dcbadge.vercel.app/api/server/6adMQxSpJS?compact=true&style=flat)](https://discord.gg/6adMQxSpJS)
-[![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/hwchase17/langchain)
-[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/hwchase17/langchain)
-[![GitHub star chart](https://img.shields.io/github/stars/hwchase17/langchain?style=social)](https://star-history.com/#hwchase17/langchain)
+[![Open in Dev Containers](https://img.shields.io/static/v1?label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/langchain-ai/langchain)
+[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/langchain-ai/langchain)
+[![GitHub star chart](https://img.shields.io/github/stars/langchain-ai/langchain?style=social)](https://star-history.com/#langchain-ai/langchain)
 [![Dependency Status](https://img.shields.io/librariesio/github/langchain-ai/langchain)](https://libraries.io/github/langchain-ai/langchain)
-[![Open Issues](https://img.shields.io/github/issues-raw/hwchase17/langchain)](https://github.com/hwchase17/langchain/issues)
+[![Open Issues](https://img.shields.io/github/issues-raw/langchain-ai/langchain)](https://github.com/langchain-ai/langchain/issues)
 
 
 Looking for the JS/TS version? Check out [LangChain.js](https://github.com/hwchase17/langchainjs).
@@ -21,7 +21,7 @@ Looking for the JS/TS version? Check out [LangChain.js](https://github.com/hwcha
 **Production Support:** As you move your LangChains into production, we'd love to offer more hands-on support.
 Fill out [this form](https://airtable.com/appwQzlErAS2qiP0L/shrGtGaVBVAz7NcV2) to share more about what you're building, and our team will get in touch.
 
-## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28
+## 🚨Breaking Changes for select chains (SQLDatabase) on 7/28/23
 
 In an effort to make `langchain` leaner and safer, we are moving select chains to `langchain_experimental`.
 This migration has already started, but we are remaining backwards compatible until 7/28.

SECURITY.md

@@ -0,0 +1,6 @@
+# Security Policy
+
+## Reporting a Vulnerability
+
+Please report security vulnerabilities by email to `security@langchain.dev`.
+This email is an alias to a subset of our maintainers, and will ensure the issue is promptly triaged and acted upon as needed.

docs/api_reference/conf.py

@@ -156,7 +156,7 @@ html_context = {
 html_static_path = ["_static"]
 
 # These paths are either relative to html_static_path
-# or fully qualified paths (eg. https://...)
+# or fully qualified paths (e.g. https://...)
 html_css_files = [
     "css/custom.css",
 ]

docs/api_reference/create_api_rst.py

@@ -145,30 +145,37 @@ def _load_package_modules(
     package_name = package_path.name
 
     for file_path in package_path.rglob("*.py"):
-        if not file_path.name.startswith("__"):
-            relative_module_name = file_path.relative_to(package_path)
-            # Get the full namespace of the module
-            namespace = str(relative_module_name).replace(".py", "").replace("/", ".")
-            # Keep only the top level namespace
-            top_namespace = namespace.split(".")[0]
+        if file_path.name.startswith("_"):
+            continue
 
-            try:
-                module_members = _load_module_members(
-                    f"{package_name}.{namespace}", namespace
+        relative_module_name = file_path.relative_to(package_path)
+
+        # Skip if any module part starts with an underscore
+        if any(part.startswith("_") for part in relative_module_name.parts):
+            continue
+
+        # Get the full namespace of the module
+        namespace = str(relative_module_name).replace(".py", "").replace("/", ".")
+        # Keep only the top level namespace
+        top_namespace = namespace.split(".")[0]
+
+        try:
+            module_members = _load_module_members(
+                f"{package_name}.{namespace}", namespace
+            )
+            # Merge module members if the namespace already exists
+            if top_namespace in modules_by_namespace:
+                existing_module_members = modules_by_namespace[top_namespace]
+                _module_members = _merge_module_members(
+                    [existing_module_members, module_members]
                 )
-                # Merge module members if the namespace already exists
-                if top_namespace in modules_by_namespace:
-                    existing_module_members = modules_by_namespace[top_namespace]
-                    _module_members = _merge_module_members(
-                        [existing_module_members, module_members]
-                    )
-                else:
-                    _module_members = module_members
+            else:
+                _module_members = module_members
 
-                modules_by_namespace[top_namespace] = _module_members
+            modules_by_namespace[top_namespace] = _module_members
 
-            except ImportError as e:
-                print(f"Error: Unable to import module '{namespace}' with error: {e}")
+        except ImportError as e:
+            print(f"Error: Unable to import module '{namespace}' with error: {e}")
 
     return modules_by_namespace
 
@@ -221,10 +228,10 @@ Classes
     :toctree: {module}
 """
 
-            for class_ in classes:
-                if not class_['is_public']:
+            for class_ in sorted(classes, key=lambda c: c["qualified_name"]):
+                if not class_["is_public"]:
                     continue
-                    
+
                 if class_["kind"] == "TypedDict":
                     template = "typeddict.rst"
                 elif class_["kind"] == "enum":

docs/api_reference/requirements.txt

@@ -1,5 +1,6 @@
 -e libs/langchain
 -e libs/experimental
+pydantic<2
 autodoc_pydantic==1.8.0
 myst_parser
 nbsphinx==0.8.9

docs/docs_skeleton/docs/community.md

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

docs/docs_skeleton/docs/expression_language/index.mdx

@@ -0,0 +1,14 @@
+---
+sidebar_class_name: hidden
+---
+
+# LangChain Expression Language (LCEL)
+
+LangChain Expression Language or LCEL is a declarative way to easily compose chains together.
+Any chain constructed this way will automatically have full sync, async, and streaming support.
+
+#### [Interface](/docs/expression_language/interface)
+The base interface shared by all LCEL objects
+
+#### [Cookbook](/docs/expression_language/cookbook)
+Examples of common LCEL usage patterns

docs/docs_skeleton/docs/get_started/introduction.mdx

@@ -28,7 +28,7 @@ LangChain provides standard, extendable interfaces and external integrations for
 
 #### [Model I/O](/docs/modules/model_io/)
 Interface with language models
-#### [Data connection](/docs/modules/data_connection/)
+#### [Retrieval](/docs/modules/data_connection/)
 Interface with application-specific data
 #### [Chains](/docs/modules/chains/)
 Construct sequences of calls
@@ -42,23 +42,22 @@ Log and stream intermediate steps of any chain
 ## Examples, ecosystem, and resources
 ### [Use cases](/docs/use_cases/)
 Walkthroughs and best-practices for common end-to-end use cases, like:
-- [Chatbots](/docs/use_cases/chatbots/)
+- [Chatbots](/docs/use_cases/chatbots)
 - [Answering questions using sources](/docs/use_cases/question_answering/)
-- [Analyzing structured data](/docs/use_cases/tabular.html)
+- [Analyzing structured data](/docs/use_cases/sql)
 - and much more...
 
 ### [Guides](/docs/guides/)
 Learn best practices for developing with LangChain.
 
 ### [Ecosystem](/docs/ecosystem/)
-LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/integrations/) and [dependent repos](/docs/ecosystem/dependents).
+LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/integrations/) and [dependent repos](/docs/additional_resources/dependents).
 
 ### [Additional resources](/docs/additional_resources/)
 Our community is full of prolific developers, creative builders, and fantastic teachers. Check out [YouTube tutorials](/docs/additional_resources/youtube.html) for great tutorials from folks in the community, and [Gallery](https://github.com/kyrolabs/awesome-langchain) for a list of awesome LangChain projects, compiled by the folks at [KyroLabs](https://kyrolabs.com).
 
-<h3><span style={{color:"#2e8555"}}> Support </span></h3>
-
-Join us on [GitHub](https://github.com/hwchase17/langchain) or [Discord](https://discord.gg/6adMQxSpJS) to ask questions, share feedback, meet other developers building with LangChain, and dream about the future of LLM’s.
+### [Community](/docs/community)
+Head to the [Community navigator](/docs/community) to find places to ask questions, share feedback, meet other developers, and dream about the future of LLM’s.
 
 ## API reference
 

docs/docs_skeleton/docs/get_started/quickstart.mdx

@@ -107,7 +107,7 @@ import PromptTemplateChatModel from "@snippets/get_started/quickstart/prompt_tem
 <PromptTemplateLLM/>
 
 However, the advantages of using these over raw string formatting are several.
-You can "partial" out variables - eg you can format only some of the variables at a time.
+You can "partial" out variables - e.g. you can format only some of the variables at a time.
 You can compose them together, easily combining different templates into a single prompt.
 For explanations of these functionalities, see the [section on prompts](/docs/modules/model_io/prompts) for more detail.
 
@@ -121,12 +121,12 @@ Let's take a look at this below:
 
 ChatPromptTemplates can also include other things besides ChatMessageTemplates - see the [section on prompts](/docs/modules/model_io/prompts) for more detail.
 
-## Output Parsers
+## Output parsers
 
 OutputParsers convert the raw output of an LLM into a format that can be used downstream.
 There are few main type of OutputParsers, including:
 
-- Convert text from LLM -> structured information (eg JSON)
+- Convert text from LLM -> structured information (e.g. JSON)
 - Convert a ChatMessage into just a string
 - Convert the extra information returned from a call besides the message (like OpenAI function invocation) into a string.
 
@@ -149,7 +149,7 @@ import LLMChain from "@snippets/get_started/quickstart/llm_chain.mdx"
 
 <LLMChain/>
 
-## Next Steps
+## Next steps
 
 This is it!
 We've now gone over how to create the core building block of LangChain applications - the LLMChains.

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

@@ -3,7 +3,7 @@ sidebar_position: 3
 ---
 # Comparison Evaluators
 
-Comparison evaluators in LangChain help measure two different chain or LLM outputs. These evaluators are helpful for comparative analyses, such as A/B testing between two language models, or comparing different versions of the same model. They can also be useful for things like generating preference scores for ai-assisted reinforcement learning.
+Comparison evaluators in LangChain help measure two different chains or LLM outputs. These evaluators are helpful for comparative analyses, such as A/B testing between two language models, or comparing different versions of the same model. They can also be useful for things like generating preference scores for ai-assisted reinforcement learning.
 
 These evaluators inherit from the `PairwiseStringEvaluator` class, providing a comparison interface for two strings - typically, the outputs from two different prompts or models, or two versions of the same model. In essence, a comparison evaluator performs an evaluation on a pair of strings and returns a dictionary containing the evaluation score and other relevant details.
 
@@ -16,7 +16,7 @@ Here's a summary of the key methods and properties of a comparison evaluator:
 - `requires_input`: This property indicates whether this evaluator requires an input string.
 - `requires_reference`: This property specifies whether this evaluator requires a reference label.
 
-Detailed information about creating custom evaluators and the available built-in comparison evaluators are provided in the following sections.
+Detailed information about creating custom evaluators and the available built-in comparison evaluators is provided in the following sections.
 
 import DocCardList from "@theme/DocCardList";
 

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

@@ -1,16 +1,12 @@
----
-sidebar_position: 6
----
-
 import DocCardList from "@theme/DocCardList";
 
 # Evaluation
 
 Building applications with language models involves many moving parts. One of the most critical components is ensuring that the outcomes produced by your models are reliable and useful across a broad array of inputs, and that they work well with your application's other software components. Ensuring reliability usually boils down to some combination of application design, testing & evaluation, and runtime checks. 
 
-The guides in this section review the APIs and functionality LangChain provides to help yous better evaluate your applications. Evaluation and testing are both critical when thinking about deploying LLM applications, since production environments require repeatable and useful outcomes.
+The guides in this section review the APIs and functionality LangChain provides to help you better evaluate your applications. Evaluation and testing are both critical when thinking about deploying LLM applications, since production environments require repeatable and useful outcomes.
 
-LangChain offers various types of evaluators to help you measure performance and integrity on diverse data, and we hope to encourage the the community to create and share other useful evaluators so everyone can improve. These docs will introduce the evaluator types, how to use them, and provide some examples of their use in real-world scenarios.
+LangChain offers various types of evaluators to help you measure performance and integrity on diverse data, and we hope to encourage the community to create and share other useful evaluators so everyone can improve. These docs will introduce the evaluator types, how to use them, and provide some examples of their use in real-world scenarios.
 
 Each evaluator type in LangChain comes with ready-to-use implementations and an extensible API that allows for customization according to your unique requirements. Here are some of the types of evaluators we offer:
 

docs/docs_skeleton/docs/guides/expression_language/index.mdx

@@ -1,9 +0,0 @@
-# LangChain Expression Language
-
-import DocCardList from "@theme/DocCardList";
-
-LangChain Expression Language is a declarative way to easily compose chains together.
-Any chain constructed this way will automatically have full sync, async, and streaming support.
-See guides below for how to interact with chains constructed this way as well as cookbook examples.
-
-<DocCardList />

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

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

docs/docs_skeleton/docs/guides/safety/index.mdx

@@ -1,6 +1,7 @@
-# Preventing harmful outputs
+# Moderation
 
 One of the key concerns with using LLMs is that they may generate harmful or unethical text. This is an area of active research in the field. Here we present some built-in chains inspired by this research, which are intended to make the outputs of LLMs safer.
 
-- [Moderation chain](/docs/use_cases/safety/moderation): Explicitly check if any output text is harmful and flag it.
-- [Constitutional chain](/docs/use_cases/safety/constitutional_chain): Prompt the model with a set of principles which should guide it's behavior.
+- [Moderation chain](/docs/guides/safety/moderation): Explicitly check if any output text is harmful and flag it.
+- [Constitutional chain](/docs/guides/safety/constitutional_chain): Prompt the model with a set of principles which should guide it's behavior.
+- [Amazon Comprehend moderation chain](/docs/guides/safety/amazon_comprehend_chain): Use [Amazon Comprehend](https://aws.amazon.com/comprehend/) to detect and handle PII and toxicity.

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

@@ -12,7 +12,7 @@ Here are the agents available in LangChain.
 
 ### [Zero-shot ReAct](/docs/modules/agents/agent_types/react.html)
 
-This agent uses the [ReAct](https://arxiv.org/pdf/2205.00445.pdf) framework to determine which tool to use
+This agent uses the [ReAct](https://arxiv.org/pdf/2210.03629) framework to determine which tool to use
 based solely on the tool's description. Any number of tools can be provided.
 This agent requires that a description is provided for each tool.
 

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

@@ -2,15 +2,60 @@
 sidebar_position: 1
 ---
 
-# Data connection
+# Retrieval
 
-Many LLM applications require user-specific data that is not part of the model's training set. LangChain gives you the 
-building blocks to load, transform, store and query your data via:
+Many LLM applications require user-specific data that is not part of the model's training set.
+The primary way of accomplishing this is through Retrieval Augmented Generation (RAG).
+In this process, external data is *retrieved* and then passed to the LLM when doing the *generation* step.
 
-- [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
-- [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
+LangChain provides all the building blocks for RAG applications - from simple to complex.
+This section of the documentation covers everything related to the *retrieval* step - e.g. the fetching of the data.
+Although this sounds simple, it can be subtly complex.
+This encompasses several key modules.
 
 ![data_connection_diagram](/img/data_connection.jpg)
+
+**[Document loaders](/docs/modules/data_connection/document_loaders/)**
+
+Load documents from many different sources.
+LangChain provides over a 100 different document loaders as well as integrations with other major providers in the space,
+like AirByte and Unstructured.
+We provide integrations to load all types of documents (html, PDF, code) from all types of locations (private s3 buckets, public websites).
+
+**[Document transformers](/docs/modules/data_connection/document_transformers/)**
+
+A key part of retrieval is fetching only the relevant parts of documents.
+This involves several transformation steps in order to best prepare the documents for retrieval.
+One of the primary ones here is splitting (or chunking) a large document into smaller chunks.
+LangChain provides several different algorithms for doing this, as well as logic optimized for specific document types (code, markdown, etc).
+
+**[Text embedding models](/docs/modules/data_connection/text_embedding/)**
+
+Another key part of retrieval has become creating embeddings for documents.
+Embeddings capture the semantic meaning of text, allowing you to quickly and
+efficiently find other pieces of text that are similar.
+LangChain provides integrations with over 25 different embedding providers and methods,
+from open-source to proprietary API,
+allowing you to choose the one best suited for your needs.
+LangChain exposes a standard interface, allowing you to easily swap between models.
+
+**[Vector stores](/docs/modules/data_connection/vectorstores/)**
+
+With the rise of embeddings, there has emerged a need for databases to support efficient storage and searching of these embeddings.
+LangChain provides integrations with over 50 different vectorstores, from open-source local ones to cloud-hosted proprietary ones,
+allowing you choose the one best suited for your needs.
+LangChain exposes a standard interface, allowing you to easily swap between vector stores.
+
+**[Retrievers](/docs/modules/data_connection/retrievers/)**
+
+Once the data is in the database, you still need to retrieve it.
+LangChain supports many different retrieval algorithms and is one of the places where we add the most value.
+We support basic methods that are easy to get started - namely simple semantic search.
+However, we have also added a collection of algorithms on top of this to increase performance.
+These include:
+
+- [Parent Document Retriever](/docs/modules/data_connection/retrievers/parent_document_retriever): This allows you to create multiple embeddings per parent document, allowing you to look up smaller chunks but return larger context.
+- [Self Query Retriever](/docs/modules/data_connection/retrievers/self_query): User questions often contain reference to something that isn't just semantic, but rather expresses some logic that can best be represented as a metadata filter. Self-query allows you to parse out the *semantic* part of a query from other *metadata filters* present in the query
+- [Ensemble Retriever](/docs/modules/data_connection/retrievers/ensemble): Sometimes you may want to retrieve documents from multiple different sources, or using multiple different algorithms. The ensemble retriever allows you to easily do this.
+- And more!
+

docs/docs_skeleton/docs/modules/index.mdx

@@ -8,7 +8,7 @@ LangChain provides standard, extendable interfaces and external integrations for
 
 #### [Model I/O](/docs/modules/model_io/)
 Interface with language models
-#### [Data connection](/docs/modules/data_connection/)
+#### [Retrieval](/docs/modules/data_connection/)
 Interface with application-specific data
 #### [Chains](/docs/modules/chains/)
 Construct sequences of calls

docs/docs_skeleton/docs/modules/memory/types/buffer.mdx

@@ -1,4 +1,4 @@
-# Conversation buffer memory
+# Conversation Buffer
 
 This notebook shows how to use `ConversationBufferMemory`. This memory allows for storing of messages and then extracts the messages in a variable.
 

docs/docs_skeleton/docs/modules/memory/types/buffer_window.mdx

@@ -1,4 +1,4 @@
-# Conversation buffer window memory
+# Conversation Buffer Window
 
 `ConversationBufferWindowMemory` keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so the buffer does not get too large
 

docs/docs_skeleton/docs/modules/memory/types/entity_summary_memory.mdx

@@ -1,4 +1,4 @@
-# Entity memory
+# Entity
 
 Entity Memory remembers given facts about specific entities in a conversation. It extracts information on entities (using an LLM) and builds up its knowledge about that entity over time (also using an LLM).
 

docs/docs_skeleton/docs/modules/memory/types/index.mdx

@@ -4,5 +4,5 @@ sidebar_position: 2
 # Memory Types
 
 There are many different types of memory.
-Each have their own parameters, their own return types, and are useful in different scenarios.
+Each has their own parameters, their own return types, and is useful in different scenarios.
 Please see their individual page for more detail on each one.

docs/docs_skeleton/docs/modules/memory/types/summary.mdx

@@ -1,4 +1,4 @@
-# Conversation summary memory
+# Conversation Summary
 Now let's take a look at using a slightly more complex type of memory - `ConversationSummaryMemory`. This type of memory creates a summary of the conversation over time. This can be useful for condensing information from the conversation over time.
 Conversation summary memory summarizes the conversation as it happens and stores the current summary in memory. This memory can then be used to inject the summary of the conversation so far into a prompt/chain. This memory is most useful for longer conversations, where keeping the past message history in the prompt verbatim would take up too many tokens.
 

docs/docs_skeleton/docs/modules/memory/types/vectorstore_retriever_memory.mdx

@@ -1,4 +1,4 @@
-# Vector store-backed memory
+# Backed by a Vector Store
 
 `VectorStoreRetrieverMemory` stores memories in a VectorDB and queries the top-K most "salient" docs every time it is called.
 

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

@@ -1,6 +1,6 @@
 # Few-shot prompt templates
 
-In this tutorial, we'll learn how to create a prompt template that uses few shot examples. A few shot prompt template can be constructed from either a set of examples, or from an Example Selector object.
+In this tutorial, we'll learn how to create a prompt template that uses few-shot examples. A few-shot prompt template can be constructed from either a set of examples, or from an Example Selector object.
 
 import Example from "@snippets/modules/model_io/prompts/prompt_templates/few_shot_examples.mdx"
 

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

@@ -6,7 +6,7 @@ sidebar_position: 0
 
 Prompt templates are pre-defined recipes for generating prompts for language models.
 
-A template may include instructions, few shot examples, and specific context and
+A template may include instructions, few-shot examples, and specific context and
 questions appropriate for a given task.
 
 LangChain provides tooling to create and work with prompt templates.

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

@@ -1,6 +1,6 @@
 # Partial prompt templates
 
-Like other methods, it can make sense to "partial" a prompt template - eg pass in a subset of the required values, as to create a new prompt template which expects only the remaining subset of values.
+Like other methods, it can make sense to "partial" a prompt template - e.g. pass in a subset of the required values, as to create a new prompt template which expects only the remaining subset of values.
 
 LangChain supports this in two ways:
 1. Partial formatting with string values.

