Prevent Outside update

import from core instead of langchain.schema

.github/CONTRIBUTING.md

@@ -23,7 +23,7 @@ It's essential that we maintain great documentation and testing. If you:
   - Update any affected example notebooks and documentation. These live in `docs`.
   - Update unit and integration tests when relevant.
 - Add a feature
-  - Add a demo notebook in `docs/modules`.
+  - Add a demo notebook in `docs/docs/`.
   - Add unit and integration tests.
 
 We are a small, progress-oriented team. If there's something you'd like to add or change, opening a pull request is the
@@ -72,9 +72,10 @@ tell Poetry to use the virtualenv python environment (`poetry config virtualenvs
 
 ### Core vs. Experimental
 
-This repository contains two separate projects:
+This repository contains three separate projects:
 - `langchain`: core langchain code, abstractions, and use cases.
-- `langchain.experimental`: see the [Experimental README](https://github.com/langchain-ai/langchain/tree/master/libs/experimental/README.md) for more information.
+- `langchain_core`: contain interfaces for key abstractions as well as logic for combining them in chains (LCEL).
+- `langchain_experimental`: see the [Experimental README](https://github.com/langchain-ai/langchain/tree/master/libs/experimental/README.md) for more information.
 
 Each of these has its own development environment. Docs are run from the top-level makefile, but development
 is split across separate test & release flows.
@@ -128,6 +129,24 @@ make docker_tests
 
 There are also [integration tests and code-coverage](https://github.com/langchain-ai/langchain/tree/master/libs/langchain/tests/README.md) available.
 
+### Only develop langchain_core or langchain_experimental
+
+If you are only developing `langchain_core` or `langchain_experimental`, you can simply install the dependencies for the respective projects and run tests:
+
+```bash
+cd libs/core
+poetry install --with test
+make test
+```
+
+Or:
+
+```bash
+cd libs/experimental
+poetry install --with test
+make test
+```
+
 ### Formatting and Linting
 
 Run these locally before submitting a PR; the CI system will check also.
@@ -214,6 +233,10 @@ ignore-words-list = 'momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogy
 
 Langchain relies heavily on optional dependencies to keep the Langchain package lightweight.
 
+You only need to add a new dependency if a **unit test** relies on the package.
+If your package is only required for **integration tests**, then you can skip these
+steps and leave all pyproject.toml and poetry.lock files alone.
+
 If you're adding a new dependency to Langchain, assume that it will be an optional dependency, and
 that most users won't have it installed.
 

.github/scripts/check_diff.py

@@ -0,0 +1,45 @@
+import json
+import sys
+
+ALL_DIRS = {
+    "libs/core",
+    "libs/langchain",
+    "libs/experimental",
+}
+
+if __name__ == "__main__":
+    files = sys.argv[1:]
+    dirs_to_run = set()
+
+    for file in files:
+        if any(
+            file.startswith(dir_)
+            for dir_ in (
+                ".github/workflows",
+                ".github/tools",
+                ".github/actions",
+                "libs/core",
+                ".github/scripts/check_diff.py",
+            )
+        ):
+            dirs_to_run = ALL_DIRS
+            break
+        elif "libs/community" in file:
+            dirs_to_run.update(
+                ("libs/community", "libs/langchain", "libs/experimental")
+            )
+        elif "libs/partners" in file:
+            partner_dir = file.split("/")[2]
+            dirs_to_run.update(
+                (f"libs/partners/{partner_dir}", "libs/langchain", "libs/experimental")
+            )
+        elif "libs/langchain" in file:
+            dirs_to_run.update(("libs/langchain", "libs/experimental"))
+        elif "libs/experimental" in file:
+            dirs_to_run.add("libs/experimental")
+        elif file.startswith("libs/"):
+            dirs_to_run = ALL_DIRS
+            break
+        else:
+            pass
+    print(json.dumps(list(dirs_to_run)))

.github/workflows/_all_ci.yml

@@ -1,20 +1,24 @@
 ---
-name: libs/langchain CI
+name: langchain CI
 
 on:
-  push:
-    branches: [ master ]
-  pull_request:
-    paths:
-      - '.github/actions/poetry_setup/action.yml'
-      - '.github/tools/**'
-      - '.github/workflows/_lint.yml'
-      - '.github/workflows/_test.yml'
-      - '.github/workflows/_pydantic_compatibility.yml'
-      - '.github/workflows/langchain_ci.yml'
-      - 'libs/*'
-      - 'libs/langchain/**'
-  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+  workflow_call:
+    inputs:
+      working-directory:
+        required: true
+        type: string
+        description: "From which folder this pipeline executes"
+  workflow_dispatch:
+    inputs:
+      working-directory:
+        required: true
+        type: choice
+        default: 'libs/langchain'
+        options:
+        - libs/langchain
+        - libs/core
+        - libs/experimental
+
 
 # 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.
@@ -23,47 +27,40 @@ on:
 # 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 }}
+  group: ${{ github.workflow }}-${{ github.ref }}-${{ inputs.working-directory }}
   cancel-in-progress: true
 
 env:
   POETRY_VERSION: "1.6.1"
-  WORKDIR: "libs/langchain"
 
 jobs:
   lint:
-    uses:
-      ./.github/workflows/_lint.yml
+    uses: ./.github/workflows/_lint.yml
     with:
-      working-directory: libs/langchain
+      working-directory: ${{ inputs.working-directory }}
     secrets: inherit
 
   test:
-    uses:
-      ./.github/workflows/_test.yml
+    uses: ./.github/workflows/_test.yml
     with:
-      working-directory: libs/langchain
+      working-directory: ${{ inputs.working-directory }}
     secrets: inherit
 
   compile-integration-tests:
-    uses:
-      ./.github/workflows/_compile_integration_test.yml
+    uses: ./.github/workflows/_compile_integration_test.yml
+    if: ${{ inputs.working-directory != 'libs/core' }}
     with:
-      working-directory: libs/langchain
+      working-directory: ${{ inputs.working-directory }}
     secrets: inherit
 
   pydantic-compatibility:
-    uses:
-      ./.github/workflows/_pydantic_compatibility.yml
+    uses: ./.github/workflows/_pydantic_compatibility.yml
     with:
-      working-directory: libs/langchain
+      working-directory: ${{ inputs.working-directory }}
     secrets: inherit
 
   extended-tests:
     runs-on: ubuntu-latest
-    defaults:
-      run:
-        working-directory: ${{ env.WORKDIR }}
     strategy:
       matrix:
         python-version:
@@ -71,7 +68,11 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
+    if: ${{ inputs.working-directory == 'libs/langchain' }}
     name: Python ${{ matrix.python-version }} extended tests
+    defaults:
+      run:
+        working-directory: ${{ inputs.working-directory }}
     steps:
       - uses: actions/checkout@v4
 
@@ -80,7 +81,7 @@ jobs:
         with:
           python-version: ${{ matrix.python-version }}
           poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: libs/langchain
+          working-directory: ${{ inputs.working-directory }}
           cache-key: extended
 
       - name: Install dependencies
@@ -89,6 +90,11 @@ jobs:
           echo "Running extended tests, installing dependencies with poetry..."
           poetry install -E extended_testing
 
+      - name: Install langchain core editable
+        shell: bash
+        run: |
+          poetry run pip install -e ../core
+
       - name: Run extended tests
         run: make extended_tests
 

.github/workflows/_lint.yml

@@ -68,7 +68,7 @@ jobs:
         # It doesn't matter how you change it, any change will cause a cache-bust.
         working-directory: ${{ inputs.working-directory }}
         run: |
-          poetry install --with dev,lint,test,typing
+          poetry install --with lint,typing
 
       - name: Install langchain editable
         working-directory: ${{ inputs.working-directory }}
@@ -76,7 +76,7 @@ jobs:
         env:
           LANGCHAIN_LOCATION: ${{ inputs.langchain-location }}
         run: |
-          pip install -e "$LANGCHAIN_LOCATION"
+          poetry run pip install -e "$LANGCHAIN_LOCATION"
 
       - name: Get .mypy_cache to speed up mypy
         uses: actions/cache@v3

.github/workflows/_pydantic_compatibility.yml

@@ -7,6 +7,10 @@ on:
         required: true
         type: string
         description: "From which folder this pipeline executes"
+      langchain-location:
+        required: false
+        type: string
+        description: "Relative path to the langchain library folder"
 
 env:
   POETRY_VERSION: "1.6.1"
@@ -40,6 +44,14 @@ jobs:
         shell: bash
         run: poetry install
 
+      - name: Install langchain editable
+        working-directory: ${{ inputs.working-directory }}
+        if: ${{ inputs.langchain-location }}
+        env:
+          LANGCHAIN_LOCATION: ${{ inputs.langchain-location }}
+        run: |
+          poetry run pip install -e "$LANGCHAIN_LOCATION"
+
       - name: Install the opposite major version of pydantic
         # If normal tests use pydantic v1, here we'll use v2, and vice versa.
         shell: bash

.github/workflows/_test.yml

@@ -7,6 +7,10 @@ on:
         required: true
         type: string
         description: "From which folder this pipeline executes"
+      langchain-location:
+        required: false
+        type: string
+        description: "Relative path to the langchain library folder"
 
 env:
   POETRY_VERSION: "1.6.1"
@@ -38,11 +42,20 @@ jobs:
 