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

@@ -2,8 +2,8 @@
 
 This notebook goes over how to compose multiple prompts together. This can be useful when you want to reuse parts of prompts. This can be done with a PipelinePrompt. A PipelinePrompt consists of two main parts:
 
-- Final prompt: This is the final prompt that is returned
-- Pipeline prompts: This is a list of tuples, consisting of a string name and a prompt template. Each prompt template will be formatted and then passed to future prompt templates as a variable with the same name.
+- Final prompt: The final prompt that is returned
+- Pipeline prompts: A list of tuples, consisting of a string name and a prompt template. Each prompt template will be formatted and then passed to future prompt templates as a variable with the same name.
 
 import Example from "@snippets/modules/model_io/prompts/prompt_templates/prompt_composition.mdx"
 

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

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

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

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

docs/docs_skeleton/package-lock.json

@@ -12,7 +12,7 @@
         "@docusaurus/preset-classic": "2.4.0",
         "@docusaurus/remark-plugin-npm2yarn": "^2.4.0",
         "@mdx-js/react": "^1.6.22",
-        "@mendable/search": "^0.0.137",
+        "@mendable/search": "^0.0.150",
         "clsx": "^1.2.1",
         "json-loader": "^0.5.7",
         "process": "^0.11.10",
@@ -3212,9 +3212,9 @@
       }
     },
     "node_modules/@mendable/search": {
-      "version": "0.0.137",
-      "resolved": "https://registry.npmjs.org/@mendable/search/-/search-0.0.137.tgz",
-      "integrity": "sha512-2J2fd5eqToK+mLzrSDA6NAr4F1kfql7QRiHpD7AUJJX0nqpvInhr/mMJKBCUSCv2z76UKCmF5wLuPSw+C90Qdg==",
+      "version": "0.0.150",
+      "resolved": "https://registry.npmjs.org/@mendable/search/-/search-0.0.150.tgz",
+      "integrity": "sha512-Eb5SeAWlMxzEim/8eJ/Ysn01Pyh39xlPBzRBw/5OyOBhti0HVLXk4wd1Fq2TKgJC2ppQIvhEKO98PUcj9dNDFw==",
       "dependencies": {
         "html-react-parser": "^4.2.0",
         "posthog-js": "^1.45.1"

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": "^0.0.137",
+    "@mendable/search": "^0.0.150",
     "clsx": "^1.2.1",
     "json-loader": "^0.5.7",
     "process": "^0.11.10",

docs/docs_skeleton/sidebars.js

@@ -44,6 +44,16 @@ module.exports = {
         id: "modules/index"
       },
     },
+    {
+      type: "category",
+      label: "LangChain Expression Language",
+      collapsed: true,
+      items: [{ type: "autogenerated", dirName: "expression_language" } ],
+      link: {
+        type: 'doc',
+        id: "expression_language/index"
+      },
+    },
     {
       type: "category",
       label: "Guides",
@@ -52,17 +62,7 @@ module.exports = {
       link: {
         type: 'generated-index',
         description: 'Design guides for key parts of the development process',
-      slug: "guides",
-      },
-    },
-    {
-      type: "category",
-      label: "Ecosystem",
-      collapsed: true,
-      items: [{ type: "autogenerated", dirName: "ecosystem" }],
-      link: {
-        type: 'generated-index',
-      slug: "ecosystem",
+        slug: "guides",
       },
     },
     {
@@ -72,9 +72,10 @@ 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",
       },
     },
+    'community'
   ],
   integrations: [
     {

docs/docs_skeleton/src/theme/CodeBlock/index.js

@@ -24,8 +24,7 @@ function Imports({ imports }) {
           <li key={imported}>
             <a href={docs}>
               <span>{imported}</span>
-            </a>{" "}
-            from <code>{source}</code>
+            </a>
           </li>
         ))}
       </ul>

docs/extras/additional_resources/dependents.mdx

@@ -0,0 +1,376 @@
+# Dependents
+
+Dependents stats for `langchain-ai/langchain`
+
+[![](https://img.shields.io/static/v1?label=Used%20by&message=19495&color=informational&logo=slickpic)](https://github.com/langchain-ai/langchain/network/dependents)
+[![](https://img.shields.io/static/v1?label=Used%20by%20(public)&message=355&color=informational&logo=slickpic)](https://github.com/langchain-ai/langchain/network/dependents)
+[![](https://img.shields.io/static/v1?label=Used%20by%20(private)&message=19140&color=informational&logo=slickpic)](https://github.com/langchain-ai/langchain/network/dependents)
+[![](https://img.shields.io/static/v1?label=Used%20by%20(stars)&message=22524&color=informational&logo=slickpic)](https://github.com/langchain-ai/langchain/network/dependents)
+
+
+[update: `2023-08-17`; only dependent repositories with Stars > 100]
+
+
+| Repository | Stars  |
+| :--------  | -----: |
+|[openai/openai-cookbook](https://github.com/openai/openai-cookbook) | 46276 |
+|[AntonOsika/gpt-engineer](https://github.com/AntonOsika/gpt-engineer) | 41497 |
+|[imartinez/privateGPT](https://github.com/imartinez/privateGPT) | 36296 |
+|[LAION-AI/Open-Assistant](https://github.com/LAION-AI/Open-Assistant) | 34861 |
+|[microsoft/TaskMatrix](https://github.com/microsoft/TaskMatrix) | 33906 |
+|[hpcaitech/ColossalAI](https://github.com/hpcaitech/ColossalAI) | 31654 |
+|[streamlit/streamlit](https://github.com/streamlit/streamlit) | 26571 |
+|[reworkd/AgentGPT](https://github.com/reworkd/AgentGPT) | 25819 |
+|[OpenBB-finance/OpenBBTerminal](https://github.com/OpenBB-finance/OpenBBTerminal) | 23180 |
+|[geekan/MetaGPT](https://github.com/geekan/MetaGPT) | 21968 |
+|[jerryjliu/llama_index](https://github.com/jerryjliu/llama_index) | 20204 |
+|[StanGirard/quivr](https://github.com/StanGirard/quivr) | 20142 |
+|[openai/chatgpt-retrieval-plugin](https://github.com/openai/chatgpt-retrieval-plugin) | 19215 |
+|[mindsdb/mindsdb](https://github.com/mindsdb/mindsdb) | 17580 |
+|[cube-js/cube](https://github.com/cube-js/cube) | 16003 |
+|[PromtEngineer/localGPT](https://github.com/PromtEngineer/localGPT) | 15134 |
+|[mlflow/mlflow](https://github.com/mlflow/mlflow) | 15027 |
+|[chatchat-space/Langchain-Chatchat](https://github.com/chatchat-space/Langchain-Chatchat) | 14024 |
+|[GaiZhenbiao/ChuanhuChatGPT](https://github.com/GaiZhenbiao/ChuanhuChatGPT) | 12020 |
+|[logspace-ai/langflow](https://github.com/logspace-ai/langflow) | 11599 |
+|[openai/evals](https://github.com/openai/evals) | 11509 |
+|[airbytehq/airbyte](https://github.com/airbytehq/airbyte) | 11493 |
+|[databrickslabs/dolly](https://github.com/databrickslabs/dolly) | 10531 |
+|[go-skynet/LocalAI](https://github.com/go-skynet/LocalAI) | 9955 |
+|[AIGC-Audio/AudioGPT](https://github.com/AIGC-Audio/AudioGPT) | 9081 |
+|[gventuri/pandas-ai](https://github.com/gventuri/pandas-ai) | 8201 |
+|[hwchase17/langchainjs](https://github.com/hwchase17/langchainjs) | 7754 |
+|[langgenius/dify](https://github.com/langgenius/dify) | 7348 |
+|[PipedreamHQ/pipedream](https://github.com/PipedreamHQ/pipedream) | 6950 |
+|[h2oai/h2ogpt](https://github.com/h2oai/h2ogpt) | 6858 |
+|[arc53/DocsGPT](https://github.com/arc53/DocsGPT) | 6300 |
+|[0xpayne/gpt-migrate](https://github.com/0xpayne/gpt-migrate) | 6193 |
+|[eosphoros-ai/DB-GPT](https://github.com/eosphoros-ai/DB-GPT) | 6026 |
+|[bentoml/OpenLLM](https://github.com/bentoml/OpenLLM) | 5641 |
+|[jmorganca/ollama](https://github.com/jmorganca/ollama) | 5448 |
+|[e2b-dev/e2b](https://github.com/e2b-dev/e2b) | 5365 |
+|[mage-ai/mage-ai](https://github.com/mage-ai/mage-ai) | 5352 |
+|[wenda-LLM/wenda](https://github.com/wenda-LLM/wenda) | 5192 |
+|[liaokongVFX/LangChain-Chinese-Getting-Started-Guide](https://github.com/liaokongVFX/LangChain-Chinese-Getting-Started-Guide) | 5129 |
+|[zilliztech/GPTCache](https://github.com/zilliztech/GPTCache) | 4993 |
+|[GreyDGL/PentestGPT](https://github.com/GreyDGL/PentestGPT) | 4831 |
+|[zauberzeug/nicegui](https://github.com/zauberzeug/nicegui) | 4824 |
+|[serge-chat/serge](https://github.com/serge-chat/serge) | 4783 |
+|[Shaunwei/RealChar](https://github.com/Shaunwei/RealChar) | 4779 |
+|[gkamradt/langchain-tutorials](https://github.com/gkamradt/langchain-tutorials) | 4752 |
+|[openchatai/OpenChat](https://github.com/openchatai/OpenChat) | 4452 |
+|[intel-analytics/BigDL](https://github.com/intel-analytics/BigDL) | 4286 |
+|[madawei2699/myGPTReader](https://github.com/madawei2699/myGPTReader) | 4167 |
+|[MineDojo/Voyager](https://github.com/MineDojo/Voyager) | 3952 |
+|[embedchain/embedchain](https://github.com/embedchain/embedchain) | 3887 |
+|[postgresml/postgresml](https://github.com/postgresml/postgresml) | 3636 |
+|[assafelovic/gpt-researcher](https://github.com/assafelovic/gpt-researcher) | 3480 |
+|[llm-workflow-engine/llm-workflow-engine](https://github.com/llm-workflow-engine/llm-workflow-engine) | 3445 |
+|[marqo-ai/marqo](https://github.com/marqo-ai/marqo) | 3397 |
+|[kyegomez/tree-of-thoughts](https://github.com/kyegomez/tree-of-thoughts) | 3366 |
+|[RayVentura/ShortGPT](https://github.com/RayVentura/ShortGPT) | 3335 |
+|[Azure-Samples/azure-search-openai-demo](https://github.com/Azure-Samples/azure-search-openai-demo) | 3316 |
+|[langchain-ai/chat-langchain](https://github.com/langchain-ai/chat-langchain) | 3270 |
+|[khoj-ai/khoj](https://github.com/khoj-ai/khoj) | 3266 |
+|[PrefectHQ/marvin](https://github.com/PrefectHQ/marvin) | 3176 |
+|[project-baize/baize-chatbot](https://github.com/project-baize/baize-chatbot) | 2999 |
+|[whitead/paper-qa](https://github.com/whitead/paper-qa) | 2932 |
+|[OpenGVLab/InternGPT](https://github.com/OpenGVLab/InternGPT) | 2816 |
+|[continuedev/continue](https://github.com/continuedev/continue) | 2803 |
+|[ParisNeo/lollms-webui](https://github.com/ParisNeo/lollms-webui) | 2679 |
+|[OpenBMB/ToolBench](https://github.com/OpenBMB/ToolBench) | 2673 |
+|[shroominic/codeinterpreter-api](https://github.com/shroominic/codeinterpreter-api) | 2492 |
+|[OpenBMB/BMTools](https://github.com/OpenBMB/BMTools) | 2486 |
+|[GerevAI/gerev](https://github.com/GerevAI/gerev) | 2450 |
+|[SamurAIGPT/EmbedAI](https://github.com/SamurAIGPT/EmbedAI) | 2448 |
+|[Unstructured-IO/unstructured](https://github.com/Unstructured-IO/unstructured) | 2255 |
+|[Mintplex-Labs/anything-llm](https://github.com/Mintplex-Labs/anything-llm) | 2216 |
+|[emptycrown/llama-hub](https://github.com/emptycrown/llama-hub) | 2198 |
+|[homanp/superagent](https://github.com/homanp/superagent) | 2177 |
+|[yanqiangmiffy/Chinese-LangChain](https://github.com/yanqiangmiffy/Chinese-LangChain) | 2144 |
+|[OpenGVLab/Ask-Anything](https://github.com/OpenGVLab/Ask-Anything) | 2092 |
+|[IntelligenzaArtificiale/Free-Auto-GPT](https://github.com/IntelligenzaArtificiale/Free-Auto-GPT) | 2060 |
+|[thomas-yanxin/LangChain-ChatGLM-Webui](https://github.com/thomas-yanxin/LangChain-ChatGLM-Webui) | 2039 |
+|[NVIDIA/NeMo-Guardrails](https://github.com/NVIDIA/NeMo-Guardrails) | 1992 |
+|[Farama-Foundation/PettingZoo](https://github.com/Farama-Foundation/PettingZoo) | 1949 |
+|[hwchase17/notion-qa](https://github.com/hwchase17/notion-qa) | 1915 |
+|[paulpierre/RasaGPT](https://github.com/paulpierre/RasaGPT) | 1783 |
+|[jupyterlab/jupyter-ai](https://github.com/jupyterlab/jupyter-ai) | 1761 |
+|[vocodedev/vocode-python](https://github.com/vocodedev/vocode-python) | 1627 |
+|[pinterest/querybook](https://github.com/pinterest/querybook) | 1509 |
+|[psychic-api/psychic](https://github.com/psychic-api/psychic) | 1499 |
+|[Kav-K/GPTDiscord](https://github.com/Kav-K/GPTDiscord) | 1476 |
+|[avinashkranjan/Amazing-Python-Scripts](https://github.com/avinashkranjan/Amazing-Python-Scripts) | 1471 |
+|[hegelai/prompttools](https://github.com/hegelai/prompttools) | 1392 |
+|[jina-ai/langchain-serve](https://github.com/jina-ai/langchain-serve) | 1370 |
+|[Forethought-Technologies/AutoChain](https://github.com/Forethought-Technologies/AutoChain) | 1360 |
+|[keephq/keep](https://github.com/keephq/keep) | 1357 |
+|[ttengwang/Caption-Anything](https://github.com/ttengwang/Caption-Anything) | 1345 |
+|[lunasec-io/lunasec](https://github.com/lunasec-io/lunasec) | 1342 |
+|[agiresearch/OpenAGI](https://github.com/agiresearch/OpenAGI) | 1332 |
+|[noahshinn024/reflexion](https://github.com/noahshinn024/reflexion) | 1314 |
+|[jina-ai/dev-gpt](https://github.com/jina-ai/dev-gpt) | 1314 |
+|[jina-ai/thinkgpt](https://github.com/jina-ai/thinkgpt) | 1313 |
+|[greshake/llm-security](https://github.com/greshake/llm-security) | 1299 |
+|[mmz-001/knowledge_gpt](https://github.com/mmz-001/knowledge_gpt) | 1237 |
+|[101dotxyz/GPTeam](https://github.com/101dotxyz/GPTeam) | 1232 |
+|[richardyc/Chrome-GPT](https://github.com/richardyc/Chrome-GPT) | 1223 |
+|[eyurtsev/kor](https://github.com/eyurtsev/kor) | 1192 |
+|[pluralsh/plural](https://github.com/pluralsh/plural) | 1126 |
+|[juncongmoo/chatllama](https://github.com/juncongmoo/chatllama) | 1117 |
+|[visual-openllm/visual-openllm](https://github.com/visual-openllm/visual-openllm) | 1110 |
+|[poe-platform/api-bot-tutorial](https://github.com/poe-platform/api-bot-tutorial) | 1096 |
+|[refuel-ai/autolabel](https://github.com/refuel-ai/autolabel) | 1080 |
+|[microsoft/X-Decoder](https://github.com/microsoft/X-Decoder) | 1075 |
+|[irgolic/AutoPR](https://github.com/irgolic/AutoPR) | 1068 |
+|[SamurAIGPT/Camel-AutoGPT](https://github.com/SamurAIGPT/Camel-AutoGPT) | 984 |
+|[peterw/Chat-with-Github-Repo](https://github.com/peterw/Chat-with-Github-Repo) | 957 |
+|[chatarena/chatarena](https://github.com/chatarena/chatarena) | 955 |
+|[griptape-ai/griptape](https://github.com/griptape-ai/griptape) | 944 |
+|[psychic-api/rag-stack](https://github.com/psychic-api/rag-stack) | 942 |
+|[nod-ai/SHARK](https://github.com/nod-ai/SHARK) | 909 |
+|[filip-michalsky/SalesGPT](https://github.com/filip-michalsky/SalesGPT) | 899 |
+|[melih-unsal/DemoGPT](https://github.com/melih-unsal/DemoGPT) | 896 |
+|[rlancemartin/auto-evaluator](https://github.com/rlancemartin/auto-evaluator) | 889 |
+|[cirediatpl/FigmaChain](https://github.com/cirediatpl/FigmaChain) | 868 |
+|[seanpixel/Teenage-AGI](https://github.com/seanpixel/Teenage-AGI) | 854 |
+|[cheshire-cat-ai/core](https://github.com/cheshire-cat-ai/core) | 847 |
+|[run-llama/llama-lab](https://github.com/run-llama/llama-lab) | 836 |
+|[corca-ai/EVAL](https://github.com/corca-ai/EVAL) | 818 |
+|[Anil-matcha/ChatPDF](https://github.com/Anil-matcha/ChatPDF) | 798 |
+|[alejandro-ao/ask-multiple-pdfs](https://github.com/alejandro-ao/ask-multiple-pdfs) | 782 |
+|[hwchase17/chat-your-data](https://github.com/hwchase17/chat-your-data) | 748 |
+|[LambdaLabsML/examples](https://github.com/LambdaLabsML/examples) | 741 |
+|[ajndkr/lanarky](https://github.com/ajndkr/lanarky) | 732 |
+|[microsoft/Llama-2-Onnx](https://github.com/microsoft/Llama-2-Onnx) | 722 |
+|[e-johnstonn/BriefGPT](https://github.com/e-johnstonn/BriefGPT) | 710 |
+|[billxbf/ReWOO](https://github.com/billxbf/ReWOO) | 710 |
+|[kennethleungty/Llama-2-Open-Source-LLM-CPU-Inference](https://github.com/kennethleungty/Llama-2-Open-Source-LLM-CPU-Inference) | 707 |
+|[databrickslabs/pyspark-ai](https://github.com/databrickslabs/pyspark-ai) | 704 |
+|[OpenBMB/AgentVerse](https://github.com/OpenBMB/AgentVerse) | 704 |
+|[kreneskyp/ix](https://github.com/kreneskyp/ix) | 692 |
+|[akshata29/entaoai](https://github.com/akshata29/entaoai) | 682 |
+|[promptfoo/promptfoo](https://github.com/promptfoo/promptfoo) | 670 |
+|[getmetal/motorhead](https://github.com/getmetal/motorhead) | 662 |
+|[ruoccofabrizio/azure-open-ai-embeddings-qna](https://github.com/ruoccofabrizio/azure-open-ai-embeddings-qna) | 650 |
+|[YiVal/YiVal](https://github.com/YiVal/YiVal) | 632 |
+|[whyiyhw/chatgpt-wechat](https://github.com/whyiyhw/chatgpt-wechat) | 624 |
+|[SamurAIGPT/ChatGPT-Developer-Plugins](https://github.com/SamurAIGPT/ChatGPT-Developer-Plugins) | 617 |
+|[dot-agent/openagent](https://github.com/dot-agent/openagent) | 602 |
+|[msoedov/langcorn](https://github.com/msoedov/langcorn) | 588 |
+|[namuan/dr-doc-search](https://github.com/namuan/dr-doc-search) | 585 |
+|[microsoft/PodcastCopilot](https://github.com/microsoft/PodcastCopilot) | 581 |
+|[alexanderatallah/window.ai](https://github.com/alexanderatallah/window.ai) | 569 |
+|[StevenGrove/GPT4Tools](https://github.com/StevenGrove/GPT4Tools) | 568 |
+|[xusenlinzy/api-for-open-llm](https://github.com/xusenlinzy/api-for-open-llm) | 559 |
+|[NoDataFound/hackGPT](https://github.com/NoDataFound/hackGPT) | 558 |
+|[langchain-ai/auto-evaluator](https://github.com/langchain-ai/auto-evaluator) | 554 |
+|[yeagerai/yeagerai-agent](https://github.com/yeagerai/yeagerai-agent) | 537 |
+|[FlagOpen/FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding) | 534 |
+|[amosjyng/langchain-visualizer](https://github.com/amosjyng/langchain-visualizer) | 534 |
+|[OpenGenerativeAI/GenossGPT](https://github.com/OpenGenerativeAI/GenossGPT) | 524 |
+|[jina-ai/agentchain](https://github.com/jina-ai/agentchain) | 496 |
+|[mckaywrigley/repo-chat](https://github.com/mckaywrigley/repo-chat) | 495 |
+|[michaelthwan/searchGPT](https://github.com/michaelthwan/searchGPT) | 494 |
+|[explosion/spacy-llm](https://github.com/explosion/spacy-llm) | 492 |
+|[plastic-labs/tutor-gpt](https://github.com/plastic-labs/tutor-gpt) | 490 |
+|[freddyaboulton/gradio-tools](https://github.com/freddyaboulton/gradio-tools) | 488 |
+|[xuwenhao/geektime-ai-course](https://github.com/xuwenhao/geektime-ai-course) | 481 |
+|[tgscan-dev/tgscan](https://github.com/tgscan-dev/tgscan) | 480 |
+|[langchain-ai/langchain-aiplugin](https://github.com/langchain-ai/langchain-aiplugin) | 480 |
+|[mpaepper/content-chatbot](https://github.com/mpaepper/content-chatbot) | 473 |
+|[yvann-hub/Robby-chatbot](https://github.com/yvann-hub/Robby-chatbot) | 471 |
+|[steamship-core/steamship-langchain](https://github.com/steamship-core/steamship-langchain) | 467 |
+|[langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent) | 463 |
+|[jonra1993/fastapi-alembic-sqlmodel-async](https://github.com/jonra1993/fastapi-alembic-sqlmodel-async) | 463 |
+|[continuum-llms/chatgpt-memory](https://github.com/continuum-llms/chatgpt-memory) | 463 |
+|[poe-platform/poe-protocol](https://github.com/poe-platform/poe-protocol) | 441 |
+|[alejandro-ao/langchain-ask-pdf](https://github.com/alejandro-ao/langchain-ask-pdf) | 437 |
+|[Dicklesworthstone/llama_embeddings_fastapi_service](https://github.com/Dicklesworthstone/llama_embeddings_fastapi_service) | 432 |
+|[DataDog/dd-trace-py](https://github.com/DataDog/dd-trace-py) | 431 |
+|[daveebbelaar/langchain-experiments](https://github.com/daveebbelaar/langchain-experiments) | 431 |
+|[jiran214/GPT-vup](https://github.com/jiran214/GPT-vup) | 428 |
+|[Azure-Samples/openai](https://github.com/Azure-Samples/openai) | 419 |
+|[NimbleBoxAI/ChainFury](https://github.com/NimbleBoxAI/ChainFury) | 414 |
+|[CarperAI/OpenELM](https://github.com/CarperAI/OpenELM) | 411 |
+|[daodao97/chatdoc](https://github.com/daodao97/chatdoc) | 404 |
+|[MiuLab/Taiwan-LLaMa](https://github.com/MiuLab/Taiwan-LLaMa) | 402 |
+|[logan-markewich/llama_index_starter_pack](https://github.com/logan-markewich/llama_index_starter_pack) | 399 |
+|[mtenenholtz/chat-twitter](https://github.com/mtenenholtz/chat-twitter) | 394 |
+|[opentensor/bittensor](https://github.com/opentensor/bittensor) | 393 |
+|[showlab/VLog](https://github.com/showlab/VLog) | 392 |
+|[microsoft/sample-app-aoai-chatGPT](https://github.com/microsoft/sample-app-aoai-chatGPT) | 391 |
+|[truera/trulens](https://github.com/truera/trulens) | 390 |
+|[Anil-matcha/Chatbase](https://github.com/Anil-matcha/Chatbase) | 363 |
+|[marella/chatdocs](https://github.com/marella/chatdocs) | 360 |
+|[jondurbin/airoboros](https://github.com/jondurbin/airoboros) | 357 |
+|[mosaicml/examples](https://github.com/mosaicml/examples) | 353 |
+|[wandb/weave](https://github.com/wandb/weave) | 352 |
+|[huchenxucs/ChatDB](https://github.com/huchenxucs/ChatDB) | 350 |
+|[rsaryev/talk-codebase](https://github.com/rsaryev/talk-codebase) | 343 |
+|[steamship-packages/langchain-production-starter](https://github.com/steamship-packages/langchain-production-starter) | 335 |
+|[jerlendds/osintbuddy](https://github.com/jerlendds/osintbuddy) | 335 |
+|[andylokandy/gpt-4-search](https://github.com/andylokandy/gpt-4-search) | 329 |
+|[MagnivOrg/prompt-layer-library](https://github.com/MagnivOrg/prompt-layer-library) | 325 |
+|[personoids/personoids-lite](https://github.com/personoids/personoids-lite) | 319 |
+|[momegas/megabots](https://github.com/momegas/megabots) | 317 |
+|[itamargol/openai](https://github.com/itamargol/openai) | 312 |
+|[intel/intel-extension-for-transformers](https://github.com/intel/intel-extension-for-transformers) | 310 |
+|[monarch-initiative/ontogpt](https://github.com/monarch-initiative/ontogpt) | 310 |
+|[BlackHC/llm-strategy](https://github.com/BlackHC/llm-strategy) | 308 |
+|[Nuggt-dev/Nuggt](https://github.com/Nuggt-dev/Nuggt) | 305 |
+|[cofactoryai/textbase](https://github.com/cofactoryai/textbase) | 304 |
+|[Cheems-Seminar/grounded-segment-any-parts](https://github.com/Cheems-Seminar/grounded-segment-any-parts) | 296 |
+|[onlyphantom/llm-python](https://github.com/onlyphantom/llm-python) | 288 |
+|[morpheuslord/GPT_Vuln-analyzer](https://github.com/morpheuslord/GPT_Vuln-analyzer) | 285 |
+|[sullivan-sean/chat-langchainjs](https://github.com/sullivan-sean/chat-langchainjs) | 280 |
+|[wandb/edu](https://github.com/wandb/edu) | 277 |
+|[austin2035/chatpdf](https://github.com/austin2035/chatpdf) | 275 |
+|[liangwq/Chatglm_lora_multi-gpu](https://github.com/liangwq/Chatglm_lora_multi-gpu) | 273 |
+|[preset-io/promptimize](https://github.com/preset-io/promptimize) | 272 |
+|[Haste171/langchain-chatbot](https://github.com/Haste171/langchain-chatbot) | 271 |
+|[hnawaz007/pythondataanalysis](https://github.com/hnawaz007/pythondataanalysis) | 268 |
+|[JohnSnowLabs/langtest](https://github.com/JohnSnowLabs/langtest) | 268 |
+|[conceptofmind/toolformer](https://github.com/conceptofmind/toolformer) | 263 |
+|[sugarforever/LangChain-Tutorials](https://github.com/sugarforever/LangChain-Tutorials) | 260 |
+|[Safiullah-Rahu/CSV-AI](https://github.com/Safiullah-Rahu/CSV-AI) | 259 |
+|[artitw/text2text](https://github.com/artitw/text2text) | 257 |
+|[bborn/howdoi.ai](https://github.com/bborn/howdoi.ai) | 256 |
+|[JayZeeDesign/researcher-gpt](https://github.com/JayZeeDesign/researcher-gpt) | 252 |
+|[paolorechia/learn-langchain](https://github.com/paolorechia/learn-langchain) | 251 |
+|[ur-whitelab/exmol](https://github.com/ur-whitelab/exmol) | 251 |
+|[Azure-Samples/miyagi](https://github.com/Azure-Samples/miyagi) | 248 |
+|[recalign/RecAlign](https://github.com/recalign/RecAlign) | 243 |
+|[airobotlab/KoChatGPT](https://github.com/airobotlab/KoChatGPT) | 242 |
+|[explodinggradients/ragas](https://github.com/explodinggradients/ragas) | 232 |
+|[kaleido-lab/dolphin](https://github.com/kaleido-lab/dolphin) | 232 |
+|[hwchase17/chroma-langchain](https://github.com/hwchase17/chroma-langchain) | 230 |
+|[eosphoros-ai/DB-GPT-Hub](https://github.com/eosphoros-ai/DB-GPT-Hub) | 229 |
+|[shaman-ai/agent-actors](https://github.com/shaman-ai/agent-actors) | 227 |
+|[gia-guar/JARVIS-ChatGPT](https://github.com/gia-guar/JARVIS-ChatGPT) | 224 |
+|[shamspias/customizable-gpt-chatbot](https://github.com/shamspias/customizable-gpt-chatbot) | 223 |
+|[hwchase17/langchain-streamlit-template](https://github.com/hwchase17/langchain-streamlit-template) | 222 |
+|[alvarosevilla95/autolang](https://github.com/alvarosevilla95/autolang) | 221 |
+|[radi-cho/datasetGPT](https://github.com/radi-cho/datasetGPT) | 221 |
+|[gustavz/DataChad](https://github.com/gustavz/DataChad) | 219 |
+|[pablomarin/GPT-Azure-Search-Engine](https://github.com/pablomarin/GPT-Azure-Search-Engine) | 217 |
+|[su77ungr/CASALIOY](https://github.com/su77ungr/CASALIOY) | 217 |
+|[ennucore/clippinator](https://github.com/ennucore/clippinator) | 211 |
+|[edreisMD/plugnplai](https://github.com/edreisMD/plugnplai) | 210 |
+|[kaarthik108/snowChat](https://github.com/kaarthik108/snowChat) | 210 |
+|[PradipNichite/Youtube-Tutorials](https://github.com/PradipNichite/Youtube-Tutorials) | 206 |
+|[ur-whitelab/chemcrow-public](https://github.com/ur-whitelab/chemcrow-public) | 202 |
+|[CambioML/pykoi](https://github.com/CambioML/pykoi) | 199 |
+|[jbrukh/gpt-jargon](https://github.com/jbrukh/gpt-jargon) | 198 |
+|[LC1332/Chat-Haruhi-Suzumiya](https://github.com/LC1332/Chat-Haruhi-Suzumiya) | 196 |
+|[nicknochnack/LangchainDocuments](https://github.com/nicknochnack/LangchainDocuments) | 196 |
+|[yuanjie-ai/ChatLLM](https://github.com/yuanjie-ai/ChatLLM) | 196 |
+|[plchld/InsightFlow](https://github.com/plchld/InsightFlow) | 196 |
+|[yakami129/VirtualWife](https://github.com/yakami129/VirtualWife) | 194 |
+|[Mintplex-Labs/vector-admin](https://github.com/Mintplex-Labs/vector-admin) | 191 |
+|[SamPink/dev-gpt](https://github.com/SamPink/dev-gpt) | 190 |
+|[yasyf/compress-gpt](https://github.com/yasyf/compress-gpt) | 190 |
+|[benthecoder/ClassGPT](https://github.com/benthecoder/ClassGPT) | 190 |
+|[WongSaang/chatgpt-ui-server](https://github.com/WongSaang/chatgpt-ui-server) | 182 |
+|[voxel51/voxelgpt](https://github.com/voxel51/voxelgpt) | 181 |
+|[hardbyte/qabot](https://github.com/hardbyte/qabot) | 176 |
+|[orgexyz/BlockAGI](https://github.com/orgexyz/BlockAGI) | 174 |
+|[handrew/browserpilot](https://github.com/handrew/browserpilot) | 173 |
+|[miaoshouai/miaoshouai-assistant](https://github.com/miaoshouai/miaoshouai-assistant) | 172 |
+|[microsoft/azure-openai-in-a-day-workshop](https://github.com/microsoft/azure-openai-in-a-day-workshop) | 170 |
+|[kyegomez/swarms](https://github.com/kyegomez/swarms) | 169 |
+|[Azure-Samples/azure-search-power-skills](https://github.com/Azure-Samples/azure-search-power-skills) | 169 |
+|[chakkaradeep/pyCodeAGI](https://github.com/chakkaradeep/pyCodeAGI) | 169 |
+|[ethanyanjiali/minChatGPT](https://github.com/ethanyanjiali/minChatGPT) | 167 |
+|[ccurme/yolopandas](https://github.com/ccurme/yolopandas) | 166 |
+|[ju-bezdek/langchain-decorators](https://github.com/ju-bezdek/langchain-decorators) | 165 |
+|[Azure-Samples/azure-search-openai-demo-csharp](https://github.com/Azure-Samples/azure-search-openai-demo-csharp) | 164 |
+|[fengyuli-dev/multimedia-gpt](https://github.com/fengyuli-dev/multimedia-gpt) | 164 |
+|[grumpyp/aixplora](https://github.com/grumpyp/aixplora) | 162 |
+|[langchain-ai/web-explorer](https://github.com/langchain-ai/web-explorer) | 158 |
+|[JorisdeJong123/7-Days-of-LangChain](https://github.com/JorisdeJong123/7-Days-of-LangChain) | 158 |
+|[shauryr/S2QA](https://github.com/shauryr/S2QA) | 158 |
+|[Azure-Samples/jp-azureopenai-samples](https://github.com/Azure-Samples/jp-azureopenai-samples) | 157 |
+|[AkshitIreddy/Interactive-LLM-Powered-NPCs](https://github.com/AkshitIreddy/Interactive-LLM-Powered-NPCs) | 156 |
+|[ibiscp/LLM-IMDB](https://github.com/ibiscp/LLM-IMDB) | 156 |
+|[jmpaz/promptlib](https://github.com/jmpaz/promptlib) | 156 |
+|[mayooear/private-chatbot-mpt30b-langchain](https://github.com/mayooear/private-chatbot-mpt30b-langchain) | 155 |
+|[homanp/vercel-langchain](https://github.com/homanp/vercel-langchain) | 152 |
+|[mlops-for-all/mlops-for-all.github.io](https://github.com/mlops-for-all/mlops-for-all.github.io) | 151 |
+|[vaibkumr/prompt-optimizer](https://github.com/vaibkumr/prompt-optimizer) | 151 |
+|[Agenta-AI/agenta](https://github.com/Agenta-AI/agenta) | 150 |
+|[Klingefjord/chatgpt-telegram](https://github.com/Klingefjord/chatgpt-telegram) | 149 |
+|[menloparklab/falcon-langchain](https://github.com/menloparklab/falcon-langchain) | 148 |
+|[deeppavlov/dream](https://github.com/deeppavlov/dream) | 146 |
+|[positive666/Prompt-Can-Anything](https://github.com/positive666/Prompt-Can-Anything) | 145 |
+|[menloparklab/langchain-cohere-qdrant-doc-retrieval](https://github.com/menloparklab/langchain-cohere-qdrant-doc-retrieval) | 145 |
+|[realminchoi/babyagi-ui](https://github.com/realminchoi/babyagi-ui) | 145 |
+|[SpecterOps/Nemesis](https://github.com/SpecterOps/Nemesis) | 144 |
+|[Jaseci-Labs/jaseci](https://github.com/Jaseci-Labs/jaseci) | 144 |
+|[summarizepaper/summarizepaper](https://github.com/summarizepaper/summarizepaper) | 142 |
+|[peterw/StoryStorm](https://github.com/peterw/StoryStorm) | 141 |
+|[Aggregate-Intellect/practical-llms](https://github.com/Aggregate-Intellect/practical-llms) | 140 |
+|[streamlit/llm-examples](https://github.com/streamlit/llm-examples) | 140 |
+|[hirokidaichi/wanna](https://github.com/hirokidaichi/wanna) | 140 |
+|[Chainlit/cookbook](https://github.com/Chainlit/cookbook) | 139 |
+|[alphasecio/langchain-examples](https://github.com/alphasecio/langchain-examples) | 139 |
+|[flurb18/AgentOoba](https://github.com/flurb18/AgentOoba) | 139 |
+|[Teahouse-Studios/akari-bot](https://github.com/Teahouse-Studios/akari-bot) | 138 |
+|[yasyf/summ](https://github.com/yasyf/summ) | 138 |
+|[kulltc/chatgpt-sql](https://github.com/kulltc/chatgpt-sql) | 137 |
+|[v7labs/benchllm](https://github.com/v7labs/benchllm) | 135 |
+|[ray-project/langchain-ray](https://github.com/ray-project/langchain-ray) | 134 |
+|[petehunt/langchain-github-bot](https://github.com/petehunt/langchain-github-bot) | 134 |
+|[peterwnjenga/aigent](https://github.com/peterwnjenga/aigent) | 133 |
+|[jina-ai/fastapi-serve](https://github.com/jina-ai/fastapi-serve) | 133 |
+|[retr0reg/Ret2GPT](https://github.com/retr0reg/Ret2GPT) | 132 |
+|[agenthubdev/agenthub_operators](https://github.com/agenthubdev/agenthub_operators) | 131 |
+|[eunomia-bpf/GPTtrace](https://github.com/eunomia-bpf/GPTtrace) | 131 |
+|[solana-labs/chatgpt-plugin](https://github.com/solana-labs/chatgpt-plugin) | 130 |
+|[aurelio-labs/arxiv-bot](https://github.com/aurelio-labs/arxiv-bot) | 130 |
+|[ChuloAI/BrainChulo](https://github.com/ChuloAI/BrainChulo) | 128 |
+|[ssheng/BentoChain](https://github.com/ssheng/BentoChain) | 128 |
+|[mallahyari/drqa](https://github.com/mallahyari/drqa) | 127 |
+|[fixie-ai/fixie-examples](https://github.com/fixie-ai/fixie-examples) | 127 |
+|[davila7/file-gpt](https://github.com/davila7/file-gpt) | 127 |
+|[showlab/UniVTG](https://github.com/showlab/UniVTG) | 125 |
+|[zenml-io/zenml-projects](https://github.com/zenml-io/zenml-projects) | 125 |
+|[RedisVentures/redis-openai-qna](https://github.com/RedisVentures/redis-openai-qna) | 124 |
+|[PJLab-ADG/DriveLikeAHuman](https://github.com/PJLab-ADG/DriveLikeAHuman) | 122 |
+|[prof-frink-lab/slangchain](https://github.com/prof-frink-lab/slangchain) | 122 |
+|[Coding-Crashkurse/Langchain-Full-Course](https://github.com/Coding-Crashkurse/Langchain-Full-Course) | 121 |
+|[ciare-robotics/world-creator](https://github.com/ciare-robotics/world-creator) | 120 |
+|[blob42/Instrukt](https://github.com/blob42/Instrukt) | 120 |
+|[langchain-ai/langsmith-cookbook](https://github.com/langchain-ai/langsmith-cookbook) | 119 |
+|[OpenPluginACI/openplugin](https://github.com/OpenPluginACI/openplugin) | 118 |
+|[defenseunicorns/leapfrogai](https://github.com/defenseunicorns/leapfrogai) | 118 |
+|[sdaaron/QueryGPT](https://github.com/sdaaron/QueryGPT) | 117 |
+|[grumpyp/chroma-langchain-tutorial](https://github.com/grumpyp/chroma-langchain-tutorial) | 117 |
+|[3Alan/DocsMind](https://github.com/3Alan/DocsMind) | 116 |
+|[CodeAlchemyAI/ViLT-GPT](https://github.com/CodeAlchemyAI/ViLT-GPT) | 114 |
+|[emarco177/ice_breaker](https://github.com/emarco177/ice_breaker) | 113 |
+|[nftblackmagic/flask-langchain](https://github.com/nftblackmagic/flask-langchain) | 113 |
+|[log1stics/voice-generator-webui](https://github.com/log1stics/voice-generator-webui) | 112 |
+|[nrl-ai/pautobot](https://github.com/nrl-ai/pautobot) | 110 |
+|[Azure/business-process-automation](https://github.com/Azure/business-process-automation) | 110 |
+|[MedalCollector/Orator](https://github.com/MedalCollector/Orator) | 109 |
+|[wombyz/HormoziGPT](https://github.com/wombyz/HormoziGPT) | 108 |
+|[afaqueumer/DocQA](https://github.com/afaqueumer/DocQA) | 106 |
+|[mortium91/langchain-assistant](https://github.com/mortium91/langchain-assistant) | 106 |
+|[Azure/azure-sdk-tools](https://github.com/Azure/azure-sdk-tools) | 105 |
+|[yeagerai/genworlds](https://github.com/yeagerai/genworlds) | 105 |
+|[AmineDiro/cria](https://github.com/AmineDiro/cria) | 104 |
+|[langchain-ai/text-split-explorer](https://github.com/langchain-ai/text-split-explorer) | 104 |
+|[luisroque/large_laguage_models](https://github.com/luisroque/large_laguage_models) | 104 |
+|[xuwenhao/mactalk-ai-course](https://github.com/xuwenhao/mactalk-ai-course) | 104 |
+|[Open-Swarm-Net/GPT-Swarm](https://github.com/Open-Swarm-Net/GPT-Swarm) | 104 |
+|[langchain-ai/langchain-aws-template](https://github.com/langchain-ai/langchain-aws-template) | 104 |
+|[aws-samples/aws-genai-llm-chatbot](https://github.com/aws-samples/aws-genai-llm-chatbot) | 103 |
+|[crosleythomas/MirrorGPT](https://github.com/crosleythomas/MirrorGPT) | 103 |
+|[Dicklesworthstone/llama2_aided_tesseract](https://github.com/Dicklesworthstone/llama2_aided_tesseract) | 101 |
+
+
+
+_Generated by [github-dependents-info](https://github.com/nvuillam/github-dependents-info)_
+
+`github-dependents-info --repo langchain-ai/langchain --markdownfile dependents.md --minstars 100 --sort stars`

docs/extras/additional_resources/tutorials.mdx

@@ -1,15 +1,15 @@
 # Tutorials
 
-Below are links to video tutorials and courses on LangChain. For written guides on common use cases for LangChain, check out the [use cases guides](/docs/use_cases).
+Below are links to tutorials and courses on LangChain. For written guides on common use cases for LangChain, check out the [use cases guides](/docs/use_cases).
 
-⛓ icon marks a new addition [last update 2023-07-05]
+⛓ icon marks a new addition [last update 2023-08-20]
 
 ---------------------
 
 ### 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)
+- [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**
@@ -36,14 +36,14 @@ Below are links to video tutorials and courses on LangChain. For written guides
 - #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)
+- [`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)
+- [Beginner's Guide To 7 Essential Concepts](https://youtu.be/2xxziIWmaSA)
+- [Beginner's 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)
@@ -63,7 +63,7 @@ Below are links to video tutorials and courses on LangChain. For written guides
 - [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)
+- [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)
@@ -73,7 +73,7 @@ Below are links to video tutorials and courses on LangChain. For written guides
 - [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)
+- [`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)
@@ -85,7 +85,7 @@ Below are links to video tutorials and courses on LangChain. For written guides
 - [`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)
+- [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)
@@ -99,7 +99,7 @@ Below are links to video tutorials and courses on LangChain. For written guides
 - [`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)
+- [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)
@@ -107,7 +107,7 @@ Below are links to video tutorials and courses on LangChain. For written guides
 - [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)
+- [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)
@@ -121,5 +121,9 @@ Below are links to video tutorials and courses on LangChain. For written guides
 - [LangChain Agents: Build Personal Assistants For Your Data (Q&A with Harrison Chase and Mayo Oshin)](https://youtu.be/gVkF8cwfBLI)
 
 
+### Codebase Analysis
+- ⛓ [Codebase Analysis: Langchain Agents](https://carbonated-yacht-2c5.notion.site/Codebase-Analysis-Langchain-Agents-0b0587acd50647ca88aaae7cff5df1f2)
+
+
 ---------------------
-⛓ icon marks a new addition [last update 2023-07-05]
+⛓ icon marks a new addition [last update 2023-08-20]

docs/extras/ecosystem/dependents.mdx

@@ -1,265 +0,0 @@
-# Dependents
-
-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)
-
-
-[update: 2023-07-07; 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 |
-
-
-
-_Generated by [github-dependents-info](https://github.com/nvuillam/github-dependents-info)_
-
-[github-dependents-info --repo hwchase17/langchain --markdownfile dependents.md --minstars 100 --sort stars]

docs/extras/expression_language/cookbook.ipynb

@@ -1318,7 +1318,7 @@
    "source": [
     "template = \"\"\"Write some python code to solve the user's problem. \n",
     "\n",
-    "Return only python code in Markdown format, eg:\n",
+    "Return only python code in Markdown format, e.g.:\n",
     "\n",
     "```python\n",
     "....\n",
@@ -1638,16 +1638,6 @@
    "source": [
     "moderated_chain.invoke({\"input\": \"you are stupid\"})"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a0a85ba4-f782-47b8-b16f-8b7a61d6dab7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Conversational Retrieval With Memory"
-   ]
   }
  ],
  "metadata": {

docs/extras/expression_language/interface.ipynb

@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 1,
    "id": "466b65b3",
    "metadata": {},
    "outputs": [],
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "d1850a1f",
    "metadata": {},
    "outputs": [],
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "56d0669f",
    "metadata": {},
    "outputs": [],
@@ -170,6 +170,36 @@
     "chain.batch([{\"topic\": \"bears\"}, {\"topic\": \"cats\"}])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "2434ab15",
+   "metadata": {},
+   "source": [
+    "You can set the number of concurrent requests by using the `max_concurrency` parameter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a08522f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[AIMessage(content=\"Why don't bears wear shoes?\\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False),\n",
+       " AIMessage(content=\"Why don't cats play poker in the wild?\\n\\nToo many cheetahs!\", additional_kwargs={}, example=False)]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain.batch([{\"topic\": \"bears\"}, {\"topic\": \"cats\"}], config={\"max_concurrency\": 5})"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b960cbfe",
@@ -256,6 +286,131 @@
    "source": [
     "await chain.abatch([{\"topic\": \"bears\"}])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0a1c409d",
+   "metadata": {},
+   "source": [
+    "## Parallelism\n",
+    "\n",
+    "Let's take a look at how LangChain Expression Language support parralel requests as much as possible. For example, when using a RunnableMapping (often written as a dictionary) it executes each element in parralel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e3014c7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.schema.runnable import RunnableMap\n",
+    "chain1 = ChatPromptTemplate.from_template(\"tell me a joke about {topic}\") | model\n",
+    "chain2 = ChatPromptTemplate.from_template(\"write a short (2 line) poem about {topic}\") | model\n",
+    "combined = RunnableMap({\n",
+    "    \"joke\": chain1,\n",
+    "    \"poem\": chain2,\n",
+    "})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "08044c0a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 31.7 ms, sys: 8.59 ms, total: 40.3 ms\n",
+      "Wall time: 1.05 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"Why don't bears like fast food?\\n\\nBecause they can't catch it!\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "chain1.invoke({\"topic\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "22c56804",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 42.9 ms, sys: 10.2 ms, total: 53 ms\n",
+      "Wall time: 1.93 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"In forest's embrace, bears roam free,\\nSilent strength, nature's majesty.\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "chain2.invoke({\"topic\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4fff4cbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 96.3 ms, sys: 20.4 ms, total: 117 ms\n",
+      "Wall time: 1.1 s\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'joke': AIMessage(content=\"Why don't bears wear socks?\\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False),\n",
+       " 'poem': AIMessage(content=\"In forest's embrace,\\nMajestic bears leave their trace.\", additional_kwargs={}, example=False)}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "combined.invoke({\"topic\": \"bears\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fab75d1d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -274,7 +429,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/extras/guides/adapters/_category_.yml

@@ -0,0 +1 @@
+label: 'Adapters'

docs/extras/guides/adapters/openai.ipynb

@@ -0,0 +1,323 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "700a516b",
+   "metadata": {},
+   "source": [
+    "# OpenAI Adapter\n",
+    "\n",
+    "A lot of people get started with OpenAI but want to explore other models. LangChain's integrations with many model providers make this easy to do so. While LangChain has it's own message and model APIs, we've also made it as easy as possible to explore other models by exposing an adapter to adapt LangChain models to the OpenAI api.\n",
+    "\n",
+    "At the moment this only deals with output and does not return other information (token counts, stop reasons, etc)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6017f26a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openai\n",
+    "from langchain.adapters import openai as lc_openai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b522ceda",
+   "metadata": {},
+   "source": [
+    "## ChatCompletion.create"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "1d22eb61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = [{\"role\": \"user\", \"content\": \"hi\"}]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d550d3ad",
+   "metadata": {},
+   "source": [
+    "Original OpenAI call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "e1d27dfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = openai.ChatCompletion.create(\n",
+    "    messages=messages, \n",
+    "    model=\"gpt-3.5-turbo\", \n",
+    "    temperature=0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "012d81ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result[\"choices\"][0]['message'].to_dict_recursive()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db5b5500",
+   "metadata": {},
+   "source": [
+    "LangChain OpenAI wrapper call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "87c2d515",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lc_result = lc_openai.ChatCompletion.create(\n",
+    "    messages=messages, \n",
+    "    model=\"gpt-3.5-turbo\", \n",
+    "    temperature=0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c67a5ac8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lc_result[\"choices\"][0]['message']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "034ba845",
+   "metadata": {},
+   "source": [
+    "Swapping out model providers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7a2c011c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lc_result = lc_openai.ChatCompletion.create(\n",
+    "    messages=messages, \n",
+    "    model=\"claude-2\", \n",
+    "    temperature=0, \n",
+    "    provider=\"ChatAnthropic\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f7c94827",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': ' Hello!'}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lc_result[\"choices\"][0]['message']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb3f181d",
+   "metadata": {},
+   "source": [
+    "## ChatCompletion.stream"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7b8cd18",
+   "metadata": {},
+   "source": [
+    "Original OpenAI call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "fd8cb1ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'role': 'assistant', 'content': ''}\n",
+      "{'content': 'Hello'}\n",
+      "{'content': '!'}\n",
+      "{'content': ' How'}\n",
+      "{'content': ' can'}\n",
+      "{'content': ' I'}\n",
+      "{'content': ' assist'}\n",
+      "{'content': ' you'}\n",
+      "{'content': ' today'}\n",
+      "{'content': '?'}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in openai.ChatCompletion.create(\n",
+    "    messages = messages,\n",
+    "    model=\"gpt-3.5-turbo\", \n",
+    "    temperature=0,\n",
+    "    stream=True\n",
+    "):\n",
+    "    print(c[\"choices\"][0]['delta'].to_dict_recursive())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b2a076b",
+   "metadata": {},
+   "source": [
+    "LangChain OpenAI wrapper call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "9521218c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'role': 'assistant', 'content': ''}\n",
+      "{'content': 'Hello'}\n",
+      "{'content': '!'}\n",
+      "{'content': ' How'}\n",
+      "{'content': ' can'}\n",
+      "{'content': ' I'}\n",
+      "{'content': ' assist'}\n",
+      "{'content': ' you'}\n",
+      "{'content': ' today'}\n",
+      "{'content': '?'}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in lc_openai.ChatCompletion.create(\n",
+    "    messages = messages,\n",
+    "    model=\"gpt-3.5-turbo\", \n",
+    "    temperature=0,\n",
+    "    stream=True\n",
+    "):\n",
+    "    print(c[\"choices\"][0]['delta'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fc39750",
+   "metadata": {},
+   "source": [
+    "Swapping out model providers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "68f0214e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'role': 'assistant', 'content': ' Hello'}\n",
+      "{'content': '!'}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in lc_openai.ChatCompletion.create(\n",
+    "    messages = messages,\n",
+    "    model=\"claude-2\", \n",
+    "    temperature=0,\n",
+    "    stream=True,\n",
+    "    provider=\"ChatAnthropic\",\n",
+    "):\n",
+    "    print(c[\"choices\"][0]['delta'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/guides/debugging.md

@@ -8,7 +8,7 @@ Here's a few different tools and functionalities to aid in debugging.
 