       - name: Install dependencies
         shell: bash
-        run: poetry install
+        run: poetry install --with test
+
+      - name: Install langchain editable
+        working-directory: ${{ inputs.working-directory }}
+        if: ${{ inputs.langchain-location }}
+        env:
+          LANGCHAIN_LOCATION: ${{ inputs.langchain-location }}
+        run: |
+          poetry run pip install -e "$LANGCHAIN_LOCATION"
 
       - name: Run core tests
         shell: bash
-        run: make test
+        run: |
+          make test
 
       - name: Ensure the tests did not create any additional files
         shell: bash

.github/workflows/check_diffs.yml

@@ -0,0 +1,47 @@
+---
+name: Check library diffs
+
+on:
+  push:
+    branches: [master]
+  pull_request:
+    paths:
+      - ".github/actions/**"
+      - ".github/tools/**"
+      - ".github/workflows/**"
+      - "libs/**"
+
+# 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
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: actions/setup-python@v4
+        with:
+          python-version: '3.10'
+      - id: files
+        uses: Ana06/get-changed-files@v2.2.0
+      - id: set-matrix
+        run: echo "dirs-to-run=$(python .github/scripts/check_diff.py ${{ steps.files.outputs.all }})" >> $GITHUB_OUTPUT
+    outputs:
+      dirs-to-run: ${{ steps.set-matrix.outputs.dirs-to-run }}
+  ci:
+    needs: [ build ]
+    strategy:
+      matrix:
+        working-directory: ${{ fromJson(needs.build.outputs.dirs-to-run) }}
+    uses: ./.github/workflows/_all_ci.yml
+    with:
+      working-directory: ${{ matrix.working-directory }}
+
+

.github/workflows/langchain_cli_ci.yml

@@ -1,47 +0,0 @@
----
-name: libs/cli CI
-
-on:
-  push:
-    branches: [ master ]
-  pull_request:
-    paths:
-      - '.github/actions/poetry_setup/action.yml'
-      - '.github/tools/**'
-      - '.github/workflows/_lint.yml'
-      - '.github/workflows/_test.yml'
-      - '.github/workflows/_pydantic_compatibility.yml'
-      - '.github/workflows/langchain_cli_ci.yml'
-      - 'libs/cli/**'
-      - 'libs/*'
-  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: "1.6.1"
-  WORKDIR: "libs/cli"
-
-jobs:
-  lint:
-    uses:
-      ./.github/workflows/_lint.yml
-    with:
-      working-directory: libs/cli
-      langchain-location: ../langchain
-    secrets: inherit
-
-  test:
-    uses:
-      ./.github/workflows/_test.yml
-    with:
-      working-directory: libs/cli
-    secrets: inherit

.github/workflows/langchain_core_release.yml

@@ -0,0 +1,13 @@
+---
+name: libs/core Release
+
+on:
+  workflow_dispatch:  # Allows to trigger the workflow manually in GitHub UI
+
+jobs:
+  release:
+    uses:
+      ./.github/workflows/_release.yml
+    with:
+      working-directory: libs/core
+    secrets: inherit

.github/workflows/langchain_experimental_ci.yml

@@ -1,137 +0,0 @@
----
-name: libs/experimental CI
-
-on:
-  push:
-    branches: [ master ]
-  pull_request:
-    paths:
-      - '.github/actions/poetry_setup/action.yml'
-      - '.github/tools/**'
-      - '.github/workflows/_lint.yml'
-      - '.github/workflows/_test.yml'
-      - '.github/workflows/langchain_experimental_ci.yml'
-      - 'libs/*'
-      - '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: "1.6.1"
-  WORKDIR: "libs/experimental"
-
-jobs:
-  lint:
-    uses:
-      ./.github/workflows/_lint.yml
-    with:
-      working-directory: libs/experimental
-      langchain-location: ../langchain
-    secrets: inherit
-
-  test:
-    uses:
-      ./.github/workflows/_test.yml
-    with:
-      working-directory: libs/experimental
-    secrets: inherit
-
-  compile-integration-tests:
-    uses:
-      ./.github/workflows/_compile_integration_test.yml
-    with:
-      working-directory: libs/experimental
-    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@v4
-
-      - 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@v4
-
-      - name: Set up Python ${{ matrix.python-version }} + Poetry ${{ env.POETRY_VERSION }}
-        uses: "./.github/actions/poetry_setup"
-        with:
-          python-version: ${{ matrix.python-version }}
-          poetry-version: ${{ env.POETRY_VERSION }}
-          working-directory: libs/experimental
-          cache-key: extended
-
-      - name: Install dependencies
-        shell: bash
-        run: |
-          echo "Running extended tests, installing dependencies with poetry..."
-          poetry install -E extended_testing
-
-      - name: Run extended tests
-        run: make extended_tests
-
-      - name: Ensure the tests did not create any additional files
-        shell: bash
-        run: |
-          set -eu
-
-          STATUS="$(git status)"
-          echo "$STATUS"
-
-          # grep will exit non-zero if the target message isn't found,
-          # and `set -e` above will cause the step to fail.
-          echo "$STATUS" | grep 'nothing to commit, working tree clean'

LICENSE

@@ -1,6 +1,6 @@
-The MIT License
+MIT License
 
-Copyright (c) Harrison Chase
+Copyright (c) LangChain, Inc.
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal
@@ -9,13 +9,13 @@ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 copies of the Software, and to permit persons to whom the Software is
 furnished to do so, subject to the following conditions:
 
-The above copyright notice and this permission notice shall be included in
-all copies or substantial portions of the Software.
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
 
 THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
-THE SOFTWARE.
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Makefile

@@ -44,6 +44,7 @@ spell_fix:
 lint:
 	poetry run ruff docs templates cookbook
 	poetry run ruff format docs templates cookbook --diff
+	poetry run ruff --select I docs templates cookbook
 
 format format_diff:
 	poetry run ruff format docs templates cookbook

README.md

@@ -30,7 +30,7 @@ pip install langchain
 
 With conda:
 ```bash
-pip install langsmith && conda install langchain -c conda-forge
+conda install langchain -c conda-forge
 ```
 
 ## 🤔 What is LangChain?
@@ -104,3 +104,7 @@ Please see [here](https://python.langchain.com) for full documentation, which in
 As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
 
 For detailed information on how to contribute, see [here](.github/CONTRIBUTING.md).
+
+## 🌟 Contributors
+
+[![langchain contributors](https://contrib.rocks/image?repo=langchain-ai/langchain&max=2000)](https://github.com/langchain-ai/langchain/graphs/contributors)

cookbook/code-analysis-deeplake.ipynb

@@ -648,7 +648,7 @@
     {
      "data": {
       "text/plain": [
-       "OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', deployment='text-embedding-ada-002', openai_api_version='', openai_api_base='', openai_api_type='', openai_proxy='', embedding_ctx_length=8191, openai_api_key='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', openai_organization='', allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6, request_timeout=None, headers=None, tiktoken_model_name=None, show_progress_bar=False, model_kwargs={})"
+       "OpenAIEmbeddings(client=<class 'openai.api_resources.embedding.Embedding'>, model='text-embedding-ada-002', deployment='text-embedding-ada-002', openai_api_version='', openai_api_base='', openai_api_type='', openai_proxy='', embedding_ctx_length=8191, openai_api_key='', openai_organization='', allowed_special=set(), disallowed_special='all', chunk_size=1000, max_retries=6, request_timeout=None, headers=None, tiktoken_model_name=None, show_progress_bar=False, model_kwargs={})"
       ]
      },
      "execution_count": 13,

cookbook/llm_bash.ipynb

@@ -69,8 +69,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.chains.llm_bash.prompt import BashOutputParser\n",
     "from langchain.prompts.prompt import PromptTemplate\n",
+    "from langchain_experimental.llm_bash.prompt import BashOutputParser\n",
     "\n",
     "_PROMPT_TEMPLATE = \"\"\"If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put \"#!/bin/bash\" in your answer. Make sure to reason step by step, using this format:\n",
     "Question: \"copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'\"\n",

docs/.local_build.sh

@@ -9,13 +9,15 @@ SCRIPT_DIR="$(cd "$(dirname "$0")"; pwd)"
 cd "${SCRIPT_DIR}"
 
 mkdir -p ../_dist
-cp -r . ../_dist
+rsync -ruv --exclude node_modules --exclude api_reference --exclude .venv --exclude .docusaurus . ../_dist
 cd ../_dist
 poetry run python scripts/model_feat_table.py
-poetry run nbdoc_build --srcdir docs
 cp ../cookbook/README.md src/pages/cookbook.mdx
 cp ../.github/CONTRIBUTING.md docs/contributing.md
+mkdir -p docs/templates
+cp ../templates/docs/INDEX.md docs/templates/index.md
 wget https://raw.githubusercontent.com/langchain-ai/langserve/main/README.md -O docs/langserve.md
-poetry run python scripts/generate_api_reference_links.py
-yarn install
-yarn start
+
+yarn
+
+quarto preview docs

docs/api_reference/create_api_rst.py

@@ -13,8 +13,10 @@ HERE = Path(__file__).parent
 
 PKG_DIR = ROOT_DIR / "libs" / "langchain" / "langchain"
 EXP_DIR = ROOT_DIR / "libs" / "experimental" / "langchain_experimental"
+CORE_DIR = ROOT_DIR / "libs" / "core" / "langchain_core"
 WRITE_FILE = HERE / "api_reference.rst"
 EXP_WRITE_FILE = HERE / "experimental_api_reference.rst"
+CORE_WRITE_FILE = HERE / "core_api_reference.rst"
 