 ## Tracing
 
-Platforms with tracing capabilities like [LangSmith](/docs/guides/langsmith/) and [WandB](/docs/ecosystem/integrations/agent_with_wandb_tracing) are the most comprehensive solutions for debugging. These platforms make it easy to not only log and visualize LLM apps, but also to actively debug, test and refine them.
+Platforms with tracing capabilities like [LangSmith](/docs/guides/langsmith/) and [WandB](/docs/integrations/providers/wandb_tracing) are the most comprehensive solutions for debugging. These platforms make it easy to not only log and visualize LLM apps, but also to actively debug, test and refine them.
 
 For anyone building production-grade LLM applications, we highly recommend using a platform like this.
 

docs/extras/guides/deployments/template_repos.mdx

@@ -79,3 +79,7 @@ See OpenLLM's [integration doc](https://github.com/bentoml/OpenLLM#%EF%B8%8F-int
 ## [Databutton](https://databutton.com/home?new-data-app=true)
 
 These templates serve as examples of how to build, deploy, and share LangChain applications using Databutton. You can create user interfaces with Streamlit, automate tasks by scheduling Python code, and store files and data in the built-in store. Examples include a Chatbot interface with conversational memory, a Personal search engine, and a starter template for LangChain apps. Deploying and sharing is just one click away.
+
+## [AzureML Online Endpoint](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/online/llm/langchain/1_langchain_basic_deploy.ipynb)
+
+A minimal example of how to deploy LangChain to an Azure Machine Learning Online Endpoint. 

docs/extras/guides/fallbacks.ipynb

@@ -0,0 +1,430 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "19c9cbd6",
+   "metadata": {},
+   "source": [
+    "# Fallbacks\n",
+    "\n",
+    "When working with language models, you may often encounter issues from the underlying APIs, whether these be rate limiting or downtime. Therefore, as you go to move your LLM applications into production it becomes more and more important to safe guard against these. That's why we've introduced the concept of fallbacks.\n",
+    "\n",
+    "Crucially, fallbacks can be applied not only on the LLM level but on the whole runnable level. This is important because often times different models require different prompts. So if your call to OpenAI fails, you don't just want to send the same prompt to Anthropic - you probably want want to use a different prompt template and send a different version there."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6bb9ba9",
+   "metadata": {},
+   "source": [
+    "## Handling LLM API Errors\n",
+    "\n",
+    "This is maybe the most common use case for fallbacks. A request to an LLM API can fail for a variety of reasons - the API could be down, you could have hit rate limits, any number of things. Therefore, using fallbacks can help protect against these types of things.\n",
+    "\n",
+    "IMPORTANT: By default, a lot of the LLM wrappers catch errors and retry. You will most likely want to turn those off when working with fallbacks. Otherwise the first wrapper will keep on retrying and not failing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "d3e893bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI, ChatAnthropic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4847c82d",
+   "metadata": {},
+   "source": [
+    "First, let's mock out what happens if we hit a RateLimitError from OpenAI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "dfdd8bf5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from unittest.mock import patch\n",
+    "from openai.error import RateLimitError"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e6fdffc1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Note that we set max_retries = 0 to avoid retrying on RateLimits, etc\n",
+    "openai_llm = ChatOpenAI(max_retries=0)\n",
+    "anthropic_llm = ChatAnthropic()\n",
+    "llm = openai_llm.with_fallbacks([anthropic_llm])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "584461ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hit error\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Let's use just the OpenAI LLm first, to show that we run into an error\n",
+    "with patch('openai.ChatCompletion.create', side_effect=RateLimitError()):\n",
+    "    try:\n",
+    "         print(openai_llm.invoke(\"Why did the chicken cross the road?\"))\n",
+    "    except:\n",
+    "        print(\"Hit error\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "4fc1e673",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content=' I don\\'t actually know why the chicken crossed the road, but here are some possible humorous answers:\\n\\n- To get to the other side!\\n\\n- It was too chicken to just stand there. \\n\\n- It wanted a change of scenery.\\n\\n- It wanted to show the possum it could be done.\\n\\n- It was on its way to a poultry farmers\\' convention.\\n\\nThe joke plays on the double meaning of \"the other side\" - literally crossing the road to the other side, or the \"other side\" meaning the afterlife. So it\\'s an anti-joke, with a silly or unexpected pun as the answer.' additional_kwargs={} example=False\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Now let's try with fallbacks to Anthropic\n",
+    "with patch('openai.ChatCompletion.create', side_effect=RateLimitError()):\n",
+    "    try:\n",
+    "         print(llm.invoke(\"Why did the the chicken cross the road?\"))\n",
+    "    except:\n",
+    "        print(\"Hit error\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f00bea25",
+   "metadata": {},
+   "source": [
+    "We can use our \"LLM with Fallbacks\" as we would a normal LLM."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "4f8eaaa0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content=\" I don't actually know why the kangaroo crossed the road, but I can take a guess! Here are some possible reasons:\\n\\n- To get to the other side (the classic joke answer!)\\n\\n- It was trying to find some food or water \\n\\n- It was trying to find a mate during mating season\\n\\n- It was fleeing from a predator or perceived threat\\n\\n- It was disoriented and crossed accidentally \\n\\n- It was following a herd of other kangaroos who were crossing\\n\\n- It wanted a change of scenery or environment \\n\\n- It was trying to reach a new habitat or territory\\n\\nThe real reason is unknown without more context, but hopefully one of those potential explanations does the joke justice! Let me know if you have any other animal jokes I can try to decipher.\" additional_kwargs={} example=False\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You're a nice assistant who always includes a compliment in your response\"),\n",
+    "        (\"human\", \"Why did the {animal} cross the road\"),\n",
+    "    ]\n",
+    ")\n",
+    "chain = prompt | llm\n",
+    "with patch('openai.ChatCompletion.create', side_effect=RateLimitError()):\n",
+    "    try:\n",
+    "         print(chain.invoke({\"animal\": \"kangaroo\"}))\n",
+    "    except:\n",
+    "        print(\"Hit error\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d62241b",
+   "metadata": {},
+   "source": [
+    "## Fallbacks for Sequences\n",
+    "\n",
+    "We can also create fallbacks for sequences, that are sequences themselves. Here we do that with two different models: ChatOpenAI and then normal OpenAI (which does not use a chat model). Because OpenAI is NOT a chat model, you likely want a different prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "6d0b8056",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First let's create a chain with a ChatModel\n",
+    "# We add in a string output parser here so the outputs between the two are the same type\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "\n",
+    "chat_prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You're a nice assistant who always includes a compliment in your response\"),\n",
+    "        (\"human\", \"Why did the {animal} cross the road\"),\n",
+    "    ]\n",
+    ")\n",
+    "# Here we're going to use a bad model name to easily create a chain that will error\n",
+    "chat_model = ChatOpenAI(model_name=\"gpt-fake\")\n",
+    "bad_chain = chat_prompt | chat_model | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "8d1fc2a5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Now lets create a chain with the normal OpenAI model\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.prompts import PromptTemplate\n",
+    "\n",
+    "prompt_template = \"\"\"Instructions: You should always include a compliment in your response.\n",
+    "\n",
+    "Question: Why did the {animal} cross the road?\"\"\"\n",
+    "prompt = PromptTemplate.from_template(prompt_template)\n",
+    "llm = OpenAI()\n",
+    "good_chain = prompt | llm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "283bfa44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\nAnswer: The turtle crossed the road to get to the other side, and I have to say he had some impressive determination.'"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We can now create a final chain which combines the two\n",
+    "chain = bad_chain.with_fallbacks([good_chain])\n",
+    "chain.invoke({\"animal\": \"turtle\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec4685b4",
+   "metadata": {},
+   "source": [
+    "## Handling Long Inputs\n",
+    "\n",
+    "One of the big limiting factors of LLMs in their context window. Usually you can count and track the length of prompts before sending them to an LLM, but in situations where that is hard/complicated you can fallback to a model with longer context length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "564b84c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "short_llm = ChatOpenAI()\n",
+    "long_llm = ChatOpenAI(model=\"gpt-3.5-turbo-16k\")\n",
+    "llm = short_llm.with_fallbacks([long_llm])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "5e27a775",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = \"What is the next number: \" + \", \".join([\"one\", \"two\"] * 3000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "0a502731",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This model's maximum context length is 4097 tokens. However, your messages resulted in 12012 tokens. Please reduce the length of the messages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print(short_llm.invoke(inputs))\n",
+    "except Exception as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "d91ba5d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "content='The next number in the sequence is two.' additional_kwargs={} example=False\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print(llm.invoke(inputs))\n",
+    "except Exception as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a6735df",
+   "metadata": {},
+   "source": [
+    "## Fallback to Better Model\n",
+    "\n",
+    "Often times we ask models to output format in a specific format (like JSON). Models like GPT-3.5 can do this okay, but sometimes struggle. This naturally points to fallbacks - we can try with GPT-3.5 (faster, cheaper), but then if parsing fails we can use GPT-4."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "867a3793",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.output_parsers import DatetimeOutputParser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "b8d9959d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = ChatPromptTemplate.from_template(\n",
+    "    \"what time was {event} (in %Y-%m-%dT%H:%M:%S.%fZ format - only return this value)\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "98087a76",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# In this case we are going to do the fallbacks on the LLM + output parser level\n",
+    "# Because the error will get raised in the OutputParser\n",
+    "openai_35 = ChatOpenAI() | DatetimeOutputParser()\n",
+    "openai_4 = ChatOpenAI(model=\"gpt-4\")| DatetimeOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "17ec9e8f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "only_35 = prompt | openai_35 \n",
+    "fallback_4 = prompt | openai_35.with_fallbacks([openai_4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "7e536f0b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Error: Could not parse datetime string: The Super Bowl in 1994 took place on January 30th at 3:30 PM local time. Converting this to the specified format (%Y-%m-%dT%H:%M:%S.%fZ) results in: 1994-01-30T15:30:00.000Z\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print(only_35.invoke({\"event\": \"the superbowl in 1994\"}))\n",
+    "except Exception as e:\n",
+    "    print(f\"Error: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "01355c5e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1994-01-30 15:30:00\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    print(fallback_4.invoke({\"event\": \"the superbowl in 1994\"}))\n",
+    "except Exception as e:\n",
+    "    print(f\"Error: {e}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c537f9d0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/guides/local_llms.ipynb

@@ -0,0 +1,807 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b8982428",
+   "metadata": {},
+   "source": [
+    "# Run LLMs locally\n",
+    "\n",
+    "## Use case\n",
+    "\n",
+    "The popularity of projects like [PrivateGPT](https://github.com/imartinez/privateGPT), [llama.cpp](https://github.com/ggerganov/llama.cpp), and [GPT4All](https://github.com/nomic-ai/gpt4all) underscore the demand to run LLMs locally (on your own device).\n",
+    "\n",
+    "This has at least two important benefits:\n",
+    "\n",
+    "1. `Privacy`: Your data is not sent to a third party, and it is not subject to the terms of service of a commercial service\n",
+    "2. `Cost`: There is no inference fee, which is important for token-intensive applications (e.g., [long-running simulations](https://twitter.com/RLanceMartin/status/1691097659262820352?s=20), summarization)\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "Running an LLM locally requires a few things:\n",
+    "\n",
+    "1. `Open source LLM`: An open source LLM that can be freely modified and shared \n",
+    "2. `Inference`: Ability to run this LLM on your device w/ acceptable latency\n",
+    "\n",
+    "### Open Source LLMs\n",
+    "\n",
+    "Users can now gain access to a rapidly growing set of [open source LLMs](https://cameronrwolfe.substack.com/p/the-history-of-open-source-llms-better). \n",
+    "\n",
+    "These LLMs can be assessed across at least two dimentions (see figure):\n",
+    " \n",
+    "1. `Base model`: What is the base-model and how was it trained?\n",
+    "2. `Fine-tuning approach`: Was the base-model fine-tuned and, if so, what [set of instructions](https://cameronrwolfe.substack.com/p/beyond-llama-the-power-of-open-llms#%C2%A7alpaca-an-instruction-following-llama-model) was used?\n",
+    "\n",
+    "![Image description](/img/OSS_LLM_overview.png)\n",
+    "\n",
+    "The relative performance of these models can be assessed using several leaderboards, including:\n",
+    "\n",
+    "1. [LmSys](https://chat.lmsys.org/?arena)\n",
+    "2. [GPT4All](https://gpt4all.io/index.html)\n",
+    "3. [HuggingFace](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard)\n",
+    "\n",
+    "### Inference\n",
+    "\n",
+    "A few frameworks for this have emerged to support inference of open source LLMs on various devices:\n",
+    "\n",
+    "1. [`llama.cpp`](https://github.com/ggerganov/llama.cpp): C++ implementation of llama inference code with [weight optimization / quantization](https://finbarr.ca/how-is-llama-cpp-possible/)\n",
+    "2. [`gpt4all`](https://docs.gpt4all.io/index.html): Optimized C backend for inference\n",
+    "3. [`Ollama`](https://ollama.ai/): Bundles model weights and environment into an app that runs on device and serves the LLM \n",
+    "\n",
+    "In general, these frameworks will do a few things:\n",
+    "\n",
+    "1. `Quantization`: Reduce the memory footprint of the raw model weights\n",
+    "2. `Efficient implementation for inference`: Support inference on consumer hardware (e.g., CPU or laptop GPU)\n",
+    "\n",
+    "In particular, see [this excellent post](https://finbarr.ca/how-is-llama-cpp-possible/) on the importance of quantization.\n",
+    "\n",
+    "![Image description](/img/llama-memory-weights.png)\n",
+    "\n",
+    "With less precision, we radically decrease the memory needed to store the LLM in memory.\n",
+    "\n",
+    "In addition, we can see the importance of GPU memory bandwidth [sheet](https://docs.google.com/spreadsheets/d/1OehfHHNSn66BP2h3Bxp2NJTVX97icU0GmCXF6pK23H8/edit#gid=0)!\n",
+    "\n",
+    "A Mac M2 Max is 5-6x faster than a M1 for inference due to the larger GPU memory bandwidth.\n",
+    "\n",
+    "![Image description](/img/llama_t_put.png)\n",
+    "\n",
+    "## Quickstart\n",
+    "\n",
+    "[`Ollama`](https://ollama.ai/) is one way to easily run inference on macOS.\n",
+    " \n",
+    "The instructions [here](docs/integrations/llms/ollama) provide details, which we summarize:\n",
+    " \n",
+    "* [Download and run](https://ollama.ai/download) the app\n",
+    "* From command line, fetch a model from this [list of options](https://github.com/jmorganca/ollama): e.g., `ollama pull llama2`\n",
+    "* When the app is running, all models are automatically served on `localhost:11434`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "86178adb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' The first man on the moon was Neil Armstrong, who landed on the moon on July 20, 1969 as part of the Apollo 11 mission. obviously.'"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.llms import Ollama\n",
+    "llm = Ollama(model=\"llama2\")\n",
+    "llm(\"The first man on the moon was ...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "343ab645",
+   "metadata": {},
+   "source": [
+    "Stream tokens as they are being generated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "9cd83603",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " The first man to walk on the moon was Neil Armstrong, an American astronaut who was part of the Apollo 11 mission in 1969. февруари 20, 1969, Armstrong stepped out of the lunar module Eagle and onto the moon's surface, famously declaring \"That's one small step for man, one giant leap for mankind\" as he took his first steps. He was followed by fellow astronaut Edwin \"Buzz\" Aldrin, who also walked on the moon during the mission."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "' The first man to walk on the moon was Neil Armstrong, an American astronaut who was part of the Apollo 11 mission in 1969. февруари 20, 1969, Armstrong stepped out of the lunar module Eagle and onto the moon\\'s surface, famously declaring \"That\\'s one small step for man, one giant leap for mankind\" as he took his first steps. He was followed by fellow astronaut Edwin \"Buzz\" Aldrin, who also walked on the moon during the mission.'"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler \n",
+    "llm = Ollama(model=\"llama2\", \n",
+    "             callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]))\n",
+    "llm(\"The first man on the moon was ...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cb27414",
+   "metadata": {},
+   "source": [
+    "## Environment\n",
+    "\n",
+    "Inference speed is a chllenge when running models locally (see above).\n",
+    "\n",
+    "To minimize latency, it is desiable to run models locally on GPU, which ships with many consumer laptops [e.g., Apple devices](https://www.apple.com/newsroom/2022/06/apple-unveils-m2-with-breakthrough-performance-and-capabilities/).\n",
+    "\n",
+    "And even with GPU, the available GPU memory bandwidth (as noted above) is important.\n",
+    "\n",
+    "### Running Apple silicon GPU\n",
+    "\n",
+    "`Ollama` will automatically utilize the GPU on Apple devices.\n",
+    " \n",
+    "Other frameworks require the user to set up the environment to utilize the Apple GPU.\n",
+    "\n",
+    "For example, `llama.cpp` python bindings can be configured to use the GPU via [Metal](https://developer.apple.com/metal/).\n",
+    "\n",
+    "Metal is a graphics and compute API created by Apple providing near-direct access to the GPU. \n",
+    "\n",
+    "See the [`llama.cpp`](docs/integrations/llms/llamacpp) setup [here](https://github.com/abetlen/llama-cpp-python/blob/main/docs/install/macos.md) to enable this.\n",
+    "\n",
+    "In particular, ensure that conda is using the correct virtual enviorment that you created (`miniforge3`).\n",
+    "\n",
+    "E.g., for me:\n",
+    "\n",
+    "```\n",
+    "conda activate /Users/rlm/miniforge3/envs/llama\n",
+    "```\n",
+    "\n",
+    "With the above confirmed, then:\n",
+    "\n",
+    "```\n",
+    "CMAKE_ARGS=\"-DLLAMA_METAL=on\" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dir\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c382e79a",
+   "metadata": {},
+   "source": [
+    "## LLMs\n",
+    "\n",
+    "There are various ways to gain access to quantized model weights.\n",
+    "\n",
+    "1. [`HuggingFace`](https://huggingface.co/TheBloke) - Many quantized model are available for download and can be run with framework such as [`llama.cpp`](https://github.com/ggerganov/llama.cpp)\n",
+    "2. [`gpt4all`](https://gpt4all.io/index.html) - The model explorer offers a leaderboard of metrics and associated quantized models available for download \n",
+    "3. [`Ollama`](https://github.com/jmorganca/ollama) - Several models can be accessed directly via `pull`\n",
+    "\n",
+    "### Ollama\n",
+    "\n",
+    "With [Ollama](docs/integrations/llms/ollama), fetch a model via `ollama pull <model family>:<tag>`:\n",
+    "\n",
+    "* E.g., for Llama-7b: `ollama pull llama2` will download the most basic version of the model (e.g., smallest # parameters and 4 bit quantization)\n",
+    "* We can also specify a particular version from the [model list](https://github.com/jmorganca/ollama), e.g., `ollama pull llama2:13b`\n",
+    "* See the full set of parameters on the [API reference page](https://api.python.langchain.com/en/latest/llms/langchain.llms.ollama.Ollama.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "8ecd2f78",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' Sure! Here\\'s the answer, broken down step by step:\\n\\nThe first man on the moon was... Neil Armstrong.\\n\\nHere\\'s how I arrived at that answer:\\n\\n1. The first manned mission to land on the moon was Apollo 11.\\n2. The mission included three astronauts: Neil Armstrong, Edwin \"Buzz\" Aldrin, and Michael Collins.\\n3. Neil Armstrong was the mission commander and the first person to set foot on the moon.\\n4. On July 20, 1969, Armstrong stepped out of the lunar module Eagle and onto the moon\\'s surface, famously declaring \"That\\'s one small step for man, one giant leap for mankind.\"\\n\\nSo, the first man on the moon was Neil Armstrong!'"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.llms import Ollama\n",
+    "llm = Ollama(model=\"llama2:13b\")\n",
+    "llm(\"The first man on the moon was ... think step by step\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07c8c0d1",
+   "metadata": {},
+   "source": [
+    "### Llama.cpp\n",
+    "\n",
+    "Llama.cpp is compatible with a [broad set of models](https://github.com/ggerganov/llama.cpp).\n",
+    "\n",
+    "For example, below we run inference on `llama2-13b` with 4 bit quantization downloaded from [HuggingFace](https://huggingface.co/TheBloke/Llama-2-13B-GGML/tree/main).\n",
+    "\n",
+    "As noted above, see the [API reference](https://api.python.langchain.com/en/latest/llms/langchain.llms.llamacpp.LlamaCpp.html?highlight=llamacpp#langchain.llms.llamacpp.LlamaCpp) for the full set of parameters. \n",
+    "\n",
+    "From the [llama.cpp docs](https://python.langchain.com/docs/integrations/llms/llamacpp), a few are worth commenting on:\n",
+    "\n",
+    "`n_gpu_layers`: number of layers to be loaded into GPU memory\n",
+    "\n",
+    "* Value: 1\n",
+    "* Meaning: Only one layer of the model will be loaded into GPU memory (1 is often sufficient).\n",
+    "\n",
+    "`n_batch`: number of tokens the model should process in parallel \n",
+    "* Value: n_batch\n",
+    "* Meaning: It's recommended to choose a value between 1 and n_ctx (which in this case is set to 2048)\n",
+    "\n",
+    "`n_ctx`: Token context window .\n",
+    "* Value: 2048\n",
+    "* Meaning: The model will consider a window of 2048 tokens at a time\n",
+    "\n",
+    "`f16_kv`: whether the model should use half-precision for the key/value cache\n",
+    "* Value: True\n",
+    "* Meaning: The model will use half-precision, which can be more memory efficient; Metal only support True."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5eba38dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install llama-cpp-python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "9d5f94b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "objc[10142]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x2a0c4c208) and /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/libllama.dylib (0x2c28bc208). One of the two will be used. Which one is undefined.\n",
+      "llama.cpp: loading model from /Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\n",
+      "llama_model_load_internal: format     = ggjt v3 (latest)\n",
+      "llama_model_load_internal: n_vocab    = 32000\n",
+      "llama_model_load_internal: n_ctx      = 2048\n",
+      "llama_model_load_internal: n_embd     = 5120\n",
+      "llama_model_load_internal: n_mult     = 256\n",
+      "llama_model_load_internal: n_head     = 40\n",
+      "llama_model_load_internal: n_layer    = 40\n",
+      "llama_model_load_internal: n_rot      = 128\n",
+      "llama_model_load_internal: freq_base  = 10000.0\n",
+      "llama_model_load_internal: freq_scale = 1\n",
+      "llama_model_load_internal: ftype      = 2 (mostly Q4_0)\n",
+      "llama_model_load_internal: n_ff       = 13824\n",
+      "llama_model_load_internal: model size = 13B\n",
+      "llama_model_load_internal: ggml ctx size =    0.09 MB\n",
+      "llama_model_load_internal: mem required  = 8953.71 MB (+ 1608.00 MB per state)\n",
+      "llama_new_context_with_model: kv self size  = 1600.00 MB\n",
+      "ggml_metal_init: allocating\n",
+      "ggml_metal_init: using MPS\n",
+      "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n",
+      "ggml_metal_init: loaded kernel_add                            0x47774af60\n",
+      "ggml_metal_init: loaded kernel_mul                            0x47774bc00\n",
+      "ggml_metal_init: loaded kernel_mul_row                        0x47774c230\n",
+      "ggml_metal_init: loaded kernel_scale                          0x47774c890\n",
+      "ggml_metal_init: loaded kernel_silu                           0x47774cef0\n",
+      "ggml_metal_init: loaded kernel_relu                           0x10e33e500\n",
+      "ggml_metal_init: loaded kernel_gelu                           0x47774b2f0\n",
+      "ggml_metal_init: loaded kernel_soft_max                       0x47771a580\n",
+      "ggml_metal_init: loaded kernel_diag_mask_inf                  0x47774dab0\n",
+      "ggml_metal_init: loaded kernel_get_rows_f16                   0x47774e110\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_0                  0x47774e7d0\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_1                  0x13efd7170\n",
+      "ggml_metal_init: loaded kernel_get_rows_q2_K                  0x13efd73d0\n",
+      "ggml_metal_init: loaded kernel_get_rows_q3_K                  0x13efd7630\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_K                  0x13efd7890\n",
+      "ggml_metal_init: loaded kernel_get_rows_q5_K                  0x4744c9740\n",
+      "ggml_metal_init: loaded kernel_get_rows_q6_K                  0x4744ca6b0\n",
+      "ggml_metal_init: loaded kernel_rms_norm                       0x4744cb250\n",
+      "ggml_metal_init: loaded kernel_norm                           0x4744cb970\n",
+      "ggml_metal_init: loaded kernel_mul_mat_f16_f32                0x10e33f700\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32               0x10e33fcd0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32               0x4744cc2d0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q2_K_f32               0x4744cc6f0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q3_K_f32               0x4744cd6b0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_K_f32               0x4744cde20\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q5_K_f32               0x10e33ff30\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q6_K_f32               0x10e340190\n",
+      "ggml_metal_init: loaded kernel_rope                           0x10e3403f0\n",
+      "ggml_metal_init: loaded kernel_alibi_f32                      0x10e340de0\n",
+      "ggml_metal_init: loaded kernel_cpy_f32_f16                    0x10e3416d0\n",
+      "ggml_metal_init: loaded kernel_cpy_f32_f32                    0x10e342080\n",
+      "ggml_metal_init: loaded kernel_cpy_f16_f16                    0x10e342ca0\n",
+      "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n",
+      "ggml_metal_init: hasUnifiedMemory             = true\n",
+      "ggml_metal_init: maxTransferRate              = built-in GPU\n",
+      "ggml_metal_add_buffer: allocated 'data            ' buffer, size =  6984.06 MB, ( 6986.19 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'eval            ' buffer, size =  1032.00 MB, ( 8018.19 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'kv              ' buffer, size =  1602.00 MB, ( 9620.19 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'scr0            ' buffer, size =   426.00 MB, (10046.19 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'scr1            ' buffer, size =   512.00 MB, (10558.19 / 21845.34)\n",
+      "AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | \n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.llms import LlamaCpp\n",
+    "llm = LlamaCpp(\n",
+    "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\",\n",
+    "    n_gpu_layers=1,\n",
+    "    n_batch=512,\n",
+    "    n_ctx=2048,\n",
+    "    f16_kv=True,  \n",
+    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f56f5168",
+   "metadata": {},
+   "source": [
+    "The console log will show the the below to indicate Metal was enabled properly from steps above:\n",
+    "```\n",
+    "ggml_metal_init: allocating\n",
+    "ggml_metal_init: using MPS\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "7890a077",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Llama.generate: prefix-match hit\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " and use logical reasoning to figure out who the first man on the moon was.\n",
+      "\n",
+      "Here are some clues:\n",
+      "\n",
+      "1. The first man on the moon was an American.\n",
+      "2. He was part of the Apollo 11 mission.\n",
+      "3. He stepped out of the lunar module and became the first person to set foot on the moon's surface.\n",
+      "4. His last name is Armstrong.\n",
+      "\n",
+      "Now, let's use our reasoning skills to figure out who the first man on the moon was. Based on clue #1, we know that the first man on the moon was an American. Clue #2 tells us that he was part of the Apollo 11 mission. Clue #3 reveals that he was the first person to set foot on the moon's surface. And finally, clue #4 gives us his last name: Armstrong.\n",
+      "Therefore, the first man on the moon was Neil Armstrong!"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "llama_print_timings:        load time =  9623.21 ms\n",
+      "llama_print_timings:      sample time =   143.77 ms /   203 runs   (    0.71 ms per token,  1412.01 tokens per second)\n",
+      "llama_print_timings: prompt eval time =   485.94 ms /     7 tokens (   69.42 ms per token,    14.40 tokens per second)\n",
+      "llama_print_timings:        eval time =  6385.16 ms /   202 runs   (   31.61 ms per token,    31.64 tokens per second)\n",
+      "llama_print_timings:       total time =  7279.28 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "\" and use logical reasoning to figure out who the first man on the moon was.\\n\\nHere are some clues:\\n\\n1. The first man on the moon was an American.\\n2. He was part of the Apollo 11 mission.\\n3. He stepped out of the lunar module and became the first person to set foot on the moon's surface.\\n4. His last name is Armstrong.\\n\\nNow, let's use our reasoning skills to figure out who the first man on the moon was. Based on clue #1, we know that the first man on the moon was an American. Clue #2 tells us that he was part of the Apollo 11 mission. Clue #3 reveals that he was the first person to set foot on the moon's surface. And finally, clue #4 gives us his last name: Armstrong.\\nTherefore, the first man on the moon was Neil Armstrong!\""
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm(\"The first man on the moon was ... Let's think step by step\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "831ddf7c",
+   "metadata": {},
+   "source": [
+    "### GPT4All\n",
+    "\n",
+    "We can use model weights downloaded from [GPT4All](https://python.langchain.com/docs/integrations/llms/gpt4all) model explorer.\n",
+    "\n",
+    "Similar to what is shown above, we can run inference and use [the API reference](https://api.python.langchain.com/en/latest/llms/langchain.llms.gpt4all.GPT4All.html?highlight=gpt4all#langchain.llms.gpt4all.GPT4All) to set parameters of interest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e27baf6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install gpt4all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "b55a2147",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found model file at  /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n",
+      "llama_new_context_with_model: max tensor size =    87.89 MB\n",
+      "llama_new_context_with_model: max tensor size =    87.89 MB\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "llama.cpp: using Metal\n",
+      "llama.cpp: loading model from /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n",
+      "llama_model_load_internal: format     = ggjt v3 (latest)\n",
+      "llama_model_load_internal: n_vocab    = 32001\n",
+      "llama_model_load_internal: n_ctx      = 2048\n",
+      "llama_model_load_internal: n_embd     = 5120\n",
+      "llama_model_load_internal: n_mult     = 256\n",
+      "llama_model_load_internal: n_head     = 40\n",
+      "llama_model_load_internal: n_layer    = 40\n",
+      "llama_model_load_internal: n_rot      = 128\n",
+      "llama_model_load_internal: ftype      = 2 (mostly Q4_0)\n",
+      "llama_model_load_internal: n_ff       = 13824\n",
+      "llama_model_load_internal: n_parts    = 1\n",
+      "llama_model_load_internal: model size = 13B\n",
+      "llama_model_load_internal: ggml ctx size =    0.09 MB\n",
+      "llama_model_load_internal: mem required  = 9031.71 MB (+ 1608.00 MB per state)\n",
+      "llama_new_context_with_model: kv self size  = 1600.00 MB\n",
+      "ggml_metal_init: allocating\n",
+      "ggml_metal_init: using MPS\n",
+      "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/ggml-metal.metal'\n",
+      "ggml_metal_init: loaded kernel_add                            0x37944d850\n",
+      "ggml_metal_init: loaded kernel_mul                            0x37944f350\n",
+      "ggml_metal_init: loaded kernel_mul_row                        0x37944fdd0\n",
+      "ggml_metal_init: loaded kernel_scale                          0x3794505a0\n",
+      "ggml_metal_init: loaded kernel_silu                           0x379450800\n",
+      "ggml_metal_init: loaded kernel_relu                           0x379450a60\n",
+      "ggml_metal_init: loaded kernel_gelu                           0x379450cc0\n",
+      "ggml_metal_init: loaded kernel_soft_max                       0x379450ff0\n",
+      "ggml_metal_init: loaded kernel_diag_mask_inf                  0x379451250\n",
+      "ggml_metal_init: loaded kernel_get_rows_f16                   0x3794514b0\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_0                  0x379451710\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_1                  0x379451970\n",
+      "ggml_metal_init: loaded kernel_get_rows_q2_k                  0x379451bd0\n",
+      "ggml_metal_init: loaded kernel_get_rows_q3_k                  0x379451e30\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_k                  0x379452090\n",
+      "ggml_metal_init: loaded kernel_get_rows_q5_k                  0x3794522f0\n",
+      "ggml_metal_init: loaded kernel_get_rows_q6_k                  0x379452550\n",
+      "ggml_metal_init: loaded kernel_rms_norm                       0x3794527b0\n",
+      "ggml_metal_init: loaded kernel_norm                           0x379452a10\n",
+      "ggml_metal_init: loaded kernel_mul_mat_f16_f32                0x379452c70\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32               0x379452ed0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32               0x379453130\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q2_k_f32               0x379453390\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q3_k_f32               0x3794535f0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_k_f32               0x379453850\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q5_k_f32               0x379453ab0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q6_k_f32               0x379453d10\n",
+      "ggml_metal_init: loaded kernel_rope                           0x379453f70\n",
+      "ggml_metal_init: loaded kernel_alibi_f32                      0x3794541d0\n",
+      "ggml_metal_init: loaded kernel_cpy_f32_f16                    0x379454430\n",
+      "ggml_metal_init: loaded kernel_cpy_f32_f32                    0x379454690\n",
+      "ggml_metal_init: loaded kernel_cpy_f16_f16                    0x3794548f0\n",
+      "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n",
+      "ggml_metal_init: hasUnifiedMemory             = true\n",
+      "ggml_metal_init: maxTransferRate              = built-in GPU\n",
+      "ggml_metal_add_buffer: allocated 'data            ' buffer, size =  6984.06 MB, (17542.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'eval            ' buffer, size =  1024.00 MB, (18566.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'kv              ' buffer, size =  1602.00 MB, (20168.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'scr0            ' buffer, size =   512.00 MB, (20680.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'scr1            ' buffer, size =   512.00 MB, (21192.94 / 21845.34)\n",
+      "ggml_metal_free: deallocating\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.llms import GPT4All\n",
+    "llm = GPT4All(model=\"/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "e3d4526f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\".\\n1) The United States decides to send a manned mission to the moon.2) They choose their best astronauts and train them for this specific mission.3) They build a spacecraft that can take humans to the moon, called the Lunar Module (LM).4) They also create a larger spacecraft, called the Saturn V rocket, which will launch both the LM and the Command Service Module (CSM), which will carry the astronauts into orbit.5) The mission is planned down to the smallest detail: from the trajectory of the rockets to the exact movements of the astronauts during their moon landing.6) On July 16, 1969, the Saturn V rocket launches from Kennedy Space Center in Florida, carrying the Apollo 11 mission crew into space.7) After one and a half orbits around the Earth, the LM separates from the CSM and begins its descent to the moon's surface.8) On July 20, 1969, at 2:56 pm EDT (GMT-4), Neil Armstrong becomes the first man on the moon. He speaks these\""
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "llm(\"The first man on the moon was ... Let's think step by step\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b84e543",
+   "metadata": {},
+   "source": [
+    "## Prompts\n",
+    "\n",
+    "Some LLMs will benefit from specific prompts.\n",
+    "\n",
+    "For example, llama2 can use [special tokens](https://twitter.com/RLanceMartin/status/1681879318493003776?s=20).\n",
+    "\n",
+    "We can use `ConditionalPromptSelector` to set prompt based on the model type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "d082b10a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "llama.cpp: loading model from /Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\n",
+      "llama_model_load_internal: format     = ggjt v3 (latest)\n",
+      "llama_model_load_internal: n_vocab    = 32000\n",
+      "llama_model_load_internal: n_ctx      = 2048\n",
+      "llama_model_load_internal: n_embd     = 5120\n",
+      "llama_model_load_internal: n_mult     = 256\n",
+      "llama_model_load_internal: n_head     = 40\n",
+      "llama_model_load_internal: n_layer    = 40\n",
+      "llama_model_load_internal: n_rot      = 128\n",
+      "llama_model_load_internal: freq_base  = 10000.0\n",
+      "llama_model_load_internal: freq_scale = 1\n",
+      "llama_model_load_internal: ftype      = 2 (mostly Q4_0)\n",
+      "llama_model_load_internal: n_ff       = 13824\n",
+      "llama_model_load_internal: model size = 13B\n",
+      "llama_model_load_internal: ggml ctx size =    0.09 MB\n",
+      "llama_model_load_internal: mem required  = 8953.71 MB (+ 1608.00 MB per state)\n",
+      "llama_new_context_with_model: kv self size  = 1600.00 MB\n",
+      "ggml_metal_init: allocating\n",
+      "ggml_metal_init: using MPS\n",
+      "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n",
+      "ggml_metal_init: loaded kernel_add                            0x4744d09d0\n",
+      "ggml_metal_init: loaded kernel_mul                            0x3781cb3d0\n",
+      "ggml_metal_init: loaded kernel_mul_row                        0x37813bb60\n",
+      "ggml_metal_init: loaded kernel_scale                          0x474481080\n",
+      "ggml_metal_init: loaded kernel_silu                           0x4744d29f0\n",
+      "ggml_metal_init: loaded kernel_relu                           0x3781254c0\n",
+      "ggml_metal_init: loaded kernel_gelu                           0x47447f280\n",
+      "ggml_metal_init: loaded kernel_soft_max                       0x4744cf470\n",
+      "ggml_metal_init: loaded kernel_diag_mask_inf                  0x4744cf6d0\n",
+      "ggml_metal_init: loaded kernel_get_rows_f16                   0x4744cf930\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_0                  0x4744cfb90\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_1                  0x4744cfdf0\n",
+      "ggml_metal_init: loaded kernel_get_rows_q2_K                  0x4744d0050\n",
+      "ggml_metal_init: loaded kernel_get_rows_q3_K                  0x4744ce980\n",
+      "ggml_metal_init: loaded kernel_get_rows_q4_K                  0x4744cebe0\n",
+      "ggml_metal_init: loaded kernel_get_rows_q5_K                  0x4744cee40\n",
+      "ggml_metal_init: loaded kernel_get_rows_q6_K                  0x4744cf0a0\n",
+      "ggml_metal_init: loaded kernel_rms_norm                       0x474482450\n",
+      "ggml_metal_init: loaded kernel_norm                           0x4744826b0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_f16_f32                0x474482910\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32               0x474482b70\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32               0x474482dd0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q2_K_f32               0x474483030\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q3_K_f32               0x474483290\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q4_K_f32               0x4744834f0\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q5_K_f32               0x474483750\n",
+      "ggml_metal_init: loaded kernel_mul_mat_q6_K_f32               0x4744839b0\n",
+      "ggml_metal_init: loaded kernel_rope                           0x474483c10\n",
+      "ggml_metal_init: loaded kernel_alibi_f32                      0x474483e70\n",
+      "ggml_metal_init: loaded kernel_cpy_f32_f16                    0x4744840d0\n",
+      "ggml_metal_init: loaded kernel_cpy_f32_f32                    0x474484330\n",
+      "ggml_metal_init: loaded kernel_cpy_f16_f16                    0x474484590\n",
+      "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n",
+      "ggml_metal_init: hasUnifiedMemory             = true\n",
+      "ggml_metal_init: maxTransferRate              = built-in GPU\n",
+      "ggml_metal_add_buffer: allocated 'data            ' buffer, size =  6984.06 MB, ( 6986.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'eval            ' buffer, size =  1032.00 MB, ( 8018.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'kv              ' buffer, size =  1602.00 MB, ( 9620.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'scr0            ' buffer, size =   426.00 MB, (10046.94 / 21845.34)\n",
+      "ggml_metal_add_buffer: allocated 'scr1            ' buffer, size =   512.00 MB, (10558.94 / 21845.34)\n",
+      "AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | \n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set our LLM\n",
+    "llm = LlamaCpp(\n",
+    "    model_path=\"/Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\",\n",
+    "    n_gpu_layers=1,\n",
+    "    n_batch=512,\n",
+    "    n_ctx=2048,\n",
+    "    f16_kv=True,  \n",
+    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66656084",
+   "metadata": {},
+   "source": [
+    "Set the associated prompt."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "8555f5bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PromptTemplate(input_variables=['question'], output_parser=None, partial_variables={}, template='<<SYS>> \\n You are an assistant tasked with improving Google search results. \\n <</SYS>> \\n\\n [INST] Generate THREE Google search queries that are similar to this question. The output should be a numbered list of questions and each should have a question mark at the end: \\n\\n {question} [/INST]', template_format='f-string', validate_template=True)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain import PromptTemplate, LLMChain\n",
+    "from langchain.chains.prompt_selector import ConditionalPromptSelector\n",
+    "\n",
+    "DEFAULT_LLAMA_SEARCH_PROMPT = PromptTemplate(\n",
+    "    input_variables=[\"question\"],\n",
+    "    template=\"\"\"<<SYS>> \\n You are an assistant tasked with improving Google search \\\n",
+    "results. \\n <</SYS>> \\n\\n [INST] Generate THREE Google search queries that \\\n",
+    "are similar to this question. The output should be a numbered list of questions \\\n",
+    "and each should have a question mark at the end: \\n\\n {question} [/INST]\"\"\",\n",
+    ")\n",
+    "\n",
+    "DEFAULT_SEARCH_PROMPT = PromptTemplate(\n",
+    "    input_variables=[\"question\"],\n",
+    "    template=\"\"\"You are an assistant tasked with improving Google search \\\n",
+    "results. Generate THREE Google search queries that are similar to \\\n",
+    "this question. The output should be a numbered list of questions and each \\\n",
+    "should have a question mark at the end: {question}\"\"\",\n",
+    ")\n",
+    "\n",
+    "QUESTION_PROMPT_SELECTOR = ConditionalPromptSelector(\n",
+    "                default_prompt=DEFAULT_SEARCH_PROMPT,\n",
+    "                conditionals=[\n",
+    "                    (lambda llm: isinstance(llm, LlamaCpp), DEFAULT_LLAMA_SEARCH_PROMPT)\n",
+    "                ],\n",
+    "            )\n",
+    "\n",
+    "prompt = QUESTION_PROMPT_SELECTOR.get_prompt(llm)\n",
+    "prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "d0aedfd2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Sure! Here are three similar search queries with a question mark at the end:\n",
+      "\n",
+      "1. Which NBA team did LeBron James lead to a championship in the year he was drafted?\n",
+      "2. Who won the Grammy Awards for Best New Artist and Best Female Pop Vocal Performance in the same year that Lady Gaga was born?\n",
+      "3. What MLB team did Babe Ruth play for when he hit 60 home runs in a single season?"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "llama_print_timings:        load time = 14943.19 ms\n",
+      "llama_print_timings:      sample time =    72.93 ms /   101 runs   (    0.72 ms per token,  1384.87 tokens per second)\n",
+      "llama_print_timings: prompt eval time = 14942.95 ms /    93 tokens (  160.68 ms per token,     6.22 tokens per second)\n",
+      "llama_print_timings:        eval time =  3430.85 ms /   100 runs   (   34.31 ms per token,    29.15 tokens per second)\n",
+      "llama_print_timings:       total time = 18578.26 ms\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'  Sure! Here are three similar search queries with a question mark at the end:\\n\\n1. Which NBA team did LeBron James lead to a championship in the year he was drafted?\\n2. Who won the Grammy Awards for Best New Artist and Best Female Pop Vocal Performance in the same year that Lady Gaga was born?\\n3. What MLB team did Babe Ruth play for when he hit 60 home runs in a single season?'"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Chain\n",
+    "llm_chain = LLMChain(prompt=prompt,llm=llm)\n",
+    "question = \"What NFL team won the Super Bowl in the year that Justin Bieber was born?\"\n",
+    "llm_chain.run({\"question\":question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ba66260",
+   "metadata": {},
+   "source": [
+    "## Use cases\n",
+    "\n",
+    "Given an `llm` created from one of the models above, you can use it for [many use cases](docs/use_cases).\n",
+    "\n",
+    "For example, here is a guide to [RAG](docs/use_cases/question_answering/how_to/local_retrieval_qa) with local LLMs.\n",
+    "\n",
+    "In general, use cases for local model can be driven by at least two factors:\n",
+    "\n",
+    "* `Privacy`: private data (e.g., journals, etc) that a user does not want to share \n",
+    "* `Cost`: text preprocessing (extraction/tagging), summarization, and agent simulations are token-use-intensive tasks\n",
+    "\n",
+    "There are a few approach to support specific use-cases: \n",
+    "\n",
+    "* Fine-tuning (e.g., [gpt-llm-trainer](https://github.com/mshumer/gpt-llm-trainer), [Anyscale](https://www.anyscale.com/blog/fine-tuning-llama-2-a-comprehensive-case-study-for-tailoring-models-to-unique-applications)) \n",
+    "* [Function-calling](https://github.com/MeetKai/functionary/tree/main) for use-cases like extraction or tagging\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/guides/privacy/_category_.yml

@@ -0,0 +1 @@
+label: 'Privacy'

docs/extras/guides/privacy/presidio_data_anonymization.ipynb

@@ -0,0 +1,451 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data anonymization with Microsoft Presidio\n",
+    "\n",
+    "[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/extras/guides/privacy/presidio_data_anonymization.ipynb)\n",
+    "\n",
+    "## Use case\n",
+    "\n",
+    "Data anonymization is crucial before passing information to a language model like GPT-4 because it helps protect privacy and maintain confidentiality. If data is not anonymized, sensitive information such as names, addresses, contact numbers, or other identifiers linked to specific individuals could potentially be learned and misused. Hence, by obscuring or removing this personally identifiable information (PII), data can be used freely without compromising individuals' privacy rights or breaching data protection laws and regulations.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "Anonynization consists of two steps:\n",
+    "\n",
+    "1. **Identification:** Identify all data fields that contain personally identifiable information (PII).\n",
+    "2. **Replacement**: Replace all PIIs with pseudo values or codes that do not reveal any personal information about the individual but can be used for reference. We're not using regular encryption, because the language model won't be able to understand the meaning or context of the encrypted data.\n",
+    "\n",
+    "We use *Microsoft Presidio* together with *Faker* framework for anonymization purposes because of the wide range of functionalities they provide. The full implementation is available in `PresidioAnonymizer`.\n",
+    "\n",
+    "## Quickstart\n",
+    "\n",
+    "Below you will find the use case on how to leverage anonymization in LangChain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Install necessary packages\n",
+    "# ! pip install langchain langchain-experimental openai presidio-analyzer presidio-anonymizer spacy Faker\n",
+    "# ! python -m spacy download en_core_web_lg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\\n",
+    "Let's see how PII anonymization works using a sample sentence:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'My name is Mrs. Rachel Chen DDS, call me at 849-829-7628x073 or email me at christopherfrey@example.org'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain_experimental.data_anonymizer import PresidioAnonymizer\n",
+    "\n",
+    "anonymizer = PresidioAnonymizer()\n",
+    "\n",
+    "anonymizer.anonymize(\n",
+    "    \"My name is Slim Shady, call me at 313-666-7440 or email me at real.slim.shady@gmail.com\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using with LangChain Expression Language\n",
+    "\n",
+    "With LCEL we can easily chain together anonymization with the rest of our application."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set env var OPENAI_API_KEY or load from a .env file:\n",
+    "# import dotenv\n",
+    "\n",
+    "# dotenv.load_dotenv()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='You can find our super secret data at https://www.ross.com/', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.prompts.prompt import PromptTemplate\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "\n",
+    "template = \"\"\"According to this text, where can you find our super secret data?\n",
+    "\n",
+    "{anonymized_text}\n",
+    "\n",
+    "Answer:\"\"\"\n",
+    "prompt = PromptTemplate.from_template(template)\n",
+    "llm = ChatOpenAI()\n",
+    "\n",
+    "chain = {\"anonymized_text\": anonymizer.anonymize} | prompt | llm\n",
+    "chain.invoke(\"You can find our super secret data at https://supersecretdata.com\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Customization\n",
+    "We can specify ``analyzed_fields`` to only anonymize particular types of data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'My name is Gabrielle Edwards, call me at 313-666-7440 or email me at real.slim.shady@gmail.com'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anonymizer = PresidioAnonymizer(analyzed_fields=[\"PERSON\"])\n",
+    "\n",
+    "anonymizer.anonymize(\n",
+    "    \"My name is Slim Shady, call me at 313-666-7440 or email me at real.slim.shady@gmail.com\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As can be observed, the name was correctly identified and replaced with another. The `analyzed_fields` attribute is responsible for what values are to be detected and substituted. We can add *PHONE_NUMBER* to the list:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'My name is Victoria Mckinney, call me at 713-549-8623 or email me at real.slim.shady@gmail.com'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anonymizer = PresidioAnonymizer(analyzed_fields=[\"PERSON\", \"PHONE_NUMBER\"])\n",
+    "anonymizer.anonymize(\n",
+    "    \"My name is Slim Shady, call me at 313-666-7440 or email me at real.slim.shady@gmail.com\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\\n",
+    "If no analyzed_fields are specified, by default the anonymizer will detect all supported formats. Below is the full list of them:\n",
+    "\n",
+    "`['PERSON', 'EMAIL_ADDRESS', 'PHONE_NUMBER', 'IBAN_CODE', 'CREDIT_CARD', 'CRYPTO', 'IP_ADDRESS', 'LOCATION', 'DATE_TIME', 'NRP', 'MEDICAL_LICENSE', 'URL', 'US_BANK_NUMBER', 'US_DRIVER_LICENSE', 'US_ITIN', 'US_PASSPORT', 'US_SSN']`\n",
+    "\n",
+    "**Disclaimer:** We suggest carefully defining the private data to be detected - Presidio doesn't work perfectly and it sometimes makes mistakes, so it's better to have more control over the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'My name is Billy Russo, call me at 970-996-9453x038 or email me at jamie80@example.org'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anonymizer = PresidioAnonymizer()\n",
+    "anonymizer.anonymize(\n",
+    "    \"My name is Slim Shady, call me at 313-666-7440 or email me at real.slim.shady@gmail.com\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\\n",
+    "It may be that the above list of detected fields is not sufficient. For example, the already available *PHONE_NUMBER* field does not support polish phone numbers and confuses it with another field:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'My polish phone number is EVIA70648911396944'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anonymizer = PresidioAnonymizer()\n",
+    "anonymizer.anonymize(\"My polish phone number is 666555444\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\\n",
+    "You can then write your own recognizers and add them to the pool of those present. How exactly to create recognizers is described in the [Presidio documentation](https://microsoft.github.io/presidio/samples/python/customizing_presidio_analyzer/)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the regex pattern in a Presidio `Pattern` object:\n",
+    "from presidio_analyzer import Pattern, PatternRecognizer\n",
+    "\n",
+    "\n",
+    "polish_phone_numbers_pattern = Pattern(\n",
+    "    name=\"polish_phone_numbers_pattern\",\n",
+    "    regex=\"(?<!\\w)(\\(?(\\+|00)?48\\)?)?[ -]?\\d{3}[ -]?\\d{3}[ -]?\\d{3}(?!\\w)\",\n",
+    "    score=1,\n",
+    ")\n",
+    "\n",
+    "# Define the recognizer with one or more patterns\n",
+    "polish_phone_numbers_recognizer = PatternRecognizer(\n",
+    "    supported_entity=\"POLISH_PHONE_NUMBER\", patterns=[polish_phone_numbers_pattern]\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\\n",
+    "Now, we can add recognizer by calling `add_recognizer` method on the anonymizer:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anonymizer.add_recognizer(polish_phone_numbers_recognizer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\\n",
+    "And voilà! With the added pattern-based recognizer, the anonymizer now handles polish phone numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "My polish phone number is <POLISH_PHONE_NUMBER>\n",
+      "My polish phone number is <POLISH_PHONE_NUMBER>\n",
+      "My polish phone number is <POLISH_PHONE_NUMBER>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(anonymizer.anonymize(\"My polish phone number is 666555444\"))\n",
+    "print(anonymizer.anonymize(\"My polish phone number is 666 555 444\"))\n",
+    "print(anonymizer.anonymize(\"My polish phone number is +48 666 555 444\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\\n",
+    "The problem is - even though we recognize polish phone numbers now, we don't have a method (operator) that would tell how to substitute a given field - because of this, in the outpit we only provide string `<POLISH_PHONE_NUMBER>` We need to create a method to replace it correctly: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'+48 533 220 543'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from faker import Faker\n",
+    "\n",
+    "fake = Faker(locale=\"pl_PL\")\n",
+    "\n",
+    "\n",
+    "def fake_polish_phone_number(_=None):\n",
+    "    return fake.phone_number()\n",
+    "\n",
+    "\n",
+    "fake_polish_phone_number()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\\\n",
+    "We used Faker to create pseudo data. Now we can create an operator and add it to the anonymizer. For complete information about operators and their creation, see the Presidio documentation for [simple](https://microsoft.github.io/presidio/tutorial/10_simple_anonymization/) and [custom](https://microsoft.github.io/presidio/tutorial/11_custom_anonymization/) anonymization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from presidio_anonymizer.entities import OperatorConfig\n",
+    "\n",
+    "new_operators = {\n",
+    "    \"POLISH_PHONE_NUMBER\": OperatorConfig(\n",
+    "        \"custom\", {\"lambda\": fake_polish_phone_number}\n",
+    "    )\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "anonymizer.add_operators(new_operators)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'My polish phone number is +48 692 715 636'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anonymizer.anonymize(\"My polish phone number is 666555444\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Future works\n",
+    "\n",
+    "- **deanonymization** - add the ability to reverse anonymization. For example, the workflow could look like this: `anonymize -> LLMChain -> deanonymize`. By doing this, we will retain anonymity in requests to, for example, OpenAI, and then be able restore the original data.\n",
+    "- **instance anonymization** - at this point, each occurrence of PII is treated as a separate entity and separately anonymized. Therefore, two occurrences of the name John Doe in the text will be changed to two different names. It is therefore worth introducing support for full instance detection, so that repeated occurrences are treated as a single object."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