 
 ClassKind = Literal["TypedDict", "Regular", "Pydantic", "enum"]
@@ -292,6 +294,17 @@ def _document_langchain_experimental() -> None:
 
 
 def _document_langchain_core() -> None:
+    """Document the langchain_core package."""
+    # Generate core_api_reference.rst
+    core_members = _load_package_modules(CORE_DIR)
+    core_doc = ".. _core_api_reference:\n\n" + _construct_doc(
+        "langchain_core", core_members
+    )
+    with open(CORE_WRITE_FILE, "w") as f:
+        f.write(core_doc)
+
+
+def _document_langchain() -> None:
     """Document the main langchain package."""
     # load top level module members
     lc_members = _load_package_modules(PKG_DIR)
@@ -306,7 +319,6 @@ def _document_langchain_core() -> None:
             "agents.output_parsers": agents["output_parsers"],
             "agents.format_scratchpad": agents["format_scratchpad"],
             "tools.render": tools["render"],
-            "schema.runnable": schema["runnable"],
         }
     )
 
@@ -318,8 +330,9 @@ def _document_langchain_core() -> None:
 
 def main() -> None:
     """Generate the reference.rst file for each package."""
-    _document_langchain_core()
+    _document_langchain()
     _document_langchain_experimental()
+    _document_langchain_core()
 
 
 if __name__ == "__main__":

docs/api_reference/requirements.txt

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

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

@@ -34,6 +34,9 @@
         <li class="nav-item">
           <a class="sk-nav-link nav-link" href="{{ pathto('api_reference') }}">API</a>
         </li>
+        <li class="nav-item">
+          <a class="sk-nav-link nav-link" href="{{ pathto('core_api_reference') }}">Core</a>
+        </li>
         <li class="nav-item">
           <a class="sk-nav-link nav-link" href="{{ pathto('experimental_api_reference') }}">Experimental</a>
         </li>

docs/docs/expression_language/cookbook/index.mdx

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 2
+sidebar_position: 3
 ---
 
 # Cookbook

docs/docs/expression_language/cookbook/multiple_chains.ipynb

@@ -146,7 +146,7 @@
    "source": [
     "### Branching and Merging\n",
     "\n",
-    "You may want the output of one component to be processed by 2 or more other components. [RunnableMaps](https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.base.RunnableMap.html) let you split or fork the chain so multiple components can process the input in parallel. Later, other components can join or merge the results to synthesize a final response. This type of chain creates a computation graph that looks like the following:\n",
+    "You may want the output of one component to be processed by 2 or more other components. [RunnableParallels](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.base.RunnableParallel.html#langchain_core.runnables.base.RunnableParallel) let you split or fork the chain so multiple components can process the input in parallel. Later, other components can join or merge the results to synthesize a final response. This type of chain creates a computation graph that looks like the following:\n",
     "\n",
     "```text\n",
     "     Input\n",

docs/docs/expression_language/cookbook/prompt_llm_parser.ipynb

@@ -317,7 +317,7 @@
    "source": [
     "## Simplifying input\n",
     "\n",
-    "To make invocation even simpler, we can add a `RunnableMap` to take care of creating the prompt input dict for us:"
+    "To make invocation even simpler, we can add a `RunnableParallel` to take care of creating the prompt input dict for us:"
    ]
   },
   {
@@ -327,9 +327,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from langchain.schema.runnable import RunnableMap, RunnablePassthrough\n",
+    "from langchain.schema.runnable import RunnableParallel, RunnablePassthrough\n",
     "\n",
-    "map_ = RunnableMap(foo=RunnablePassthrough())\n",
+    "map_ = RunnableParallel(foo=RunnablePassthrough())\n",
     "chain = (\n",
     "    map_\n",
     "    | prompt\n",

docs/docs/expression_language/cookbook/prompt_size.ipynb

@@ -209,7 +209,10 @@
    "id": "637f994a-5134-402a-bcf0-4de3911eaf49",
    "metadata": {},
    "source": [
-    ":::tip [LangSmith trace](https://smith.langchain.com/public/60909eae-f4f1-43eb-9f96-354f5176f66f/r)\n",
+    ":::tip\n",
+    "\n",
+    "[LangSmith trace](https://smith.langchain.com/public/60909eae-f4f1-43eb-9f96-354f5176f66f/r)\n",
+    "\n",
     ":::"
    ]
   },
@@ -374,7 +377,10 @@
    "id": "5a7e498b-dc68-4267-a35c-90ceffa91c46",
    "metadata": {},
    "source": [
-    ":::tip [LangSmith trace](https://smith.langchain.com/public/3b27d47f-e4df-4afb-81b1-0f88b80ca97e/r)\n",
+    ":::tip\n",
+    "\n",
+    "[LangSmith trace](https://smith.langchain.com/public/3b27d47f-e4df-4afb-81b1-0f88b80ca97e/r)\n",
+    "\n",
     ":::"
    ]
   }

docs/docs/expression_language/cookbook/retrieval.ipynb

@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 1,
    "id": "33be32af",
    "metadata": {},
    "outputs": [],
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "bfc47ec1",
    "metadata": {},
    "outputs": [],
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "eae31755",
    "metadata": {},
    "outputs": [],
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 4,
    "id": "f3040b0c",
    "metadata": {},
    "outputs": [
@@ -95,7 +95,7 @@
        "'Harrison worked at Kensho.'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -106,7 +106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "e1d20c7c",
    "metadata": {},
    "outputs": [],
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "7ee8b2d4",
    "metadata": {},
    "outputs": [
@@ -144,7 +144,7 @@
        "'Harrison ha lavorato a Kensho.'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -165,18 +165,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 21,
    "id": "3f30c348",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.schema import format_document\n",
-    "from langchain.schema.runnable import RunnableMap"
+    "from langchain.schema.messages import get_buffer_string\n",
+    "from langchain.schema.runnable import RunnableParallel\n",
+    "from langchain_core.messages import AIMessage, HumanMessage"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "64ab1dbf",
    "metadata": {},
    "outputs": [],
@@ -194,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "7d628c97",
    "metadata": {},
    "outputs": [],
@@ -209,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "f60a5d0f",
    "metadata": {},
    "outputs": [],
@@ -226,33 +228,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "7d007db6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from typing import List, Tuple\n",
-    "\n",
-    "\n",
-    "def _format_chat_history(chat_history: List[Tuple]) -> str:\n",
-    "    buffer = \"\"\n",
-    "    for dialogue_turn in chat_history:\n",
-    "        human = \"Human: \" + dialogue_turn[0]\n",
-    "        ai = \"Assistant: \" + dialogue_turn[1]\n",
-    "        buffer += \"\\n\" + \"\\n\".join([human, ai])\n",
-    "    return buffer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "id": "5c32cc89",
    "metadata": {},
    "outputs": [],
    "source": [
-    "_inputs = RunnableMap(\n",
+    "_inputs = RunnableParallel(\n",
     "    standalone_question=RunnablePassthrough.assign(\n",
-    "        chat_history=lambda x: _format_chat_history(x[\"chat_history\"])\n",
+    "        chat_history=lambda x: get_buffer_string(x[\"chat_history\"])\n",
     "    )\n",
     "    | CONDENSE_QUESTION_PROMPT\n",
     "    | ChatOpenAI(temperature=0)\n",
@@ -267,17 +250,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "id": "135c8205",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False)"
+       "AIMessage(content='Harrison was employed at Kensho.')"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -293,17 +276,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 22,
    "id": "424e7e7a",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='Harrison worked at Kensho.', additional_kwargs={}, example=False)"
+       "AIMessage(content='Harrison worked at Kensho.')"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -312,7 +295,10 @@
     "conversational_qa_chain.invoke(\n",
     "    {\n",
     "        \"question\": \"where did he work?\",\n",
-    "        \"chat_history\": [(\"Who wrote this notebook?\", \"Harrison\")],\n",
+    "        \"chat_history\": [\n",
+    "            HumanMessage(content=\"Who wrote this notebook?\"),\n",
+    "            AIMessage(content=\"Harrison\"),\n",
+    "        ],\n",
     "    }\n",
     ")"
    ]
@@ -329,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "id": "e31dd17c",
    "metadata": {},
    "outputs": [],
@@ -341,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "id": "d4bffe94",
    "metadata": {},
    "outputs": [],
@@ -353,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "id": "733be985",
    "metadata": {},
    "outputs": [],
@@ -367,7 +353,7 @@
     "standalone_question = {\n",
     "    \"standalone_question\": {\n",
     "        \"question\": lambda x: x[\"question\"],\n",
-    "        \"chat_history\": lambda x: _format_chat_history(x[\"chat_history\"]),\n",
+    "        \"chat_history\": lambda x: get_buffer_string(x[\"chat_history\"]),\n",
     "    }\n",
     "    | CONDENSE_QUESTION_PROMPT\n",
     "    | ChatOpenAI(temperature=0)\n",
@@ -394,18 +380,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "id": "806e390c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'answer': AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False),\n",
-       " 'docs': [Document(page_content='harrison worked at kensho', metadata={})]}"
+       "{'answer': AIMessage(content='Harrison was employed at Kensho.'),\n",
+       " 'docs': [Document(page_content='harrison worked at kensho')]}"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -418,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "id": "977399fd",
    "metadata": {},
    "outputs": [],
@@ -431,18 +417,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "id": "f94f7de4",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'history': [HumanMessage(content='where did harrison work?', additional_kwargs={}, example=False),\n",
-       "  AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False)]}"
+       "{'history': [HumanMessage(content='where did harrison work?'),\n",
+       "  AIMessage(content='Harrison was employed at Kensho.')]}"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -450,6 +436,38 @@
    "source": [
     "memory.load_memory_variables({})"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "88f2b7cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'answer': AIMessage(content='Harrison actually worked at Kensho.'),\n",
+       " 'docs': [Document(page_content='harrison worked at kensho')]}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "inputs = {\"question\": \"but where did he really work?\"}\n",
+    "result = final_chain.invoke(inputs)\n",
+    "result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "207a2782",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -468,7 +486,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.1"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/get_started.ipynb