docs/extras/guides/pydantic_compatibility.md

@@ -0,0 +1,105 @@
+# Pydantic compatibility
+
+- Pydantic v2 was released in June, 2023 (https://docs.pydantic.dev/2.0/blog/pydantic-v2-final/)
+- v2 contains has a number of breaking changes (https://docs.pydantic.dev/2.0/migration/)
+- Pydantic v2 and v1 are under the same package name, so both versions cannot be installed at the same time
+
+## LangChain Pydantic migration plan
+
+As of `langchain>=0.0.267`, LangChain will allow users to install either Pydantic V1 or V2. 
+   * Internally LangChain will continue to [use V1](https://docs.pydantic.dev/latest/migration/#continue-using-pydantic-v1-features).
+   * During this time, users can pin their pydantic version to v1 to avoid breaking changes, or start a partial
+   migration using pydantic v2 throughout their code, but avoiding mixing v1 and v2 code for LangChain (see below).
+
+User can either pin to pydantic v1, and upgrade their code in one go once LangChain has migrated to v2 internally, or they can start a partial migration to v2, but must avoid mixing v1 and v2 code for LangChain.
+
+Below are two examples of showing how to avoid mixing pydantic v1 and v2 code in
+the case of inheritance and in the case of passing objects to LangChain.
+
+**Example 1: Extending via inheritance**
+
+**YES** 
+
+```python
+from pydantic.v1 import root_validator, validator
+
+class CustomTool(BaseTool): # BaseTool is v1 code
+    x: int = Field(default=1)
+
+    def _run(*args, **kwargs):
+        return "hello"
+
+    @validator('x') # v1 code
+    @classmethod
+    def validate_x(cls, x: int) -> int:
+        return 1
+    
+
+CustomTool(
+    name='custom_tool',
+    description="hello",
+    x=1,
+)
+```
+
+Mixing Pydantic v2 primitives with Pydantic v1 primitives can raise cryptic errors
+
+**NO** 
+
+```python
+from pydantic import Field, field_validator # pydantic v2
+
+class CustomTool(BaseTool): # BaseTool is v1 code
+    x: int = Field(default=1)
+
+    def _run(*args, **kwargs):
+        return "hello"
+
+    @field_validator('x') # v2 code
+    @classmethod
+    def validate_x(cls, x: int) -> int:
+        return 1
+    
+
+CustomTool( 
+    name='custom_tool',
+    description="hello",
+    x=1,
+)
+```
+
+**Example 2: Passing objects to LangChain**
+
+**YES**
+
+```python
+from langchain.tools.base import Tool
+from pydantic.v1 import BaseModel, Field # <-- Uses v1 namespace
+
+class CalculatorInput(BaseModel):
+    question: str = Field()
+
+Tool.from_function( # <-- tool uses v1 namespace
+    func=lambda question: 'hello',
+    name="Calculator",
+    description="useful for when you need to answer questions about math",
+    args_schema=CalculatorInput
+)
+```
+
+**NO**
+
+```python
+from langchain.tools.base import Tool
+from pydantic import BaseModel, Field # <-- Uses v2 namespace
+
+class CalculatorInput(BaseModel):
+    question: str = Field()
+
+Tool.from_function( # <-- tool uses v1 namespace
+    func=lambda question: 'hello',
+    name="Calculator",
+    description="useful for when you need to answer questions about math",
+    args_schema=CalculatorInput
+)
+```

docs/extras/integrations/callbacks/argilla.ipynb

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

docs/extras/integrations/callbacks/context.ipynb

@@ -7,12 +7,12 @@
    "source": [
     "# Context\n",
     "\n",
-    "![Context - Product Analytics for AI Chatbots](https://go.getcontext.ai/langchain.png)\n",
+    "![Context - User Analytics for LLM Powered Products](https://with.context.ai/langchain.png)\n",
     "\n",
-    "[Context](https://getcontext.ai/) provides product analytics for AI chatbots.\n",
+    "[Context](https://context.ai/) provides user analytics for LLM powered products and features.\n",
     "\n",
-    "Context helps you understand how users are interacting with your AI chat products.\n",
-    "Gain critical insights, optimise poor experiences, and minimise brand risks.\n"
+    "With Context, you can start understanding your users and improving their experiences in less than 30 minutes.\n",
+    "\n"
    ]
   },
   {
@@ -55,7 +55,7 @@
     "\n",
     "To get your Context API token:\n",
     "\n",
-    "1. Go to the settings page within your Context account (https://go.getcontext.ai/settings).\n",
+    "1. Go to the settings page within your Context account (https://with.context.ai/settings).\n",
     "2. Generate a new API Token.\n",
     "3. Store this token somewhere secure."
    ]
@@ -207,7 +207,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.9.1"
   },
   "vscode": {
    "interpreter": {

docs/extras/integrations/callbacks/infino.ipynb

@@ -1,86 +1,73 @@
 {
  "cells": [
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "8d10861f-a550-4443-bc63-4ce2ae13b841",
    "metadata": {},
    "source": [
-    "# Infino - LangChain LLM Monitoring Example\n",
+    "# Infino\n",
     "\n",
-    "This example shows how one can track the following while calling OpenAI models via LangChain and [Infino](https://github.com/infinohq/infino):\n",
+    "This example shows how one can track the following while calling OpenAI models via `LangChain` and [Infino](https://github.com/infinohq/infino):\n",
     "\n",
     "* prompt input,\n",
-    "* response from chatgpt or any other LangChain model,\n",
+    "* response from `ChatGPT` or any other `LangChain` model,\n",
     "* latency,\n",
     "* errors,\n",
     "* number of tokens consumed"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "3a5a0976-9953-41d8-880c-eb3f2992e936",
+   "cell_type": "markdown",
+   "id": "64d14c88-b71c-4524-ab1b-4250a7dbb62b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: matplotlib in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (3.7.1)\n",
-      "Requirement already satisfied: contourpy>=1.0.1 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (1.0.7)\n",
-      "Requirement already satisfied: cycler>=0.10 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (0.11.0)\n",
-      "Requirement already satisfied: fonttools>=4.22.0 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (4.39.4)\n",
-      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (1.4.4)\n",
-      "Requirement already satisfied: numpy>=1.20 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (1.24.3)\n",
-      "Requirement already satisfied: packaging>=20.0 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (23.1)\n",
-      "Requirement already satisfied: pillow>=6.2.0 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (9.5.0)\n",
-      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (3.0.9)\n",
-      "Requirement already satisfied: python-dateutil>=2.7 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from matplotlib) (2.8.2)\n",
-      "Requirement already satisfied: six>=1.5 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
-      "Requirement already satisfied: infinopy in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (0.0.1)\n",
-      "Requirement already satisfied: docker in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from infinopy) (6.1.3)\n",
-      "Requirement already satisfied: requests in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from infinopy) (2.31.0)\n",
-      "Requirement already satisfied: packaging>=14.0 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from docker->infinopy) (23.1)\n",
-      "Requirement already satisfied: urllib3>=1.26.0 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from docker->infinopy) (2.0.2)\n",
-      "Requirement already satisfied: websocket-client>=0.32.0 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from docker->infinopy) (1.5.2)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from requests->infinopy) (3.1.0)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from requests->infinopy) (3.4)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in /Users/vinaykakade/.pyenv/versions/3.10.11/lib/python3.10/site-packages (from requests->infinopy) (2023.5.7)\n"
-     ]
-    }
-   ],
+   "source": [
+    "## Initializing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed46c894-caa6-49b2-85d1-f275374fa308",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Install necessary dependencies.\n",
     "!pip install infinopy\n",
-    "!pip install matplotlib\n",
-    "\n",
+    "!pip install matplotlib"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a5a0976-9953-41d8-880c-eb3f2992e936",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# Remove the (1) import sys and sys.path.append(..) and (2) uncomment `!pip install langchain` after merging the PR for Infino/LangChain integration.\n",
     "import sys\n",
     "\n",
     "sys.path.append(\"../../../../../langchain\")\n",
-    "#!pip install langchain\n",
     "\n",
     "\n",
     "import datetime as dt\n",
-    "from infinopy import InfinoClient\n",
     "import json\n",
     "from langchain.llms import OpenAI\n",
-    "from langchain.callbacks import InfinoCallbackHandler\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib.dates as md\n",
     "import os\n",
     "import time\n",
-    "import sys"
+    "import sys\n",
+    "\n",
+    "from infinopy import InfinoClient\n",
+    "from langchain.callbacks import InfinoCallbackHandler"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "9f90210d-c805-4a0c-81e4-d5298942afc4",
    "metadata": {},
    "source": [
-    "## Start Infino server, initialize the Infino client\n"
+    "## Start Infino server, initialize the Infino client"
    ]
   },
   {
@@ -106,7 +93,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "b6b81cda-b841-43ee-8c5e-b1576555765f",
    "metadata": {},
@@ -148,7 +134,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "dce1b820-3f1a-4b94-b848-4c6032cadc18",
    "metadata": {},
@@ -214,7 +199,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "b68ec697-c922-4fd9-aad1-f49c6ac24e8a",
    "metadata": {},
@@ -326,7 +310,6 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "c3d61822-1781-4bc6-97a2-2abc5c2b2e75",
    "metadata": {},
@@ -364,12 +347,11 @@
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "id": "4b171074-c775-48e0-a4b3-f550e2c8eccb",
    "metadata": {},
    "source": [
-    "## Step 5: Stop infino server"
+    "## Stop infino server"
    ]
   },
   {
@@ -415,7 +397,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.4"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/extras/integrations/callbacks/labelstudio.ipynb

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

docs/extras/integrations/callbacks/llmonitor.md

@@ -0,0 +1,63 @@
+# LLMonitor
+
+[LLMonitor](https://llmonitor.com) is an open-source observability platform that provides cost tracking, user tracking and powerful agent tracing.
+
+<video controls width='100%' >
+  <source src='https://llmonitor.com/videos/demo-annotated.mp4'/>
+</video>
+
+## Setup
+Create an account on [llmonitor.com](https://llmonitor.com), create an `App`, and then copy the associated `tracking id`.
+Once you have it, set it as an environment variable by running:
+```bash
+export LLMONITOR_APP_ID="..."
+```
+
+If you'd prefer not to set an environment variable, you can pass the key directly when initializing the callback handler:
+```python
+from langchain.callbacks import LLMonitorCallbackHandler
+
+handler = LLMonitorCallbackHandler(app_id="...")
+```
+
+## Usage with LLM/Chat models
+```python
+from langchain.llms import OpenAI
+from langchain.chat_models import ChatOpenAI
+from langchain.callbacks import LLMonitorCallbackHandler
+
+handler = LLMonitorCallbackHandler(app_id="...")
+
+llm = OpenAI(
+    callbacks=[handler],
+)
+
+chat = ChatOpenAI(
+    callbacks=[handler],
+    metadata={"userId": "123"},  # you can assign user ids to models in the metadata
+)
+```
+
+
+## Usage with agents
+```python
+from langchain.agents import load_tools, initialize_agent, AgentType
+from langchain.llms import OpenAI
+from langchain.callbacks import LLMonitorCallbackHandler
+
+handler = LLMonitorCallbackHandler(app_id="...")
+
+llm = OpenAI(temperature=0)
+tools = load_tools(["serpapi", "llm-math"], llm=llm)
+agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION)
+agent.run(
+    "Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?",
+    callbacks=[handler],
+    metadata={
+        "agentName": "Leo DiCaprio's girlfriend",  # you can assign a custom agent in the metadata
+    },
+)
+```
+
+## Support
+For any question or issue with integration you can reach out to the LLMonitor team on [Discord](http://discord.com/invite/8PafSG58kK) or via [email](mailto:vince@llmonitor.com).

docs/extras/integrations/callbacks/promptlayer.ipynb

@@ -11,7 +11,7 @@
     "\n",
     "[PromptLayer](https://promptlayer.com) is a an LLM observability platform that lets you visualize requests, version prompts, and track usage. In this guide we will go over how to setup the `PromptLayerCallbackHandler`. \n",
     "\n",
-    "While PromptLayer does have LLMs that integrate directly with LangChain (eg [`PromptLayerOpenAI`](https://python.langchain.com/docs/integrations/llms/promptlayer_openai)), this callback is the recommended way to integrate PromptLayer with LangChain.\n",
+    "While PromptLayer does have LLMs that integrate directly with LangChain (e.g. [`PromptLayerOpenAI`](https://python.langchain.com/docs/integrations/llms/promptlayer_openai)), this callback is the recommended way to integrate PromptLayer with LangChain.\n",
     "\n",
     "See [our docs](https://docs.promptlayer.com/languages/langchain) for more information."
    ]

docs/extras/integrations/chat/azure_chat_openai.ipynb

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

docs/extras/integrations/chat/ernie.ipynb

@@ -0,0 +1,88 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ERNIE-Bot Chat\n",
+    "\n",
+    "[ERNIE-Bot](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/jlil56u11) is a large language model developed by Baidu, covering a huge amount of Chinese data.\n",
+    "This notebook covers how to get started with ErnieBot chat models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ErnieBotChat\n",
+    "from langchain.schema import HumanMessage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chat = ErnieBotChat(ernie_client_id='YOUR_CLIENT_ID', ernie_client_secret='YOUR_CLIENT_SECRET')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "or you can set `client_id` and `client_secret` in your environment variables\n",
+    "```bash\n",
+    "export ERNIE_CLIENT_ID=YOUR_CLIENT_ID\n",
+    "export ERNIE_CLIENT_SECRET=YOUR_CLIENT_SECRET\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='Hello, I am an artificial intelligence language model. My purpose is to help users answer questions or provide information. What can I do for you?', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat([\n",
+    "    HumanMessage(content='hello there, who are you?')\n",
+    "])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/extras/integrations/chat/litellm.ipynb

@@ -0,0 +1,185 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "bf733a38-db84-4363-89e2-de6735c37230",
+   "metadata": {},
+   "source": [
+    "# 🚅 LiteLLM\n",
+    "\n",
+    "[LiteLLM](https://github.com/BerriAI/litellm) is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc. \n",
+    "\n",
+    "This notebook covers how to get started with using Langchain + the LiteLLM I/O library. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatLiteLLM\n",
+    "from langchain.prompts.chat import (\n",
+    "    ChatPromptTemplate,\n",
+    "    SystemMessagePromptTemplate,\n",
+    "    AIMessagePromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
+    ")\n",
+    "from langchain.schema import AIMessage, HumanMessage, SystemMessage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "chat = ChatLiteLLM(model=\"gpt-3.5-turbo\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    HumanMessage(\n",
+    "        content=\"Translate this sentence from English to French. I love programming.\"\n",
+    "    )\n",
+    "]\n",
+    "chat(messages)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "c361ab1e-8c0c-4206-9e3c-9d1424a12b9c",
+   "metadata": {},
+   "source": [
+    "## `ChatLiteLLM` also supports async and streaming functionality:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "93a21c5c-6ef9-4688-be60-b2e1f94842fb",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c5fac0e9-05a4-4fc1-a3b3-e5bbb24b971b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LLMResult(generations=[[ChatGeneration(text=\" J'aime programmer.\", generation_info=None, message=AIMessage(content=\" J'aime programmer.\", additional_kwargs={}, example=False))]], llm_output={}, run=[RunInfo(run_id=UUID('8cc8fb68-1c35-439c-96a0-695036a93652'))])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await chat.agenerate([messages])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "025be980-e50d-4a68-93dc-c9c7b500ce34",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " J'aime la programmation."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat = ChatLiteLLM(\n",
+    "    streaming=True,\n",
+    "    verbose=True,\n",
+    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
+    ")\n",
+    "chat(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c253883f",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/chat/ollama.ipynb