@@ -0,0 +1,493 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "366a0e68-fd67-4fe5-a292-5c33733339ea",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 0\n",
+    "title: Get started\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "befa7fd1",
+   "metadata": {},
+   "source": [
+    "LCEL makes it easy to build complex chains from basic components, and supports out of the box functionality such as streaming, parallelism, and logging."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a9acd2e",
+   "metadata": {},
+   "source": [
+    "## Basic example: prompt + model + output parser\n",
+    "\n",
+    "The most basic and common use case is chaining a prompt template and a model together. To see how this works, let's create a chain that takes a topic and generates a joke:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "466b65b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Why did the ice cream go to therapy?\\n\\nBecause it had too many toppings and couldn't find its cone-fidence!\""
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "\n",
+    "prompt = ChatPromptTemplate.from_template(\"tell me a short joke about {topic}\")\n",
+    "model = ChatOpenAI()\n",
+    "output_parser = StrOutputParser()\n",
+    "\n",
+    "chain = prompt | model | output_parser\n",
+    "\n",
+    "chain.invoke({\"topic\": \"ice cream\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81c502c5-85ee-4f36-aaf4-d6e350b7792f",
+   "metadata": {},
+   "source": [
+    "Notice this line of this code, where we piece together then different components into a single chain using LCEL:\n",
+    "\n",
+    "```\n",
+    "chain = prompt | model | output_parser\n",
+    "```\n",
+    "\n",
+    "The `|` symbol is similar to a [unix pipe operator](https://en.wikipedia.org/wiki/Pipeline_(Unix)), which chains together the different components feeds the output from one component as input into the next component. \n",
+    "\n",
+    "In this chain the user input is passed to the prompt template, then the prompt template output is passed to the model, then the model output is passed to the output parser. Let's take a look at each component individually to really understand what's going on. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa1b77fa",
+   "metadata": {},
+   "source": [
+    "### 1. Prompt\n",
+    "\n",
+    "`prompt` is a `BasePromptTemplate`, which means it takes in a dictionary of template variables and produces a `PromptValue`. A `PromptValue` is a wrapper around a completed prompt that can be passed to either an `LLM` (which takes a string as input) or `ChatModel` (which takes a sequence of messages as input). It can work with either language model type because it defines logic both for producing `BaseMessage`s and for producing a string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b8656990",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ChatPromptValue(messages=[HumanMessage(content='tell me a short joke about ice cream')])"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prompt_value = prompt.invoke({\"topic\": \"ice cream\"})\n",
+    "prompt_value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "e6034488",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[HumanMessage(content='tell me a short joke about ice cream')]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prompt_value.to_messages()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "60565463",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Human: tell me a short joke about ice cream'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prompt_value.to_string()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "577f0f76",
+   "metadata": {},
+   "source": [
+    "### 2. Model\n",
+    "\n",
+    "The `PromptValue` is then passed to `model`. In this case our `model` is a `ChatModel`, meaning it will output a `BaseMessage`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "33cf5f72",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=\"Why did the ice cream go to therapy? \\n\\nBecause it had too many toppings and couldn't find its cone-fidence!\")"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "message = model.invoke(prompt_value)\n",
+    "message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "327e7db8",
+   "metadata": {},
+   "source": [
+    "If our `model` was an `LLM`, it would output a string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8feb05da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n\\nRobot: Why did the ice cream go to therapy? Because it had a rocky road.'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.llms import OpenAI\n",
+    "\n",
+    "llm = OpenAI(model=\"gpt-3.5-turbo-instruct\")\n",
+    "llm.invoke(prompt_value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91847478",
+   "metadata": {},
+   "source": [
+    "### 3. Output parser\n",
+    "\n",
+    "And lastly we pass our `model` output to the `output_parser`, which is a `BaseOutputParser` meaning it takes either a string or a \n",
+    "`BaseMessage` as input. The `StrOutputParser` specifically simple converts any input into a string."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "533e59a8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Why did the ice cream go to therapy? \\n\\nBecause it had too many toppings and couldn't find its cone-fidence!\""
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output_parser.invoke(message)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9851e842",
+   "metadata": {},
+   "source": [
+    "### 4. Entire Pipeline\n",
+    "\n",
+    "To follow the steps along:\n",
+    "\n",
+    "1. We pass in user input on the desired topic as `{\"topic\": \"ice cream\"}`\n",
+    "2. The `prompt` component takes the user input, which is then used to construct a PromptValue after using the `topic` to construct the prompt. \n",
+    "3. The `model` component takes the generated prompt, and passes into the OpenAI LLM model for evaluation. The generated output from the model is a `ChatMessage` object. \n",
+    "4. Finally, the `output_parser` component takes in a `ChatMessage`, and transforms this into a Python string, which is returned from the invoke method. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4873109",
+   "metadata": {},
+   "source": [
+    "```mermaid\n",
+    "graph LR\n",
+    "    A(Input: topic=ice cream) --> |Dict| B(PromptTemplate)\n",
+    "    B -->|PromptValue| C(ChatModel)    \n",
+    "    C -->|ChatMessage| D(StrOutputParser)\n",
+    "    D --> |String| F(Result)\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fe63534d",
+   "metadata": {},
+   "source": [
+    ":::info\n",
+    "\n",
+    "Note that if you’re curious about the output of any components, you can always test out a smaller version of the chain such as `prompt`  or `prompt | model` to see the intermediate results:\n",
+    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11089b6f-23f8-474f-97ec-8cae8d0ca6d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "input = {\"topic\": \"ice cream\"}\n",
+    "\n",
+    "prompt.invoke(input)\n",
+    "# > ChatPromptValue(messages=[HumanMessage(content='tell me a short joke about ice cream')])\n",
+    "\n",
+    "(prompt | model).invoke(input)\n",
+    "# > AIMessage(content=\"Why did the ice cream go to therapy?\\nBecause it had too many toppings and couldn't cone-trol itself!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc7d3b9d-e400-4c9b-9188-f29dac73e6bb",
+   "metadata": {},
+   "source": [
+    "## RAG Search Example\n",
+    "\n",
+    "For our next example, we want to run a retrieval-augmented generation chain to add some context when responding to questions. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "662426e8-4316-41dc-8312-9b58edc7e0c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Requires:\n",
+    "# pip install langchain docarray\n",
+    "\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.schema.runnable import RunnableParallel, RunnablePassthrough\n",
+    "from langchain.vectorstores import DocArrayInMemorySearch\n",
+    "\n",
+    "vectorstore = DocArrayInMemorySearch.from_texts(\n",
+    "    [\"harrison worked at kensho\", \"bears like to eat honey\"],\n",
+    "    embedding=OpenAIEmbeddings(),\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "template = \"\"\"Answer the question based only on the following context:\n",
+    "{context}\n",
+    "\n",
+    "Question: {question}\n",
+    "\"\"\"\n",
+    "prompt = ChatPromptTemplate.from_template(template)\n",
+    "model = ChatOpenAI()\n",
+    "output_parser = StrOutputParser()\n",
+    "\n",
+    "setup_and_retrieval = RunnableParallel(\n",
+    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
+    ")\n",
+    "chain = setup_and_retrieval | prompt | model | output_parser\n",
+    "\n",
+    "chain.invoke(\"where did harrison work?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f0999140-6001-423b-970b-adf1dfdb4dec",
+   "metadata": {},
+   "source": [
+    "In this case, the composed chain is: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b88e9bb-f04a-4a56-87ec-19a0e6350763",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain = setup_and_retrieval | prompt | model | output_parser"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e929e15-40a5-4569-8969-384f636cab87",
+   "metadata": {},
+   "source": [
+    "To explain this, we first can see that the prompt template above takes in `context` and `question` as values to be substituted in the prompt. Before building the prompt template, we want to retrieve relevant documents to the search and include them as part of the context. \n",
+    "\n",
+    "As a preliminary step, we’ve setup the retriever using an in memory store, which can retrieve documents based on a query. This is a runnable component as well that can be chained together with other components, but you can also try to run it separately:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7319ef6-613b-4638-ad7d-4a2183702c1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "retriever.invoke(\"where did harrison work?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6833844-f1c4-444c-a3d2-31b3c6b31d46",
+   "metadata": {},
+   "source": [
+    "We then use the `RunnableParallel` to prepare the expected inputs into the prompt by using the entries for the retrieved documents as well as the original user question, using the retriever for document search, and RunnablePassthrough to pass the user’s question:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dcbca26b-d6b9-4c24-806c-1ec8fdaab4ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "setup_and_retrieval = RunnableParallel(\n",
+    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68c721c1-048b-4a64-9d78-df54fe465992",
+   "metadata": {},
+   "source": [
+    "To review, the complete chain is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d5115a7-7b8e-458b-b936-26cc87ee81c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "setup_and_retrieval = RunnableParallel(\n",
+    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
+    ")\n",
+    "chain = setup_and_retrieval | prompt | model | output_parser"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c6f5f74-b387-48a0-bedd-1fae202cd10a",
+   "metadata": {},
+   "source": [
+    "With the flow being:\n",
+    "\n",
+    "1. The first steps create a `RunnableParallel` object with two entries.  The first entry, `context` will include the document results fetched by the retriever. The second entry, `question` will contain the user’s original question. To pass on the question, we use `RunnablePassthrough` to copy this entry. \n",
+    "2. Feed the dictionary from the step above to the `prompt` component. It then takes the user input which is `question` as well as the retrieved document which is `context` to construct a prompt and output a PromptValue.  \n",
+    "3. The `model` component takes the generated prompt, and passes into the OpenAI LLM model for evaluation. The generated output from the model is a `ChatMessage` object. \n",
+    "4. Finally, the `output_parser` component takes in a `ChatMessage`, and transforms this into a Python string, which is returned from the invoke method.\n",
+    "\n",
+    "```mermaid\n",
+    "graph LR\n",
+    "    A(Question) --> B(RunnableParallel)\n",
+    "    B -->|Question| C(Retriever)\n",
+    "    B -->|Question| D(RunnablePassThrough)\n",
+    "    C -->|context=retrieved docs| E(PromptTemplate)\n",
+    "    D -->|question=Question| E\n",
+    "    E -->|PromptValue| F(ChatModel)    \n",
+    "    F -->|ChatMessage| G(StrOutputParser)\n",
+    "    G --> |String| H(Result)\n",
+    "```\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8c2438df-164e-4bbe-b5f4-461695e45b0f",
+   "metadata": {},
+   "source": [
+    "## Next steps\n",
+    "\n",
+    "We recommend reading our [Why use LCEL](/docs/expression_language/why) section next to see a side-by-side comparison of the code needed to produce common functionality with and without LCEL."
+   ]
+  }
+ ],
+ "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/docs/expression_language/how_to/configure.ipynb

@@ -43,6 +43,7 @@
    "source": [
     "from langchain.chat_models import ChatOpenAI\n",
     "from langchain.prompts import PromptTemplate\n",
+    "from langchain.schema.runnable import ConfigurableField\n",
     "\n",
     "model = ChatOpenAI(temperature=0).configurable_fields(\n",
     "    temperature=ConfigurableField(\n",
@@ -594,7 +595,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/fallbacks.ipynb

@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "d3e893bf",
    "metadata": {},
    "outputs": [],
@@ -44,19 +44,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "dfdd8bf5",
    "metadata": {},
    "outputs": [],
    "source": [
     "from unittest.mock import patch\n",
     "\n",
-    "from openai.error import RateLimitError"
+    "import httpx\n",
+    "from openai import RateLimitError\n",
+    "\n",
+    "request = httpx.Request(\"GET\", \"/\")\n",
+    "response = httpx.Response(200, request=request)\n",
+    "error = RateLimitError(\"rate limit\", response=response, body=\"\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "e6fdffc1",
    "metadata": {},
    "outputs": [],
@@ -69,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 4,
    "id": "584461ab",
    "metadata": {},
    "outputs": [
@@ -83,7 +88,7 @@
    ],
    "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",
+    "with patch(\"openai.resources.chat.completions.Completions.create\", side_effect=error):\n",
     "    try:\n",
     "        print(openai_llm.invoke(\"Why did the chicken cross the road?\"))\n",
     "    except:\n",
@@ -106,7 +111,7 @@
    ],
    "source": [
     "# Now let's try with fallbacks to Anthropic\n",
-    "with patch(\"openai.ChatCompletion.create\", side_effect=RateLimitError()):\n",
+    "with patch(\"openai.resources.chat.completions.Completions.create\", side_effect=error):\n",
     "    try:\n",
     "        print(llm.invoke(\"Why did the chicken cross the road?\"))\n",
     "    except:\n",
@@ -148,7 +153,7 @@
     "    ]\n",
     ")\n",
     "chain = prompt | llm\n",
-    "with patch(\"openai.ChatCompletion.create\", side_effect=RateLimitError()):\n",
+    "with patch(\"openai.resources.chat.completions.Completions.create\", side_effect=error):\n",
     "    try:\n",
     "        print(chain.invoke({\"animal\": \"kangaroo\"}))\n",
     "    except:\n",
@@ -185,7 +190,7 @@
     ")\n",
     "\n",
     "chain = prompt | llm\n",
-    "with patch(\"openai.ChatCompletion.create\", side_effect=RateLimitError()):\n",
+    "with patch(\"openai.ChatCompletion.create\", side_effect=error):\n",
     "    try:\n",
     "        print(chain.invoke({\"animal\": \"kangaroo\"}))\n",
     "    except:\n",
@@ -286,7 +291,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/functions.ipynb

@@ -1,5 +1,16 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 2\n",
+    "title: \"RunnableLambda: Run Custom Functions\"\n",
+    "keywords: [RunnableLambda, LCEL]\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "fbc4bf6e",
@@ -7,14 +18,14 @@
    "source": [
     "# Run custom functions\n",
     "\n",
-    "You can use arbitrary functions in the pipeline\n",
+    "You can use arbitrary functions in the pipeline.\n",
     "\n",
     "Note that all inputs to these functions need to be a SINGLE argument. If you have a function that accepts multiple arguments, you should write a wrapper that accepts a single input and unpacks it into multiple argument."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "6bb221b3",
    "metadata": {},
    "outputs": [],
@@ -56,17 +67,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "5488ec85",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='3 + 9 equals 12.', additional_kwargs={}, example=False)"
+       "AIMessage(content='3 + 9 equals 12.')"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -82,12 +93,12 @@
    "source": [
     "## Accepting a Runnable Config\n",
     "\n",
-    "Runnable lambdas can optionally accept a [RunnableConfig](https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.config.RunnableConfig.html?highlight=runnableconfig#langchain.schema.runnable.config.RunnableConfig), which they can use to pass callbacks, tags, and other configuration information to nested runs."
+    "Runnable lambdas can optionally accept a [RunnableConfig](https://api.python.langchain.com/en/latest/runnables/langchain_core.runnables.config.RunnableConfig.html#langchain_core.runnables.config.RunnableConfig), which they can use to pass callbacks, tags, and other configuration information to nested runs."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "id": "80b3b5f6-5d58-44b9-807e-cce9a46bf49f",
    "metadata": {},
    "outputs": [],
@@ -98,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 4,
    "id": "ff0daf0c-49dd-4d21-9772-e5fa133c5f36",
    "metadata": {},
    "outputs": [],
@@ -125,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 5,
    "id": "1a5e709e-9d75-48c7-bb9c-503251990505",
    "metadata": {},
    "outputs": [
@@ -133,6 +144,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "{'foo': 'bar'}\n",
       "Tokens Used: 65\n",
       "\tPrompt Tokens: 56\n",
       "\tCompletion Tokens: 9\n",
@@ -145,9 +157,10 @@
     "from langchain.callbacks import get_openai_callback\n",
     "\n",
     "with get_openai_callback() as cb:\n",
-    "    RunnableLambda(parse_or_fix).invoke(\n",
+    "    output = RunnableLambda(parse_or_fix).invoke(\n",
     "        \"{foo: bar}\", {\"tags\": [\"my-tag\"], \"callbacks\": [cb]}\n",
     "    )\n",
+    "    print(output)\n",
     "    print(cb)"
    ]
   },
@@ -176,7 +189,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/generators.ipynb

@@ -17,6 +17,13 @@
     "Let's implement a custom output parser for comma-separated lists."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sync version"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -57,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -66,7 +73,7 @@
        "'lion, tiger, wolf, gorilla, panda'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -152,12 +159,81 @@
     "list_chain.invoke({\"animal\": \"bear\"})"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Async version"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "from typing import AsyncIterator\n",
+    "\n",
+    "\n",
+    "async def asplit_into_list(\n",
+    "    input: AsyncIterator[str]\n",
+    ") -> AsyncIterator[List[str]]:  # async def\n",
+    "    buffer = \"\"\n",
+    "    async for (\n",
+    "        chunk\n",
+    "    ) in input:  # `input` is a `async_generator` object, so use `async for`\n",
+    "        buffer += chunk\n",
+    "        while \",\" in buffer:\n",
+    "            comma_index = buffer.index(\",\")\n",
+    "            yield [buffer[:comma_index].strip()]\n",
+    "            buffer = buffer[comma_index + 1 :]\n",
+    "    yield [buffer.strip()]\n",
+    "\n",
+    "\n",
+    "list_chain = str_chain | asplit_into_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['lion']\n",
+      "['tiger']\n",
+      "['wolf']\n",
+      "['gorilla']\n",
+      "['panda']\n"
+     ]
+    }
+   ],
+   "source": [
+    "async for chunk in list_chain.astream({\"animal\": \"bear\"}):\n",
+    "    print(chunk, flush=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['lion', 'tiger', 'wolf', 'gorilla', 'panda']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "await list_chain.ainvoke({\"animal\": \"bear\"})"
+   ]
   }
  ],
  "metadata": {
@@ -176,7 +252,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/index.mdx