@@ -0,0 +1,382 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Ollama\n",
+    "\n",
+    "[Ollama](https://ollama.ai/) allows you to run open-source large language models, such as LLaMA2, locally.\n",
+    "\n",
+    "Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. \n",
+    "\n",
+    "It optimizes setup and configuration details, including GPU usage.\n",
+    "\n",
+    "For a complete list of supported models and model variants, see the [Ollama model library](https://ollama.ai/library).\n",
+    "\n",
+    "## Setup\n",
+    "\n",
+    "First, follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:\n",
+    "\n",
+    "* [Download](https://ollama.ai/download)\n",
+    "* Fetch a model via `ollama pull <model family>`\n",
+    "* e.g., for `Llama-7b`: `ollama pull llama2`\n",
+    "* This will download the most basic version of the model (e.g., minimum # parameters and 4-bit quantization)\n",
+    "* On Mac, it will download to:\n",
+    "\n",
+    "`~/.ollama/models/manifests/registry.ollama.ai/library/<model family>/latest`\n",
+    "\n",
+    "* And we can specify a particular version, e.g., for `ollama pull vicuna:13b-v1.5-16k-q4_0`\n",
+    "* The file is here with the model version in place of `latest`\n",
+    "\n",
+    "`~/.ollama/models/manifests/registry.ollama.ai/library/vicuna/13b-v1.5-16k-q4_0`\n",
+    "\n",
+    "You can easily access models in a few ways:\n",
+    "\n",
+    "1/ if the app is running:\n",
+    "* All of your local models are automatically served on `localhost:11434`\n",
+    "* Select your model when setting `llm = Ollama(..., model=\"<model family>:<version>\")`\n",
+    "* If you set `llm = Ollama(..., model=\"<model family\")` withoout a version it will simply look for `latest`\n",
+    "\n",
+    "2/ if building from source or just running the binary: \n",
+    "* Then you must run `ollama serve`\n",
+    "* All of your local models are automatically served on `localhost:11434`\n",
+    "* Then, select as shown above\n",
+    "\n",
+    "\n",
+    "## Usage\n",
+    "\n",
+    "You can see a full list of supported parameters on the [API reference page](https://api.python.langchain.com/en/latest/llms/langchain.llms.ollama.Ollama.html).\n",
+    "\n",
+    "If you are using a LLaMA `chat` model (e.g., `ollama pull llama2:7b-chat`) then you can use the `ChatOllama` interface.\n",
+    "\n",
+    "This includes [special tokens](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) for system message and user input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOllama\n",
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler                                  \n",
+    "chat_model = ChatOllama(model=\"llama2:7b-chat\", \n",
+    "                        callback_manager = CallbackManager([StreamingStdOutCallbackHandler()]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With `StreamingStdOutCallbackHandler`, you will see tokens streamed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Artificial intelligence (AI) has a rich and varied history that spans several decades. Hinweis: The following is a brief overview of the major milestones in the history of AI, but it is by no means exhaustive.\n",
+      "\n",
+      "1. Early Beginnings (1950s-1960s): The term \"Artificial Intelligence\" was coined in 1956 by computer scientist John McCarthy. However, the concept of creating machines that can think and learn like humans dates back to ancient times. In the 1950s and 1960s, researchers began exploring the possibilities of AI using simple algorithms and machine learning techniques.\n",
+      "2. Rule-Based Systems (1970s-1980s): In the 1970s and 1980s, AI research focused on developing rule-based systems, which use predefined rules to reason and make decisions. This led to the development of expert systems, which were designed to mimic the decision-making abilities of human experts in specific domains.\n",
+      "3. Machine Learning (1980s-1990s): The 1980s saw a shift towards machine learning, which enables machines to learn from data without being explicitly programmed. This led to the development of algorithms such as decision trees, neural networks, and support vector machines.\n",
+      "4. Deep Learning (2000s-present): In the early 2000s, deep learning emerged as a subfield of machine learning, focusing on neural networks with multiple layers. These networks can learn complex representations of data, leading to breakthroughs in image and speech recognition, natural language processing, and other areas.\n",
+      "5. Natural Language Processing (NLP) (1980s-present): NLP has been an active area of research since the 1980s, with a focus on developing algorithms that can understand and generate human language. This has led to applications such as chatbots, voice assistants, and language translation systems.\n",
+      "6. Robotics (1970s-present): The development of robotics has been closely tied to AI research, with a focus on creating machines that can perform tasks that typically require human intelligence, such as manipulation and locomotion.\n",
+      "7. Computer Vision (1980s-present): Computer vision has been an active area of research since the 1980s, with a focus on enabling machines to interpret and understand visual data from the world around us. This has led to applications such as image recognition, object detection, and autonomous driving.\n",
+      "8. Ethics and Society (1990s-present): As AI technology has become more advanced and integrated into various aspects of society, there has been a growing concern about the ethical implications of AI. This includes issues related to privacy, bias, and job displacement.\n",
+      "9. Reinforcement Learning (2000s-present): Reinforcement learning is a subfield of machine learning that involves training machines to make decisions based on feedback from their environment. This has led to breakthroughs in areas such as game playing, robotics, and autonomous driving.\n",
+      "10. Generative Models (2010s-present): Generative models are a class of AI algorithms that can generate new data that is similar to a given dataset. This has led to applications such as image synthesis, music generation, and language creation.\n",
+      "\n",
+      "These are just a few of the many developments in the history of AI. As the field continues to evolve, we can expect even more exciting breakthroughs and innovations in the years to come."
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=' Artificial intelligence (AI) has a rich and varied history that spans several decades. Hinweis: The following is a brief overview of the major milestones in the history of AI, but it is by no means exhaustive.\\n\\n1. Early Beginnings (1950s-1960s): The term \"Artificial Intelligence\" was coined in 1956 by computer scientist John McCarthy. However, the concept of creating machines that can think and learn like humans dates back to ancient times. In the 1950s and 1960s, researchers began exploring the possibilities of AI using simple algorithms and machine learning techniques.\\n2. Rule-Based Systems (1970s-1980s): In the 1970s and 1980s, AI research focused on developing rule-based systems, which use predefined rules to reason and make decisions. This led to the development of expert systems, which were designed to mimic the decision-making abilities of human experts in specific domains.\\n3. Machine Learning (1980s-1990s): The 1980s saw a shift towards machine learning, which enables machines to learn from data without being explicitly programmed. This led to the development of algorithms such as decision trees, neural networks, and support vector machines.\\n4. Deep Learning (2000s-present): In the early 2000s, deep learning emerged as a subfield of machine learning, focusing on neural networks with multiple layers. These networks can learn complex representations of data, leading to breakthroughs in image and speech recognition, natural language processing, and other areas.\\n5. Natural Language Processing (NLP) (1980s-present): NLP has been an active area of research since the 1980s, with a focus on developing algorithms that can understand and generate human language. This has led to applications such as chatbots, voice assistants, and language translation systems.\\n6. Robotics (1970s-present): The development of robotics has been closely tied to AI research, with a focus on creating machines that can perform tasks that typically require human intelligence, such as manipulation and locomotion.\\n7. Computer Vision (1980s-present): Computer vision has been an active area of research since the 1980s, with a focus on enabling machines to interpret and understand visual data from the world around us. This has led to applications such as image recognition, object detection, and autonomous driving.\\n8. Ethics and Society (1990s-present): As AI technology has become more advanced and integrated into various aspects of society, there has been a growing concern about the ethical implications of AI. This includes issues related to privacy, bias, and job displacement.\\n9. Reinforcement Learning (2000s-present): Reinforcement learning is a subfield of machine learning that involves training machines to make decisions based on feedback from their environment. This has led to breakthroughs in areas such as game playing, robotics, and autonomous driving.\\n10. Generative Models (2010s-present): Generative models are a class of AI algorithms that can generate new data that is similar to a given dataset. This has led to applications such as image synthesis, music generation, and language creation.\\n\\nThese are just a few of the many developments in the history of AI. As the field continues to evolve, we can expect even more exciting breakthroughs and innovations in the years to come.', additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.schema import HumanMessage\n",
+    "\n",
+    "messages = [\n",
+    "    HumanMessage(content=\"Tell me about the history of AI\")\n",
+    "]\n",
+    "chat_model(messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## RAG\n",
+    "\n",
+    "We can use Olama with RAG, [just as shown here](https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa).\n",
+    "\n",
+    "Let's use the 13b model:\n",
+    "\n",
+    "```\n",
+    "ollama pull llama2:13b\n",
+    "```\n",
+    "\n",
+    "Or, the 13b-chat model:\n",
+    "\n",
+    "```\n",
+    "ollama pull llama2:13b-chat\n",
+    "```\n",
+    "\n",
+    "Let's also use local embeddings from `GPT4AllEmbeddings` and `Chroma`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install gpt4all chromadb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import WebBaseLoader\n",
+    "loader = WebBaseLoader(\"https://lilianweng.github.io/posts/2023-06-23-agent/\")\n",
+    "data = loader.load()\n",
+    "\n",
+    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
+    "text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)\n",
+    "all_splits = text_splitter.split_documents(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found model file at  /Users/rlm/.cache/gpt4all/ggml-all-MiniLM-L6-v2-f16.bin\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.vectorstores import Chroma\n",
+    "from langchain.embeddings import GPT4AllEmbeddings\n",
+    "\n",
+    "vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "question = \"What are the approaches to Task Decomposition?\"\n",
+    "docs = vectorstore.similarity_search(question)\n",
+    "len(docs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain import PromptTemplate\n",
+    "\n",
+    "# Prompt\n",
+    "template = \"\"\"[INST] <<SYS>> Use the following pieces of context to answer the question at the end. \n",
+    "If you don't know the answer, just say that you don't know, don't try to make up an answer. \n",
+    "Use three sentences maximum and keep the answer as concise as possible. <</SYS>>\n",
+    "{context}\n",
+    "Question: {question}\n",
+    "Helpful Answer:[/INST]\"\"\"\n",
+    "QA_CHAIN_PROMPT = PromptTemplate(\n",
+    "    input_variables=[\"context\", \"question\"],\n",
+    "    template=template,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Chat model\n",
+    "from langchain.chat_models import ChatOllama\n",
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "chat_model = ChatOllama(model=\"llama2:13b-chat\",\n",
+    "                        verbose=True,\n",
+    "                        callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# QA chain\n",
+    "from langchain.chains import RetrievalQA\n",
+    "qa_chain = RetrievalQA.from_chain_type(\n",
+    "    chat_model,\n",
+    "    retriever=vectorstore.as_retriever(),\n",
+    "    chain_type_kwargs={\"prompt\": QA_CHAIN_PROMPT},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Based on the provided context, there are three approaches to task decomposition for AI agents:\n",
+      "\n",
+      "1. LLM with simple prompting, such as \"Steps for XYZ.\" or \"What are the subgoals for achieving XYZ?\"\n",
+      "2. Task-specific instructions, such as \"Write a story outline\" for writing a novel.\n",
+      "3. Human inputs."
+     ]
+    }
+   ],
+   "source": [
+    "question = \"What are the various approaches to Task Decomposition for AI Agents?\"\n",
+    "result = qa_chain({\"query\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also get logging for tokens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Based on the given context, here is the answer to the question \"What are the approaches to Task Decomposition?\"\n",
+      "\n",
+      "There are three approaches to task decomposition:\n",
+      "\n",
+      "1. LLM with simple prompting, such as \"Steps for XYZ.\" or \"What are the subgoals for achieving XYZ?\"\n",
+      "2. Using task-specific instructions, like \"Write a story outline\" for writing a novel.\n",
+      "3. With human inputs.{'model': 'llama2:13b-chat', 'created_at': '2023-08-23T15:37:51.469127Z', 'done': True, 'context': [1, 29871, 1, 29961, 25580, 29962, 518, 25580, 29962, 518, 25580, 29962, 3532, 14816, 29903, 6778, 4803, 278, 1494, 12785, 310, 3030, 304, 1234, 278, 1139, 472, 278, 1095, 29889, 29871, 13, 3644, 366, 1016, 29915, 29873, 1073, 278, 1234, 29892, 925, 1827, 393, 366, 1016, 29915, 29873, 1073, 29892, 1016, 29915, 29873, 1018, 304, 1207, 701, 385, 1234, 29889, 29871, 13, 11403, 2211, 25260, 7472, 322, 3013, 278, 1234, 408, 3022, 895, 408, 1950, 29889, 529, 829, 14816, 29903, 6778, 13, 5398, 26227, 508, 367, 2309, 313, 29896, 29897, 491, 365, 26369, 411, 2560, 9508, 292, 763, 376, 7789, 567, 363, 1060, 29979, 29999, 7790, 29876, 29896, 19602, 376, 5618, 526, 278, 1014, 1484, 1338, 363, 3657, 15387, 1060, 29979, 29999, 29973, 613, 313, 29906, 29897, 491, 773, 3414, 29899, 14940, 11994, 29936, 321, 29889, 29887, 29889, 376, 6113, 263, 5828, 27887, 1213, 363, 5007, 263, 9554, 29892, 470, 313, 29941, 29897, 411, 5199, 10970, 29889, 13, 13, 5398, 26227, 508, 367, 2309, 313, 29896, 29897, 491, 365, 26369, 411, 2560, 9508, 292, 763, 376, 7789, 567, 363, 1060, 29979, 29999, 7790, 29876, 29896, 19602, 376, 5618, 526, 278, 1014, 1484, 1338, 363, 3657, 15387, 1060, 29979, 29999, 29973, 613, 313, 29906, 29897, 491, 773, 3414, 29899, 14940, 11994, 29936, 321, 29889, 29887, 29889, 376, 6113, 263, 5828, 27887, 1213, 363, 5007, 263, 9554, 29892, 470, 313, 29941, 29897, 411, 5199, 10970, 29889, 13, 13, 1451, 16047, 267, 297, 1472, 29899, 8489, 18987, 322, 3414, 26227, 29901, 1858, 9450, 975, 263, 3309, 29891, 4955, 322, 17583, 3902, 8253, 278, 1650, 2913, 3933, 18066, 292, 29889, 365, 26369, 29879, 21117, 304, 10365, 13900, 746, 20050, 411, 15668, 4436, 29892, 3907, 963, 3109, 16424, 9401, 304, 25618, 1058, 5110, 515, 14260, 322, 1059, 29889, 13, 13, 1451, 16047, 267, 297, 1472, 29899, 8489, 18987, 322, 3414, 26227, 29901, 1858, 9450, 975, 263, 3309, 29891, 4955, 322, 17583, 3902, 8253, 278, 1650, 2913, 3933, 18066, 292, 29889, 365, 26369, 29879, 21117, 304, 10365, 13900, 746, 20050, 411, 15668, 4436, 29892, 3907, 963, 3109, 16424, 9401, 304, 25618, 1058, 5110, 515, 14260, 322, 1059, 29889, 13, 16492, 29901, 1724, 526, 278, 13501, 304, 9330, 897, 510, 3283, 29973, 13, 29648, 1319, 673, 10834, 29914, 25580, 29962, 518, 29914, 25580, 29962, 518, 29914, 25580, 29962, 29871, 16564, 373, 278, 2183, 3030, 29892, 1244, 338, 278, 1234, 304, 278, 1139, 376, 5618, 526, 278, 13501, 304, 9330, 897, 510, 3283, 3026, 13, 13, 8439, 526, 2211, 13501, 304, 3414, 26227, 29901, 13, 13, 29896, 29889, 365, 26369, 411, 2560, 9508, 292, 29892, 1316, 408, 376, 7789, 567, 363, 1060, 29979, 29999, 1213, 470, 376, 5618, 526, 278, 1014, 1484, 1338, 363, 3657, 15387, 1060, 29979, 29999, 3026, 13, 29906, 29889, 5293, 3414, 29899, 14940, 11994, 29892, 763, 376, 6113, 263, 5828, 27887, 29908, 363, 5007, 263, 9554, 29889, 13, 29941, 29889, 2973, 5199, 10970, 29889, 2], 'total_duration': 9514823750, 'load_duration': 795542, 'sample_count': 99, 'sample_duration': 68732000, 'prompt_eval_count': 146, 'prompt_eval_duration': 6206275000, 'eval_count': 98, 'eval_duration': 3229641000}\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.schema import LLMResult\n",
+    "from langchain.callbacks.base import BaseCallbackHandler\n",
+    "\n",
+    "class GenerationStatisticsCallback(BaseCallbackHandler):\n",
+    "    def on_llm_end(self, response: LLMResult, **kwargs) -> None:\n",
+    "        print(response.generations[0][0].generation_info)\n",
+    "        \n",
+    "callback_manager = CallbackManager([StreamingStdOutCallbackHandler(), GenerationStatisticsCallback()])\n",
+    "\n",
+    "chat_model = ChatOllama(model=\"llama2:13b-chat\",\n",
+    "                        verbose=True,\n",
+    "                        callback_manager=callback_manager)\n",
+    "\n",
+    "qa_chain = RetrievalQA.from_chain_type(\n",
+    "    chat_model,\n",
+    "    retriever=vectorstore.as_retriever(),\n",
+    "    chain_type_kwargs={\"prompt\": QA_CHAIN_PROMPT},\n",
+    ")\n",
+    "\n",
+    "question = \"What are the approaches to Task Decomposition?\"\n",
+    "result = qa_chain({\"query\": question})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`eval_count` / (`eval_duration`/10e9)  gets `tok / s`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "30.343929867127645"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "98 / (3229641000/1000/1000/1000)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/extras/integrations/chat/openai.ipynb

@@ -143,12 +143,39 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c095285d",
+   "cell_type": "markdown",
+   "id": "57e27714",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "## Fine-tuning\n",
+    "\n",
+    "You can call fine-tuned OpenAI models by passing in your corresponding `modelName` parameter.\n",
+    "\n",
+    "This generally takes the form of `ft:{OPENAI_MODEL_NAME}:{ORG_NAME}::{MODEL_ID}`. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "33c4a8b0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"J'adore la programmation.\", additional_kwargs={}, example=False)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fine_tuned_model = ChatOpenAI(temperature=0, model_name=\"ft:gpt-3.5-turbo-0613:langchain::7qTVM5AR\")\n",
+    "\n",
+    "fine_tuned_model(messages)"
+   ]
   }
  ],
  "metadata": {
@@ -167,7 +194,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.10.5"
   }
  },
  "nbformat": 4,

docs/extras/integrations/chat_loaders/discord.ipynb

@@ -0,0 +1,325 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c4ff9336-1cf3-459e-bd70-d1314c1da6a0",
+   "metadata": {},
+   "source": [
+    "# Discord\n",
+    "\n",
+    "This notebook shows how to create your own chat loader that works on copy-pasted messages (from dms) to a list of LangChain messages.\n",
+    "\n",
+    "The process has four steps:\n",
+    "1. Create the chat .txt file by copying chats from the Discord app and pasting them in a file on your local computer\n",
+    "2. Copy the chat loader definition from below to a local file.\n",
+    "3. Initialize the `DiscordChatLoader` with the file path pointed to the text file.\n",
+    "4. Call `loader.load()` (or `loader.lazy_load()`) to perform the conversion.\n",
+    "\n",
+    "## 1. Creat message dump\n",
+    "\n",
+    "Currently (2023/08/23) this loader only supports .txt files in the format generated by copying messages in the app to your clipboard and pasting in a file. Below is an example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e4ccfdfa-6869-4d67-90a0-ab99f01b7553",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overwriting discord_chats.txt\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%writefile discord_chats.txt\n",
+    "talkingtower — 08/15/2023 11:10 AM\n",
+    "Love music! Do you like jazz?\n",
+    "reporterbob — 08/15/2023 9:27 PM\n",
+    "Yes! Jazz is fantastic. Ever heard this one?\n",
+    "Website\n",
+    "Listen to classic jazz track...\n",
+    "\n",
+    "talkingtower — Yesterday at 5:03 AM\n",
+    "Indeed! Great choice. 🎷\n",
+    "reporterbob — Yesterday at 5:23 AM\n",
+    "Thanks! How about some virtual sightseeing?\n",
+    "Website\n",
+    "Virtual tour of famous landmarks...\n",
+    "\n",
+    "talkingtower — Today at 2:38 PM\n",
+    "Sounds fun! Let's explore.\n",
+    "reporterbob — Today at 2:56 PM\n",
+    "Enjoy the tour! See you around.\n",
+    "talkingtower — Today at 3:00 PM\n",
+    "Thank you! Goodbye! 👋\n",
+    "reporterbob — Today at 3:02 PM\n",
+    "Farewell! Happy exploring."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "359565a7-dad3-403c-a73c-6414b1295127",
+   "metadata": {},
+   "source": [
+    "## 2. Define chat loader\n",
+    "\n",
+    "LangChain currently does not support "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a429e0c4-4d7d-45f8-bbbb-c7fc5229f6af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "import re\n",
+    "from typing import Iterator, List\n",
+    "\n",
+    "from langchain import schema\n",
+    "from langchain.chat_loaders import base as chat_loaders\n",
+    "\n",
+    "logger = logging.getLogger()\n",
+    "\n",
+    "\n",
+    "class DiscordChatLoader(chat_loaders.BaseChatLoader):\n",
+    "    \n",
+    "    def __init__(self, path: str):\n",
+    "        \"\"\"\n",
+    "        Initialize the Discord chat loader.\n",
+    "\n",
+    "        Args:\n",
+    "            path: Path to the exported Discord chat text file.\n",
+    "        \"\"\"\n",
+    "        self.path = path\n",
+    "        self._message_line_regex = re.compile(\n",
+    "            r\"(.+?) — (\\w{3,9} \\d{1,2}(?:st|nd|rd|th)?(?:, \\d{4})? \\d{1,2}:\\d{2} (?:AM|PM)|Today at \\d{1,2}:\\d{2} (?:AM|PM)|Yesterday at \\d{1,2}:\\d{2} (?:AM|PM))\",  # noqa\n",
+    "            flags=re.DOTALL,\n",
+    "        )\n",
+    "\n",
+    "    def _load_single_chat_session_from_txt(\n",
+    "        self, file_path: str\n",
+    "    ) -> chat_loaders.ChatSession:\n",
+    "        \"\"\"\n",
+    "        Load a single chat session from a text file.\n",
+    "\n",
+    "        Args:\n",
+    "            file_path: Path to the text file containing the chat messages.\n",
+    "\n",
+    "        Returns:\n",
+    "            A `ChatSession` object containing the loaded chat messages.\n",
+    "        \"\"\"\n",
+    "        with open(file_path, \"r\", encoding=\"utf-8\") as file:\n",
+    "            lines = file.readlines()\n",
+    "\n",
+    "        results: List[schema.BaseMessage] = []\n",
+    "        current_sender = None\n",
+    "        current_timestamp = None\n",
+    "        current_content = []\n",
+    "        for line in lines:\n",
+    "            if re.match(\n",
+    "                r\".+? — (\\d{2}/\\d{2}/\\d{4} \\d{1,2}:\\d{2} (?:AM|PM)|Today at \\d{1,2}:\\d{2} (?:AM|PM)|Yesterday at \\d{1,2}:\\d{2} (?:AM|PM))\",  # noqa\n",
+    "                line,\n",
+    "            ):\n",
+    "                if current_sender and current_content:\n",
+    "                    results.append(\n",
+    "                        schema.HumanMessage(\n",
+    "                            content=\"\".join(current_content).strip(),\n",
+    "                            additional_kwargs={\n",
+    "                                \"sender\": current_sender,\n",
+    "                                \"events\": [{\"message_time\": current_timestamp}],\n",
+    "                            },\n",
+    "                        )\n",
+    "                    )\n",
+    "                current_sender, current_timestamp = line.split(\" — \")[:2]\n",
+    "                current_content = [\n",
+    "                    line[len(current_sender) + len(current_timestamp) + 4 :].strip()\n",
+    "                ]\n",
+    "            elif re.match(r\"\\[\\d{1,2}:\\d{2} (?:AM|PM)\\]\", line.strip()):\n",
+    "                results.append(\n",
+    "                    schema.HumanMessage(\n",
+    "                        content=\"\".join(current_content).strip(),\n",
+    "                        additional_kwargs={\n",
+    "                            \"sender\": current_sender,\n",
+    "                            \"events\": [{\"message_time\": current_timestamp}],\n",
+    "                        },\n",
+    "                    )\n",
+    "                )\n",
+    "                current_timestamp = line.strip()[1:-1]\n",
+    "                current_content = []\n",
+    "            else:\n",
+    "                current_content.append(\"\\n\" + line.strip())\n",
+    "\n",
+    "        if current_sender and current_content:\n",
+    "            results.append(\n",
+    "                schema.HumanMessage(\n",
+    "                    content=\"\".join(current_content).strip(),\n",
+    "                    additional_kwargs={\n",
+    "                        \"sender\": current_sender,\n",
+    "                        \"events\": [{\"message_time\": current_timestamp}],\n",
+    "                    },\n",
+    "                )\n",
+    "            )\n",
+    "\n",
+    "        return chat_loaders.ChatSession(messages=results)\n",
+    "\n",
+    "    def lazy_load(self) -> Iterator[chat_loaders.ChatSession]:\n",
+    "        \"\"\"\n",
+    "        Lazy load the messages from the chat file and yield them in the required format.\n",
+    "\n",
+    "        Yields:\n",
+    "            A `ChatSession` object containing the loaded chat messages.\n",
+    "        \"\"\"\n",
+    "        yield self._load_single_chat_session_from_txt(self.path)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8240393-48be-44d2-b0d6-52c215cd8ac2",
+   "metadata": {},
+   "source": [
+    "## 2. Create loader\n",
+    "\n",
+    "We will point to the file we just wrote to disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1268de40-b0e5-445d-9cd8-54856cd0293a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = DiscordChatLoader(\n",
+    "    path=\"./discord_chats.txt\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4928df4b-ae31-48a7-bd76-be3ecee1f3e0",
+   "metadata": {},
+   "source": [
+    "## 3. Load Messages\n",
+    "\n",
+    "Assuming the format is correct, the loader will convert the chats to langchain messages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c8a0836d-4a22-4790-bfe9-97f2145bb0d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "from langchain.chat_loaders.base import ChatSession\n",
+    "from langchain.chat_loaders.utils import (\n",
+    "    map_ai_messages,\n",
+    "    merge_chat_runs,\n",
+    ")\n",
+    "\n",
+    "raw_messages = loader.lazy_load()\n",
+    "# Merge consecutive messages from the same sender into a single message\n",
+    "merged_messages = merge_chat_runs(raw_messages)\n",
+    "# Convert messages from \"talkingtower\" to AI messages\n",
+    "messages: List[ChatSession] = list(map_ai_messages(merged_messages, sender=\"talkingtower\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1913963b-c44e-4f7a-aba7-0423c9b8bd59",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'messages': [AIMessage(content='Love music! Do you like jazz?', additional_kwargs={'sender': 'talkingtower', 'events': [{'message_time': '08/15/2023 11:10 AM\\n'}]}, example=False),\n",
+       "   HumanMessage(content='Yes! Jazz is fantastic. Ever heard this one?\\nWebsite\\nListen to classic jazz track...', additional_kwargs={'sender': 'reporterbob', 'events': [{'message_time': '08/15/2023 9:27 PM\\n'}]}, example=False),\n",
+       "   AIMessage(content='Indeed! Great choice. 🎷', additional_kwargs={'sender': 'talkingtower', 'events': [{'message_time': 'Yesterday at 5:03 AM\\n'}]}, example=False),\n",
+       "   HumanMessage(content='Thanks! How about some virtual sightseeing?\\nWebsite\\nVirtual tour of famous landmarks...', additional_kwargs={'sender': 'reporterbob', 'events': [{'message_time': 'Yesterday at 5:23 AM\\n'}]}, example=False),\n",
+       "   AIMessage(content=\"Sounds fun! Let's explore.\", additional_kwargs={'sender': 'talkingtower', 'events': [{'message_time': 'Today at 2:38 PM\\n'}]}, example=False),\n",
+       "   HumanMessage(content='Enjoy the tour! See you around.', additional_kwargs={'sender': 'reporterbob', 'events': [{'message_time': 'Today at 2:56 PM\\n'}]}, example=False),\n",
+       "   AIMessage(content='Thank you! Goodbye! 👋', additional_kwargs={'sender': 'talkingtower', 'events': [{'message_time': 'Today at 3:00 PM\\n'}]}, example=False),\n",
+       "   HumanMessage(content='Farewell! Happy exploring.', additional_kwargs={'sender': 'reporterbob', 'events': [{'message_time': 'Today at 3:02 PM\\n'}]}, example=False)]}]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "messages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8595a518-5c89-44aa-94a7-ca51e7e2a5fa",
+   "metadata": {},
+   "source": [
+    "### Next Steps\n",
+    "\n",
+    "You can then use these messages how you see fit, such as finetuning a model, few-shot example selection, or directly make predictions for the next message  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "08ff0a1e-fca0-4da3-aacd-d7401f99d946",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Thank you! Have a wonderful day! 🌟"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "\n",
+    "llm = ChatOpenAI()\n",
+    "\n",
+    "for chunk in llm.stream(messages[0]['messages']):\n",
+    "    print(chunk.content, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50a5251f-074a-4a3c-a2b0-b1de85e0ac6a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/chat_loaders/facebook.ipynb