@@ -1,5 +1,5 @@
 ---
-sidebar_position: 1
+sidebar_position: 2
 ---
 
 # How to

docs/docs/expression_language/how_to/map.ipynb

@@ -1,29 +1,192 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e2596041-9b76-4e74-836f-e6235086bbf0",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 0\n",
+    "title: \"RunnableParallel: Manipulating data\"\n",
+    "keywords: [RunnableParallel, RunnableMap, LCEL]\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "b022ab74-794d-4c54-ad47-ff9549ddb9d2",
    "metadata": {},
    "source": [
-    "# Parallelize steps\n",
+    "# Manipulating inputs & output\n",
+    "\n",
+    "RunnableParallel can be useful for manipulating the output of one Runnable to match the input format of the next Runnable in a sequence.\n",
+    "\n",
+    "Here the input to prompt is expected to be a map with keys \"context\" and \"question\". The user input is just the question. So we need to get the context using our retriever and passthrough the user input under the \"question\" key.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "267d1460-53c1-4fdb-b2c3-b6a1eb7fccff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Harrison worked at Kensho.'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain.vectorstores import FAISS\n",
+    "\n",
+    "vectorstore = FAISS.from_texts(\n",
+    "    [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "template = \"\"\"Answer the question based only on the following context:\n",
+    "{context}\n",
+    "\n",
+    "Question: {question}\n",
+    "\"\"\"\n",
+    "prompt = ChatPromptTemplate.from_template(template)\n",
+    "model = ChatOpenAI()\n",
+    "\n",
+    "retrieval_chain = (\n",
+    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
+    "    | prompt\n",
+    "    | model\n",
+    "    | StrOutputParser()\n",
+    ")\n",
+    "\n",
+    "retrieval_chain.invoke(\"where did harrison work?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "392cd4c4-e7ed-4ab8-934d-f7a4eca55ee1",
+   "metadata": {},
+   "source": [
+    "::: {.callout-tip}\n",
+    "Note that when composing a RunnableParallel with another Runnable we don't even need to wrap our dictionary in the RunnableParallel class — the type conversion is handled for us. In the context of a chain, these are equivalent:\n",
+    ":::\n",
+    "\n",
+    "```\n",
+    "{\"context\": retriever, \"question\": RunnablePassthrough()}\n",
+    "```\n",
+    "\n",
+    "```\n",
+    "RunnableParallel({\"context\": retriever, \"question\": RunnablePassthrough()})\n",
+    "```\n",
+    "\n",
+    "```\n",
+    "RunnableParallel(context=retriever, question=RunnablePassthrough())\n",
+    "```\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c1b8baa-3a80-44f0-bb79-d22f79815d3d",
+   "metadata": {},
+   "source": [
+    "## Using itemgetter as shorthand\n",
+    "\n",
+    "Note that you can use Python's `itemgetter` as shorthand to extract data from the map when combining with `RunnableParallel`. You can find more information about itemgetter in the [Python Documentation](https://docs.python.org/3/library/operator.html#operator.itemgetter). \n",
+    "\n",
+    "In the example below, we use itemgetter to extract specific keys from the map:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "84fc49e1-2daf-4700-ae33-a0a6ed47d5f6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Harrison ha lavorato a Kensho.'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from operator import itemgetter\n",
+    "\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain.vectorstores import FAISS\n",
+    "\n",
+    "vectorstore = FAISS.from_texts(\n",
+    "    [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "\n",
+    "template = \"\"\"Answer the question based only on the following context:\n",
+    "{context}\n",
+    "\n",
+    "Question: {question}\n",
+    "\n",
+    "Answer in the following language: {language}\n",
+    "\"\"\"\n",
+    "prompt = ChatPromptTemplate.from_template(template)\n",
+    "\n",
+    "chain = (\n",
+    "    {\n",
+    "        \"context\": itemgetter(\"question\") | retriever,\n",
+    "        \"question\": itemgetter(\"question\"),\n",
+    "        \"language\": itemgetter(\"language\"),\n",
+    "    }\n",
+    "    | prompt\n",
+    "    | model\n",
+    "    | StrOutputParser()\n",
+    ")\n",
+    "\n",
+    "chain.invoke({\"question\": \"where did harrison work\", \"language\": \"italian\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc2f9847-39aa-4fe4-9049-3a8969bc4bce",
+   "metadata": {},
+   "source": [
+    "## Parallelize steps\n",
     "\n",
     "RunnableParallel (aka. RunnableMap) makes it easy to execute multiple Runnables in parallel, and to return the output of these Runnables as a map."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "id": "7e1873d6-d4b6-43ac-96a1-edcf178201e0",
+   "execution_count": 1,
+   "id": "31f18442-f837-463f-bef4-8729368f5f8b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'joke': AIMessage(content=\"Why don't bears wear shoes? \\n\\nBecause they have bear feet!\", additional_kwargs={}, example=False),\n",
-       " 'poem': AIMessage(content=\"In woodland depths, bear prowls with might,\\nSilent strength, nature's sovereign, day and night.\", additional_kwargs={}, example=False)}"
+       "{'joke': AIMessage(content=\"Why don't bears wear shoes?\\n\\nBecause they have bear feet!\"),\n",
+       " 'poem': AIMessage(content=\"In the wild's embrace, bear roams free,\\nStrength and grace, a majestic decree.\")}"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -44,69 +207,6 @@
     "map_chain.invoke({\"topic\": \"bear\"})"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "df867ae9-1cec-4c9e-9fef-21969b206af5",
-   "metadata": {},
-   "source": [
-    "## Manipulating outputs/inputs\n",
-    "Maps can be useful for manipulating the output of one Runnable to match the input format of the next Runnable in a sequence."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "267d1460-53c1-4fdb-b2c3-b6a1eb7fccff",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Harrison worked at Kensho.'"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnablePassthrough\n",
-    "from langchain.vectorstores import FAISS\n",
-    "\n",
-    "vectorstore = FAISS.from_texts(\n",
-    "    [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",
-    ")\n",
-    "retriever = vectorstore.as_retriever()\n",
-    "template = \"\"\"Answer the question based only on the following context:\n",
-    "{context}\n",
-    "\n",
-    "Question: {question}\n",
-    "\"\"\"\n",
-    "prompt = ChatPromptTemplate.from_template(template)\n",
-    "\n",
-    "retrieval_chain = (\n",
-    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
-    "    | prompt\n",
-    "    | model\n",
-    "    | StrOutputParser()\n",
-    ")\n",
-    "\n",
-    "retrieval_chain.invoke(\"where did harrison work?\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "392cd4c4-e7ed-4ab8-934d-f7a4eca55ee1",
-   "metadata": {},
-   "source": [
-    "Here the input to prompt is expected to be a map with keys \"context\" and \"question\". The user input is just the question. So we need to get the context using our retriever and passthrough the user input under the \"question\" key.\n",
-    "\n",
-    "Note that when composing a RunnableMap when another Runnable we don't even need to wrap our dictionary in the RunnableMap class — the type conversion is handled for us."
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "833da249-c0d4-4e5b-b3f8-cab549f0f7e1",
@@ -114,7 +214,7 @@
    "source": [
     "## Parallelism\n",
     "\n",
-    "RunnableMaps are also useful for running independent processes in parallel, since each Runnable in the map is executed in parallel. For example, we can see our earlier `joke_chain`, `poem_chain` and `map_chain` all have about the same runtime, even though `map_chain` executes both of the other two."
+    "RunnableParallel are also useful for running independent processes in parallel, since each Runnable in the map is executed in parallel. For example, we can see our earlier `joke_chain`, `poem_chain` and `map_chain` all have about the same runtime, even though `map_chain` executes both of the other two."
    ]
   },
   {
@@ -194,7 +294,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/message_history.ipynb