@@ -0,0 +1,579 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e4bd269b",
+   "metadata": {},
+   "source": [
+    "# Facebook Messenger\n",
+    "\n",
+    "This notebook shows how to load data from Facebook in a format you can finetune on. The overall steps are:\n",
+    "\n",
+    "1. Download your messenger data to disk.\n",
+    "2. Create the Chat Loader and call `loader.load()` (or `loader.lazy_load()`) to perform the conversion.\n",
+    "3. Optionally use `merge_chat_runs` to combine message from the same sender in sequence, and/or `map_ai_messages` to convert messages from the specified sender to the \"AIMessage\" class. Once you've done this, call `convert_messages_for_finetuning` to prepare your data for fine-tuning.\n",
+    "\n",
+    "\n",
+    "Once this has been done, you can fine-tune your model. To do so you would complete the following steps:\n",
+    "\n",
+    "4. Upload your messages to OpenAI and run a fine-tuning job.\n",
+    "6. Use the resulting model in your LangChain app!\n",
+    "\n",
+    "\n",
+    "Let's begin.\n",
+    "\n",
+    "\n",
+    "## 1. Download Data\n",
+    "\n",
+    "To download your own messenger data, following instructions [here](https://www.zapptales.com/en/download-facebook-messenger-chat-history-how-to/). IMPORTANT - make sure to download them in JSON format (not HTML).\n",
+    "\n",
+    "We are hosting an example dump at [this google drive link](https://drive.google.com/file/d/1rh1s1o2i7B-Sk1v9o8KNgivLVGwJ-osV/view?usp=sharing) that we will use in this walkthrough."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "647f2158-a42e-4634-b283-b8492caf542a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File file.zip downloaded.\n",
+      "File file.zip has been unzipped.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This uses some example data\n",
+    "import requests\n",
+    "import zipfile\n",
+    "\n",
+    "def download_and_unzip(url: str, output_path: str = 'file.zip') -> None:\n",
+    "    file_id = url.split('/')[-2]\n",
+    "    download_url = f'https://drive.google.com/uc?export=download&id={file_id}'\n",
+    "\n",
+    "    response = requests.get(download_url)\n",
+    "    if response.status_code != 200:\n",
+    "        print('Failed to download the file.')\n",
+    "        return\n",
+    "\n",
+    "    with open(output_path, 'wb') as file:\n",
+    "        file.write(response.content)\n",
+    "        print(f'File {output_path} downloaded.')\n",
+    "\n",
+    "    with zipfile.ZipFile(output_path, 'r') as zip_ref:\n",
+    "        zip_ref.extractall()\n",
+    "        print(f'File {output_path} has been unzipped.')\n",
+    "\n",
+    "# URL of the file to download\n",
+    "url = 'https://drive.google.com/file/d/1rh1s1o2i7B-Sk1v9o8KNgivLVGwJ-osV/view?usp=sharing'\n",
+    "\n",
+    "# Download and unzip\n",
+    "download_and_unzip(url)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "48ef8bb1-fc28-453c-835a-94a552f05a91",
+   "metadata": {},
+   "source": [
+    "## 2. Create Chat Loader\n",
+    "\n",
+    "We have 2 different `FacebookMessengerChatLoader` classes, one for an entire directory of chats, and one to load individual files. We"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a0869bc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "directory_path = \"./hogwarts\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "0460bf25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_loaders.facebook_messenger import (\n",
+    "    SingleFileFacebookMessengerChatLoader,\n",
+    "    FolderFacebookMessengerChatLoader,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f61ee277",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = SingleFileFacebookMessengerChatLoader(\n",
+    "    path=\"./hogwarts/inbox/HermioneGranger/messages_Hermione_Granger.json\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "ec466ad7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[HumanMessage(content=\"Hi Hermione! How's your summer going so far?\", additional_kwargs={'sender': 'Harry Potter'}, example=False),\n",
+       " HumanMessage(content=\"Harry! Lovely to hear from you. My summer is going well, though I do miss everyone. I'm spending most of my time going through my books and researching fascinating new topics. How about you?\", additional_kwargs={'sender': 'Hermione Granger'}, example=False),\n",
+       " HumanMessage(content=\"I miss you all too. The Dursleys are being their usual unpleasant selves but I'm getting by. At least I can practice some spells in my room without them knowing. Let me know if you find anything good in your researching!\", additional_kwargs={'sender': 'Harry Potter'}, example=False)]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat_session = loader.load()[0]\n",
+    "chat_session[\"messages\"][:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8a3ee473",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = FolderFacebookMessengerChatLoader(\n",
+    "    path=\"./hogwarts\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "9f41e122",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat_sessions = loader.load()\n",
+    "len(chat_sessions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4aa3580-adc1-4b48-9bba-0e8e8d9f44ce",
+   "metadata": {},
+   "source": [
+    "## 3. Prepare for fine-tuning\n",
+    "\n",
+    "Calling `load()` returns all the chat messages we could extract as human messages. When conversing with chat bots, conversations typically follow a more strict alternating dialogue pattern relative to real conversations. \n",
+    "\n",
+    "You can choose to merge message \"runs\" (consecutive messages from the same sender) and select a sender to represent the \"AI\". The fine-tuned LLM will learn to generate these AI messages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "5a78030d-b757-4bbe-8a6c-841056f46df7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_loaders.utils import (\n",
+    "    merge_chat_runs,\n",
+    "    map_ai_messages,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ff35b028-78bf-4c5b-9ec6-939fe67de7f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merged_sessions = merge_chat_runs(chat_sessions)\n",
+    "alternating_sessions = list(map_ai_messages(merged_sessions, \"Harry Potter\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "4b11906e-a496-4d01-9f0d-1938c14147bf",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[AIMessage(content=\"Professor Snape, I was hoping I could speak with you for a moment about something that's been concerning me lately.\", additional_kwargs={'sender': 'Harry Potter'}, example=False),\n",
+       " HumanMessage(content=\"What is it, Potter? I'm quite busy at the moment.\", additional_kwargs={'sender': 'Severus Snape'}, example=False),\n",
+       " AIMessage(content=\"I apologize for the interruption, sir. I'll be brief. I've noticed some strange activity around the school grounds at night. I saw a cloaked figure lurking near the Forbidden Forest last night. I'm worried someone may be plotting something sinister.\", additional_kwargs={'sender': 'Harry Potter'}, example=False)]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Now all of Harry Potter's messages will take the AI message class\n",
+    "# which maps to the 'assistant' role in OpenAI's training format\n",
+    "alternating_sessions[0]['messages'][:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d985478d-062e-47b9-ae9a-102f59be07c0",
+   "metadata": {},
+   "source": [
+    "#### Now we can convert to OpenAI format dictionaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "21372331",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.adapters.openai import convert_messages_for_finetuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "92c5ae7a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prepared 9 dialogues for training\n"
+     ]
+    }
+   ],
+   "source": [
+    "training_data = convert_messages_for_finetuning(alternating_sessions)\n",
+    "print(f\"Prepared {len(training_data)} dialogues for training\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "dfcbd181",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'role': 'assistant',\n",
+       "  'content': \"Professor Snape, I was hoping I could speak with you for a moment about something that's been concerning me lately.\"},\n",
+       " {'role': 'user',\n",
+       "  'content': \"What is it, Potter? I'm quite busy at the moment.\"},\n",
+       " {'role': 'assistant',\n",
+       "  'content': \"I apologize for the interruption, sir. I'll be brief. I've noticed some strange activity around the school grounds at night. I saw a cloaked figure lurking near the Forbidden Forest last night. I'm worried someone may be plotting something sinister.\"}]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_data[0][:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1a9fd64-4f9f-42d3-b5dc-2a340e51e9e7",
+   "metadata": {},
+   "source": [
+    "OpenAI currently requires at least 10 training examples for a fine-tuning job, though they recommend between 50-100 for most tasks. Since we only have 9 chat sessions, we can subdivide them (optionally with some overlap) so that each training example is comprised of a portion of a whole conversation.\n",
+    "\n",
+    "Facebook chat sessions (1 per person) often span multiple days and conversations,\n",
+    "so the long-range dependencies may not be that important to model anyhow."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "13cd290a-b1e9-4686-bb5e-d99de8b8612b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Our chat is alternating, we will make each datapoint a group of 8 messages,\n",
+    "# with 2 messages overlapping\n",
+    "chunk_size = 8\n",
+    "overlap = 2\n",
+    "\n",
+    "training_examples = [\n",
+    "    conversation_messages[i: i + chunk_size] \n",
+    "    for conversation_messages in training_data\n",
+    "    for i in range(\n",
+    "        0, len(conversation_messages) - chunk_size + 1, \n",
+    "        chunk_size - overlap)\n",
+    "]\n",
+    "\n",
+    "len(training_examples)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc8baf41-ff07-4492-96bd-b2472ee7cef9",
+   "metadata": {},
+   "source": [
+    "## 4. Fine-tune the model\n",
+    "\n",
+    "It's time to fine-tune the model. Make sure you have `openai` installed\n",
+    "and have set your `OPENAI_API_KEY` appropriately"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "95ce3f63-3c80-44b2-9060-534ad74e16fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U openai --quiet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "ab9e28eb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File file-zCyNBeg4snpbBL7VkvsuhCz8 ready afer 30.55 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "from io import BytesIO\n",
+    "import time\n",
+    "\n",
+    "import openai\n",
+    "\n",
+    "# We will write the jsonl file in memory\n",
+    "my_file = BytesIO()\n",
+    "for m in training_examples:\n",
+    "    my_file.write((json.dumps({\"messages\": m}) + \"\\n\").encode('utf-8'))\n",
+    "\n",
+    "my_file.seek(0)\n",
+    "training_file = openai.File.create(\n",
+    "  file=my_file,\n",
+    "  purpose='fine-tune'\n",
+    ")\n",
+    "\n",
+    "# OpenAI audits each training file for compliance reasons.\n",
+    "# This make take a few minutes\n",
+    "status = openai.File.retrieve(training_file.id).status\n",
+    "start_time = time.time()\n",
+    "while status != \"processed\":\n",
+    "    print(f\"Status=[{status}]... {time.time() - start_time:.2f}s\", end=\"\\r\", flush=True)\n",
+    "    time.sleep(5)\n",
+    "    status = openai.File.retrieve(training_file.id).status\n",
+    "print(f\"File {training_file.id} ready after {time.time() - start_time:.2f} seconds.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "759a7f51-fde9-4b75-aaa9-e600e6537bd1",
+   "metadata": {},
+   "source": [
+    "With the file ready, it's time to kick off a training job."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "3f451425",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "job = openai.FineTuningJob.create(\n",
+    "    training_file=training_file.id,\n",
+    "    model=\"gpt-3.5-turbo\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "489b23ef-5e14-42a9-bafb-44220ec6960b",
+   "metadata": {},
+   "source": [
+    "Grab a cup of tea while your model is being prepared. This may take some time!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "bac1637a-c087-4523-ade1-c47f9bf4c6f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status=[running]... 908.87s\r"
+     ]
+    }
+   ],
+   "source": [
+    "status = openai.FineTuningJob.retrieve(job.id).status\n",
+    "start_time = time.time()\n",
+    "while status != \"succeeded\":\n",
+    "    print(f\"Status=[{status}]... {time.time() - start_time:.2f}s\", end=\"\\r\", flush=True)\n",
+    "    time.sleep(5)\n",
+    "    job = openai.FineTuningJob.retrieve(job.id)\n",
+    "    status = job.status"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "535895e1-bc69-40e5-82ed-e24ed2baeeee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ft:gpt-3.5-turbo-0613:personal::7rDwkaOq\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(job.fine_tuned_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "502ff73b-f9e9-49ce-ba45-401811e57946",
+   "metadata": {},
+   "source": [
+    "## 5. Use in LangChain\n",
+    "\n",
+    "You can use the resulting model ID directly the `ChatOpenAI` model class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "3925d60d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI(\n",
+    "    model=job.fine_tuned_model,\n",
+    "    temperature=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "7190cf2e-ab34-4ceb-bdad-45f24f069c29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | model | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "f02057e9-f914-40b1-9c9d-9432ff594b98",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The usual - Potions, Transfiguration, Defense Against the Dark Arts. What about you?"
+     ]
+    }
+   ],
+   "source": [
+    "for tok in chain.stream({\"input\": \"What classes are you taking?\"}):\n",
+    "    print(tok, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35331503-3cc6-4d64-955e-64afe6b5fef3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/chat_loaders/gmail.ipynb

@@ -0,0 +1,179 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b3d1705d",
+   "metadata": {},
+   "source": [
+    "# GMail\n",
+    "\n",
+    "This loader goes over how to load data from GMail. There are many ways you could want to load data from GMail. This loader is currently fairly opionated in how to do so. The way it does it is it first looks for all messages that you have sent. It then looks for messages where you are responding to a previous email. It then fetches that previous email, and creates a training example of that email, followed by your email.\n",
+    "\n",
+    "Note that there are clear limitations here. For example, all examples created are only looking at the previous email for context.\n",
+    "\n",
+    "To use:\n",
+    "\n",
+    "- Set up a Google Developer Account: Go to the Google Developer Console, create a project, and enable the Gmail API for that project. This will give you a credentials.json file that you'll need later.\n",
+    "\n",
+    "- Install the Google Client Library: Run the following command to install the Google Client Library:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84578039",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install --upgrade google-auth google-auth-oauthlib google-auth-httplib2 google-api-python-client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "be18f796",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os.path\n",
+    "import base64\n",
+    "import json\n",
+    "import re\n",
+    "import time\n",
+    "from google.auth.transport.requests import Request\n",
+    "from google.oauth2.credentials import Credentials\n",
+    "from google_auth_oauthlib.flow import InstalledAppFlow\n",
+    "from googleapiclient.discovery import build\n",
+    "import logging\n",
+    "import requests\n",
+    "\n",
+    "SCOPES = ['https://www.googleapis.com/auth/gmail.readonly']\n",
+    "\n",
+    "\n",
+    "creds = None\n",
+    "# The file token.json stores the user's access and refresh tokens, and is\n",
+    "# created automatically when the authorization flow completes for the first\n",
+    "# time.\n",
+    "if os.path.exists('email_token.json'):\n",
+    "    creds = Credentials.from_authorized_user_file('email_token.json', SCOPES)\n",
+    "# If there are no (valid) credentials available, let the user log in.\n",
+    "if not creds or not creds.valid:\n",
+    "    if creds and creds.expired and creds.refresh_token:\n",
+    "        creds.refresh(Request())\n",
+    "    else:\n",
+    "        flow = InstalledAppFlow.from_client_secrets_file(               \n",
+    "            # your creds file here. Please create json file as here https://cloud.google.com/docs/authentication/getting-started\n",
+    "            'creds.json', SCOPES)\n",
+    "        creds = flow.run_local_server(port=0)\n",
+    "    # Save the credentials for the next run\n",
+    "    with open('email_token.json', 'w') as token:\n",
+    "        token.write(creds.to_json())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a2793ba0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_loaders.gmail import GMailLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2154597f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = GMailLoader(creds=creds, n=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "0b7d11bd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "74764bc7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sometimes there can be errors which we silently ignore\n",
+    "len(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d9360a85",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_loaders.utils import (\n",
+    "    map_ai_messages,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "a9646f7a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This makes messages sent by hchase@langchain.com the AI Messages\n",
+    "# This means you will train an LLM to predict as if it's responding as hchase\n",
+    "training_data = list(map_ai_messages(data, sender=\"Harrison Chase <hchase@langchain.com>\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d1a182f0",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/extras/integrations/chat_loaders/imessage.ipynb

@@ -0,0 +1,420 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "01fcfa2f-33a9-48f3-835a-b1956c394d6b",
+   "metadata": {},
+   "source": [
+    "# iMessage\n",
+    "\n",
+    "This notebook shows how to use the iMessage chat loader. This class helps convert iMessage conversations to LangChain chat messages.\n",
+    "\n",
+    "On MacOS, iMessage stores conversations in a sqlite database at `~/Library/Messages/chat.db` (at least for macOS Ventura 13.4). \n",
+    "The `IMessageChatLoader` loads from this database file. \n",
+    "\n",
+    "1. Create the `IMessageChatLoader` with the file path pointed to `chat.db` database you'd like to process.\n",
+    "2. Call `loader.load()` (or `loader.lazy_load()`) to perform the conversion. Optionally use `merge_chat_runs` to combine message from the same sender in sequence, and/or `map_ai_messages` to convert messages from the specified sender to the \"AIMessage\" class.\n",
+    "\n",
+    "## 1. Access Chat DB\n",
+    "\n",
+    "It's likely that your terminal is denied access to `~/Library/Messages`. To use this class, you can copy the DB to an accessible directory (e.g., Documents) and load from there. Alternatively (and not recommended), you can grant full disk access for your terminal emulator in System Settings > Securityand Privacy > Full Disk Access.\n",
+    "\n",
+    "We have created an example database you can use at [this linked drive file](https://drive.google.com/file/d/1NebNKqTA2NXApCmeH6mu0unJD2tANZzo/view?usp=sharing)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "036ce7e0-a38f-4cbe-89a6-a205ae7c23be",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File chat.db downloaded.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# This uses some example data\n",
+    "import requests\n",
+    "\n",
+    "def download_drive_file(url: str, output_path: str = 'chat.db') -> None:\n",
+    "    file_id = url.split('/')[-2]\n",
+    "    download_url = f'https://drive.google.com/uc?export=download&id={file_id}'\n",
+    "\n",
+    "    response = requests.get(download_url)\n",
+    "    if response.status_code != 200:\n",
+    "        print('Failed to download the file.')\n",
+    "        return\n",
+    "\n",
+    "    with open(output_path, 'wb') as file:\n",
+    "        file.write(response.content)\n",
+    "        print(f'File {output_path} downloaded.')\n",
+    "\n",
+    "url = 'https://drive.google.com/file/d/1NebNKqTA2NXApCmeH6mu0unJD2tANZzo/view?usp=sharing'\n",
+    "\n",
+    "# Download file to chat.db\n",
+    "download_drive_file(url)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf60f703-76f1-4602-a723-02c59535c1af",
+   "metadata": {},
+   "source": [
+    "## 2. Create the Chat Loader\n",
+    "\n",
+    "Provide the loader with the file path to the zip directory. You can optionally specify the user id that maps to an ai message as well an configure whether to merge message runs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4b8b432a-d2bc-49e1-b35f-761730a8fd6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_loaders.imessage import IMessageChatLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8ec6661b-0aca-48ae-9e2b-6412856c287b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = IMessageChatLoader(\n",
+    "    path=\"./chat.db\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8805a7c5-84b4-49f5-8989-0022f2054ace",
+   "metadata": {},
+   "source": [
+    "## 3. Load messages\n",
+    "\n",
+    "The `load()` (or `lazy_load`) methods return a list of \"ChatSessions\" that currently just contain a list of messages per loaded conversation. All messages are mapped to \"HumanMessage\" objects to start. \n",
+    "\n",
+    "You can optionally choose to merge message \"runs\" (consecutive messages from the same sender) and select a sender to represent the \"AI\". The fine-tuned LLM will learn to generate these AI messages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "fcd69b3e-020d-4a15-8a0d-61c2d34e1ee1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List\n",
+    "from langchain.chat_loaders.base import ChatSession\n",
+    "from langchain.chat_loaders.utils import (\n",
+    "    map_ai_messages,\n",
+    "    merge_chat_runs,\n",
+    ")\n",
+    "\n",
+    "raw_messages = loader.lazy_load()\n",
+    "# Merge consecutive messages from the same sender into a single message\n",
+    "merged_messages = merge_chat_runs(raw_messages)\n",
+    "# Convert messages from \"Tortoise\" to AI messages. Do you have a guess who these conversations are between?\n",
+    "chat_sessions: List[ChatSession] = list(map_ai_messages(merged_messages, sender=\"Tortoise\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "370b8c26-c7a8-434c-a225-45c20ff14a03",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[AIMessage(content=\"Slow and steady, that's my motto.\", additional_kwargs={'message_time': 1693182723, 'sender': 'Tortoise'}, example=False),\n",
+       " HumanMessage(content='Speed is key!', additional_kwargs={'message_time': 1693182753, 'sender': 'Hare'}, example=False),\n",
+       " AIMessage(content='A balanced approach is more reliable.', additional_kwargs={'message_time': 1693182783, 'sender': 'Tortoise'}, example=False)]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Now all of the Tortoise's messages will take the AI message class\n",
+    "# which maps to the 'assistant' role in OpenAI's training format\n",
+    "alternating_sessions[0]['messages'][:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05208f9d-3193-4a8d-86a5-13df2c8197e5",
+   "metadata": {},
+   "source": [
+    "## 3. Prepare for fine-tuning\n",
+    "\n",
+    "Now it's time to convert our chat  messages to OpenAI dictionaries. We can use the `convert_messages_for_finetuning` utility to do so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8834861f-f37f-4c08-96c6-917269bf09b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.adapters.openai import convert_messages_for_finetuning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "ce7ab0f9-6e6a-4a1c-8b86-c635251d437e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Prepared 10 dialogues for training\n"
+     ]
+    }
+   ],
+   "source": [
+    "training_data = convert_messages_for_finetuning(alternating_sessions)\n",
+    "print(f\"Prepared {len(training_data)} dialogues for training\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b494d64c-8056-42ae-b4c1-a9cfabc002ea",
+   "metadata": {},
+   "source": [
+    "## 4. Fine-tune the model\n",
+    "\n",
+    "It's time to fine-tune the model. Make sure you have `openai` installed\n",
+    "and have set your `OPENAI_API_KEY` appropriately"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b4b60daa-b899-4291-a09a-412ce9c218fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# %pip install -U openai --quiet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "2cca6c95-c0d6-4826-b4fa-1c403f217f93",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File file-zHIgf4r8LltZG3RFpkGd4Sjf ready after 10.19 seconds.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "from io import BytesIO\n",
+    "import time\n",
+    "\n",
+    "import openai\n",
+    "\n",
+    "# We will write the jsonl file in memory\n",
+    "my_file = BytesIO()\n",
+    "for m in training_data:\n",
+    "    my_file.write((json.dumps({\"messages\": m}) + \"\\n\").encode('utf-8'))\n",
+    "\n",
+    "my_file.seek(0)\n",
+    "training_file = openai.File.create(\n",
+    "  file=my_file,\n",
+    "  purpose='fine-tune'\n",
+    ")\n",
+    "\n",
+    "# OpenAI audits each training file for compliance reasons.\n",
+    "# This make take a few minutes\n",
+    "status = openai.File.retrieve(training_file.id).status\n",
+    "start_time = time.time()\n",
+    "while status != \"processed\":\n",
+    "    print(f\"Status=[{status}]... {time.time() - start_time:.2f}s\", end=\"\\r\", flush=True)\n",
+    "    time.sleep(5)\n",
+    "    status = openai.File.retrieve(training_file.id).status\n",
+    "print(f\"File {training_file.id} ready after {time.time() - start_time:.2f} seconds.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60ee0476-3113-4dc8-a886-bce878c60b07",
+   "metadata": {},
+   "source": [
+    "With the file ready, it's time to kick off a training job."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "c376ddca-5b4f-4e5a-bf4e-6beeb467eacc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "job = openai.FineTuningJob.create(\n",
+    "    training_file=training_file.id,\n",
+    "    model=\"gpt-3.5-turbo\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "09344c60-0bee-4989-b8d1-4a8821553cc3",
+   "metadata": {},
+   "source": [
+    "Grab a cup of tea while your model is being prepared. This may take some time!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "22eae900-04ca-456b-ba51-1dfff1f8e0e1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Status=[running]... 524.95s\r"
+     ]
+    }
+   ],
+   "source": [
+    "status = openai.FineTuningJob.retrieve(job.id).status\n",
+    "start_time = time.time()\n",
+    "while status != \"succeeded\":\n",
+    "    print(f\"Status=[{status}]... {time.time() - start_time:.2f}s\", end=\"\\r\", flush=True)\n",
+    "    time.sleep(5)\n",
+    "    job = openai.FineTuningJob.retrieve(job.id)\n",
+    "    status = job.status"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "39e72616-a7d9-44b8-a4eb-506611d119f4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ft:gpt-3.5-turbo-0613:personal::7sKoRdlz\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(job.fine_tuned_model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d717749-b1b6-451f-b3c5-3286b82d45b9",
+   "metadata": {},
+   "source": [
+    "## 5. Use in LangChain\n",
+    "\n",
+    "You can use the resulting model ID directly the `ChatOpenAI` model class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "1579dfca-95c6-47b7-8549-1195b9dce5b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "\n",
+    "model = ChatOpenAI(\n",
+    "    model=job.fine_tuned_model,\n",
+    "    temperature=1,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "6f53d1b1-dcbf-4976-a61a-17f74c6f1b0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You are speaking to hare.\"),\n",
+    "        (\"human\", \"{input}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | model | StrOutputParser()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "6619c9bc-54ea-4136-bd9a-44557f7da724",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A symbol of interconnectedness."
+     ]
+    }
+   ],
+   "source": [
+    "for tok in chain.stream({\"input\": \"What's the golden thread?\"}):\n",
+    "    print(tok, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88e0d1a1-48a9-4d9d-9f4e-010cbbb65af8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}