@@ -0,0 +1,402 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6a4becbd-238e-4c1d-a02d-08e61fbc3763",
+   "metadata": {},
+   "source": [
+    "# Add message history (memory)\n",
+    "\n",
+    "The `RunnableWithMessageHistory` let's us add message history to certain types of chains.\n",
+    "\n",
+    "Specifically, it can be used for any Runnable that takes as input one of\n",
+    "* a sequence of `BaseMessage`\n",
+    "* a dict with a key that takes a sequence of `BaseMessage`\n",
+    "* a dict with a key that takes the latest message(s) as a string or sequence of `BaseMessage`, and a separate key that takes historical messages\n",
+    "\n",
+    "And returns as output one of\n",
+    "* a string that can be treated as the contents of an `AIMessage`\n",
+    "* a sequence of `BaseMessage`\n",
+    "* a dict with a key that contains a sequence of `BaseMessage`\n",
+    "\n",
+    "Let's take a look at some examples to see how it works."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6bca45e5-35d9-4603-9ca9-6ac0ce0e35cd",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "\n",
+    "We'll use Redis to store our chat message histories and Anthropic's claude-2 model so we'll need to install the following dependencies:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "477d04b3-c2b6-4ba5-962f-492c0d625cd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -U langchain redis anthropic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93776323-d6b8-4912-bb6a-867c5e655f46",
+   "metadata": {},
+   "source": [
+    "Set your [Anthropic API  key](https://console.anthropic.com/):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7f56f69-d2f1-4a21-990c-b5551eb012fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import getpass\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"ANTHROPIC_API_KEY\"] = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a0ec9e0-7b1c-4c6f-b570-e61d520b47c6",
+   "metadata": {},
+   "source": [
+    "Start a local Redis Stack server if we don't have an existing Redis deployment to connect to:\n",
+    "```bash\n",
+    "docker run -d -p 6379:6379 -p 8001:8001 redis/redis-stack:latest\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cd6a250e-17fe-4368-a39d-1fe6b2cbde68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "REDIS_URL = \"redis://localhost:6379/0\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "36f43b87-655c-4f64-aa7b-bd8c1955d8e5",
+   "metadata": {},
+   "source": [
+    "### [LangSmith](/docs/langsmith)\n",
+    "\n",
+    "LangSmith is especially useful for something like message history injection, where it can be hard to otherwise understand what the inputs are to various parts of the chain.\n",
+    "\n",
+    "Note that LangSmith is not needed, but it is helpful.\n",
+    "If you do want to use LangSmith, after you sign up at the link above, make sure to uncoment the below and set your environment variables to start logging traces:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2afc1556-8da1-4499-ba11-983b66c58b18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
+    "# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1a5a632e-ba9e-4488-b586-640ad5494f62",
+   "metadata": {},
+   "source": [
+    "## Example: Dict input, message output\n",
+    "\n",
+    "Let's create a simple chain that takes a dict as input and returns a BaseMessage.\n",
+    "\n",
+    "In this case the `\"question\"` key in the input represents our input message, and the `\"history\"` key is where our historical messages will be injected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "2a150d6f-8878-4950-8634-a608c5faad56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Optional\n",
+    "\n",
+    "from langchain.chat_models import ChatAnthropic\n",
+    "from langchain.memory.chat_message_histories import RedisChatMessageHistory\n",
+    "from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder\n",
+    "from langchain.schema.chat_history import BaseChatMessageHistory\n",
+    "from langchain.schema.runnable.history import RunnableWithMessageHistory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "3185edba-4eb6-4b32-80c6-577c0d19af97",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "prompt = ChatPromptTemplate.from_messages(\n",
+    "    [\n",
+    "        (\"system\", \"You're an assistant who's good at {ability}\"),\n",
+    "        MessagesPlaceholder(variable_name=\"history\"),\n",
+    "        (\"human\", \"{question}\"),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "chain = prompt | ChatAnthropic(model=\"claude-2\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9d81796-ce61-484c-89e2-6c567d5e54ef",
+   "metadata": {},
+   "source": [
+    "### Adding message history\n",
+    "\n",
+    "To add message history to our original chain we wrap it in the `RunnableWithMessageHistory` class.\n",
+    "\n",
+    "Crucially, we also need to  define a method that takes a session_id string and based on it returns a `BaseChatMessageHistory`. Given the same input, this method should return an equivalent output.\n",
+    "\n",
+    "In this case we'll also want to specify `input_messages_key` (the key to be treated as the latest input message) and `history_messages_key` (the key to add historical messages to)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ca7c64d8-e138-4ef8-9734-f82076c47d80",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chain_with_history = RunnableWithMessageHistory(\n",
+    "    chain,\n",
+    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
+    "    input_messages_key=\"question\",\n",
+    "    history_messages_key=\"history\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37eefdec-9901-4650-b64c-d3c097ed5f4d",
+   "metadata": {},
+   "source": [
+    "## Invoking with config\n",
+    "\n",
+    "Whenever we call our chain with message history, we need to include a config that contains the `session_id`\n",
+    "```python\n",
+    "config={\"configurable\": {\"session_id\": \"<SESSION_ID>\"}}\n",
+    "```\n",
+    "\n",
+    "Given the same configuration, our chain should be pulling from the same chat message history."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a85bcc22-ca4c-4ad5-9440-f94be7318f3e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=' Cosine is one of the basic trigonometric functions in mathematics. It is defined as the ratio of the adjacent side to the hypotenuse in a right triangle.\\n\\nSome key properties and facts about cosine:\\n\\n- It is denoted by cos(θ), where θ is the angle in a right triangle. \\n\\n- The cosine of an acute angle is always positive. For angles greater than 90 degrees, cosine can be negative.\\n\\n- Cosine is one of the three main trig functions along with sine and tangent.\\n\\n- The cosine of 0 degrees is 1. As the angle increases towards 90 degrees, the cosine value decreases towards 0.\\n\\n- The range of values for cosine is -1 to 1.\\n\\n- The cosine function maps angles in a circle to the x-coordinate on the unit circle.\\n\\n- Cosine is used to find adjacent side lengths in right triangles, and has many other applications in mathematics, physics, engineering and more.\\n\\n- Key cosine identities include: cos(A+B) = cosAcosB − sinAsinB and cos(2A) = cos^2(A) − sin^2(A)\\n\\nSo in summary, cosine is a fundamental trig')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain_with_history.invoke(\n",
+    "    {\"ability\": \"math\", \"question\": \"What does cosine mean?\"},\n",
+    "    config={\"configurable\": {\"session_id\": \"foobar\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ab29abd3-751f-41ce-a1b0-53f6b565e79d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content=' The inverse of the cosine function is called the arccosine or inverse cosine, often denoted as cos-1(x) or arccos(x).\\n\\nThe key properties and facts about arccosine:\\n\\n- It is defined as the angle θ between 0 and π radians whose cosine is x. So arccos(x) = θ such that cos(θ) = x.\\n\\n- The range of arccosine is 0 to π radians (0 to 180 degrees).\\n\\n- The domain of arccosine is -1 to 1. \\n\\n- arccos(cos(θ)) = θ for values of θ from 0 to π radians.\\n\\n- arccos(x) is the angle in a right triangle whose adjacent side is x and hypotenuse is 1.\\n\\n- arccos(0) = 90 degrees. As x increases from 0 to 1, arccos(x) decreases from 90 to 0 degrees.\\n\\n- arccos(1) = 0 degrees. arccos(-1) = 180 degrees.\\n\\n- The graph of y = arccos(x) is part of the unit circle, restricted to x')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain_with_history.invoke(\n",
+    "    {\"ability\": \"math\", \"question\": \"What's its inverse\"},\n",
+    "    config={\"configurable\": {\"session_id\": \"foobar\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da3d1feb-b4bb-4624-961c-7db2e1180df7",
+   "metadata": {},
+   "source": [
+    ":::tip\n",
+    "\n",
+    "[Langsmith trace](https://smith.langchain.com/public/863a003b-7ca8-4b24-be9e-d63ec13c106e/r)\n",
+    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61d5115e-64a1-4ad5-b676-8afd4ef6093e",
+   "metadata": {},
+   "source": [
+    "Looking at the Langsmith trace for the second call, we can see that when constructing the prompt, a \"history\" variable has been injected which is a list of two messages (our first input and first output)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "028cf151-6cd5-4533-b3cf-c8d735554647",
+   "metadata": {},
+   "source": [
+    "## Example: messages input, dict output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "0bb446b5-6251-45fe-a92a-4c6171473c53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output_message': AIMessage(content=' Here is a summary of Simone de Beauvoir\\'s views on free will:\\n\\n- De Beauvoir was an existentialist philosopher and believed strongly in the concept of free will. She rejected the idea that human nature or instincts determine behavior.\\n\\n- Instead, de Beauvoir argued that human beings define their own essence or nature through their actions and choices. As she famously wrote, \"One is not born, but rather becomes, a woman.\"\\n\\n- De Beauvoir believed that while individuals are situated in certain cultural contexts and social conditions, they still have agency and the ability to transcend these situations. Freedom comes from choosing one\\'s attitude toward these constraints.\\n\\n- She emphasized the radical freedom and responsibility of the individual. We are \"condemned to be free\" because we cannot escape making choices and taking responsibility for our choices. \\n\\n- De Beauvoir felt that many people evade their freedom and responsibility by adopting rigid mindsets, ideologies, or conforming uncritically to social roles.\\n\\n- She advocated for the recognition of ambiguity in the human condition and warned against the quest for absolute rules that deny freedom and responsibility. Authentic living involves embracing ambiguity.\\n\\nIn summary, de Beauvoir promoted an existential ethics')}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.schema.messages import HumanMessage\n",
+    "from langchain.schema.runnable import RunnableParallel\n",
+    "\n",
+    "chain = RunnableParallel({\"output_message\": ChatAnthropic(model=\"claude-2\")})\n",
+    "chain_with_history = RunnableWithMessageHistory(\n",
+    "    chain,\n",
+    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
+    "    output_messages_key=\"output_message\",\n",
+    ")\n",
+    "\n",
+    "chain_with_history.invoke(\n",
+    "    [HumanMessage(content=\"What did Simone de Beauvoir believe about free will\")],\n",
+    "    config={\"configurable\": {\"session_id\": \"baz\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "601ce3ff-aea8-424d-8e54-fd614256af4f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'output_message': AIMessage(content=\" There are many similarities between Simone de Beauvoir's views on free will and those of Jean-Paul Sartre, though some key differences emerge as well:\\n\\nSimilarities with Sartre:\\n\\n- Both were existentialist thinkers who rejected determinism and emphasized human freedom and responsibility.\\n\\n- They agreed that existence precedes essence - there is no predefined human nature that determines who we are.\\n\\n- Individuals must define themselves through their choices and actions. This leads to anxiety but also freedom.\\n\\n- The human condition is characterized by ambiguity and uncertainty, rather than fixed meanings/values.\\n\\n- Both felt that most people evade their freedom through self-deception, conformity, or adopting collective identities/values uncritically.\\n\\nDifferences from Sartre: \\n\\n- Sartre placed more emphasis on the burden and anguish of radical freedom. De Beauvoir focused more on its positive potential.\\n\\n- De Beauvoir critiqued Sartre's premise that human relations are necessarily conflictual. She saw more potential for mutual recognition.\\n\\n- Sartre saw the Other's gaze as a threat to freedom. De Beauvoir put more stress on how the Other's gaze can confirm\")}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chain_with_history.invoke(\n",
+    "    [HumanMessage(content=\"How did this compare to Sartre\")],\n",
+    "    config={\"configurable\": {\"session_id\": \"baz\"}},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b898d1b1-11e6-4d30-a8dd-cc5e45533611",
+   "metadata": {},
+   "source": [
+    ":::tip\n",
+    "\n",
+    "[LangSmith trace](https://smith.langchain.com/public/f6c3e1d1-a49d-4955-a9fa-c6519df74fa7/r)\n",
+    "\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1724292c-01c6-44bb-83e8-9cdb6bf01483",
+   "metadata": {},
+   "source": [
+    "## More examples\n",
+    "\n",
+    "We could also do any of the below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd89240b-5a25-48f8-9568-5c1127f9ffad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from operator import itemgetter\n",
+    "\n",
+    "# messages in, messages out\n",
+    "RunnableWithMessageHistory(\n",
+    "    ChatAnthropic(model=\"claude-2\"),\n",
+    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
+    ")\n",
+    "\n",
+    "# dict with single key for all messages in, messages out\n",
+    "RunnableWithMessageHistory(\n",
+    "    itemgetter(\"input_messages\") | ChatAnthropic(model=\"claude-2\"),\n",
+    "    lambda session_id: RedisChatMessageHistory(session_id, url=REDIS_URL),\n",
+    "    input_messages_key=\"input_messages\",\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "poetry-venv",
+   "language": "python",
+   "name": "poetry-venv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/expression_language/how_to/passthrough.ipynb

@@ -0,0 +1,159 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d35de667-0352-4bfb-a890-cebe7f676fe7",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 1\n",
+    "title: \"RunnablePassthrough: Passing data through\"\n",
+    "keywords: [RunnablePassthrough, RunnableParallel, LCEL]\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b022ab74-794d-4c54-ad47-ff9549ddb9d2",
+   "metadata": {},
+   "source": [
+    "# Passing data through\n",
+    "\n",
+    "RunnablePassthrough allows to pass inputs unchanged or with the addition of extra keys. This typically is used in conjuction with RunnableParallel to assign data to a new key in the map. \n",
+    "\n",
+    "RunnablePassthrough() called on it's own, will simply take the input and pass it through. \n",
+    "\n",
+    "RunnablePassthrough called with assign (`RunnablePassthrough.assign(...)`) will take the input, and will add the extra arguments passed to the assign function. \n",
+    "\n",
+    "See the example below:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "03988b8d-d54c-4492-8707-1594372cf093",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'passed': {'num': 1}, 'extra': {'num': 1, 'mult': 3}, 'modified': 2}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.schema.runnable import RunnableParallel, RunnablePassthrough\n",
+    "\n",
+    "runnable = RunnableParallel(\n",
+    "    passed=RunnablePassthrough(),\n",
+    "    extra=RunnablePassthrough.assign(mult=lambda x: x[\"num\"] * 3),\n",
+    "    modified=lambda x: x[\"num\"] + 1,\n",
+    ")\n",
+    "\n",
+    "runnable.invoke({\"num\": 1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "702c7acc-cd31-4037-9489-647df192fd7c",
+   "metadata": {},
+   "source": [
+    "As seen above, `passed` key was called with `RunnablePassthrough()` and so it simply passed on `{'num': 1}`. \n",
+    "\n",
+    "In the second line, we used `RunnablePastshrough.assign` with a lambda that multiplies the numerical value by 3. In this cased, `extra` was set with `{'num': 1, 'mult': 3}` which is the original value with the `mult` key added. \n",
+    "\n",
+    "Finally, we also set a third key in the map with `modified` which uses a labmda to set a single value adding 1 to the num, which resulted in `modified` key with the value of `2`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "15187a3b-d666-4b9b-a258-672fc51fe0e2",
+   "metadata": {},
+   "source": [
+    "## Retrieval Example\n",
+    "\n",
+    "In the example below, we see a use case where we use RunnablePassthrough along with RunnableMap. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "267d1460-53c1-4fdb-b2c3-b6a1eb7fccff",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Harrison worked at Kensho.'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.chat_models import ChatOpenAI\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.prompts import ChatPromptTemplate\n",
+    "from langchain.schema.output_parser import StrOutputParser\n",
+    "from langchain.schema.runnable import RunnablePassthrough\n",
+    "from langchain.vectorstores import FAISS\n",
+    "\n",
+    "vectorstore = FAISS.from_texts(\n",
+    "    [\"harrison worked at kensho\"], embedding=OpenAIEmbeddings()\n",
+    ")\n",
+    "retriever = vectorstore.as_retriever()\n",
+    "template = \"\"\"Answer the question based only on the following context:\n",
+    "{context}\n",
+    "\n",
+    "Question: {question}\n",
+    "\"\"\"\n",
+    "prompt = ChatPromptTemplate.from_template(template)\n",
+    "model = ChatOpenAI()\n",
+    "\n",
+    "retrieval_chain = (\n",
+    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
+    "    | prompt\n",
+    "    | model\n",
+    "    | StrOutputParser()\n",
+    ")\n",
+    "\n",
+    "retrieval_chain.invoke(\"where did harrison work?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "392cd4c4-e7ed-4ab8-934d-f7a4eca55ee1",
+   "metadata": {},
+   "source": [
+    "Here the input to prompt is expected to be a map with keys \"context\" and \"question\". The user input is just the question. So we need to get the context using our retriever and passthrough the user input under the \"question\" key. In this case, the RunnablePassthrough allows us to pass on the user's question to the prompt and model. \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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/expression_language/how_to/routing.ipynb

@@ -1,5 +1,16 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_position: 3\n",
+    "title: \"RunnableBranch: Dynamically route logic based on input\"\n",
+    "keywords: [RunnableBranch, LCEL]\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4b47436a",
@@ -63,7 +74,7 @@
     "chain = (\n",
     "    PromptTemplate.from_template(\n",
     "        \"\"\"Given the user question below, classify it as either being about `LangChain`, `Anthropic`, or `Other`.\n",
-    "                                     \n",
+    "\n",
     "Do not respond with more than one word.\n",
     "\n",
     "<question>\n",
@@ -293,7 +304,7 @@
     }
    ],
    "source": [
-    "full_chain.invoke({\"question\": \"how do I use Anthroipc?\"})"
+    "full_chain.invoke({\"question\": \"how do I use Anthropic?\"})"
    ]
   },
   {

docs/docs/expression_language/index.mdx

@@ -20,7 +20,7 @@ Whenever your LCEL chains have steps that can be executed in parallel (eg if you
 Configure retries and fallbacks for any part of your LCEL chain. This is a great way to make your chains more reliable at scale. We’re currently working on adding streaming support for retries/fallbacks, so you can get the added reliability without any latency cost.
 
 **Access intermediate results**
-For more complex chains it’s often very useful to access the results of intermediate steps even before the final output is produced. This can be used let end-users know something is happening, or even just to debug your chain. You can stream intermediate results, and it’s available on every [LangServe](/docs/langserve) server.
+For more complex chains it’s often very useful to access the results of intermediate steps even before the final output is produced. This can be used to let end-users know something is happening, or even just to debug your chain. You can stream intermediate results, and it’s available on every [LangServe](/docs/langserve) server.
 
 **Input and output schemas**
 Input and output schemas give every LCEL chain Pydantic and JSONSchema schemas inferred from the structure of your chain. This can be used for validation of inputs and outputs, and is an integral part of LangServe.
@@ -30,4 +30,4 @@ As your chains get more and more complex, it becomes increasingly important to u
 With LCEL, **all** steps are automatically logged to [LangSmith](/docs/langsmith/) for maximum observability and debuggability.
 
 **Seamless LangServe deployment integration**
-Any chain created with LCEL can be easily deployed using [LangServe](/docs/langserve).
+Any chain created with LCEL can be easily deployed using [LangServe](/docs/langserve).

docs/docs/expression_language/interface.ipynb

@@ -6,7 +6,7 @@
    "metadata": {},
    "source": [
     "---\n",
-    "sidebar_position: 0\n",
+    "sidebar_position: 1\n",
     "title: Interface\n",
     "---"
    ]
@@ -16,7 +16,7 @@
    "id": "9a9acd2e",
    "metadata": {},
    "source": [
-    "To make it as easy as possible to create custom chains, we've implemented a [\"Runnable\"](https://api.python.langchain.com/en/latest/schema/langchain.schema.runnable.base.Runnable.html#langchain.schema.runnable.base.Runnable) protocol. The `Runnable` protocol is implemented for most components. \n",
+    "To make it as easy as possible to create custom chains, we've implemented a [\"Runnable\"](https://api.python.langchain.com/en/stable/runnables/langchain_core.runnables.base.Runnable.html#langchain_core.runnables.base.Runnable) protocol. The `Runnable` protocol is implemented for most components. \n",
     "This is a standard interface, which makes it easy to define custom chains as well as invoke them in a standard way. \n",
     "The standard interface includes:\n",
     "\n",

docs/docs/get_started/introduction.mdx

@@ -14,7 +14,7 @@ This framework consists of several parts.
 - **[LangServe](/docs/langserve)**: A library for deploying LangChain chains as a REST API.
 - **[LangSmith](/docs/langsmith)**: A developer platform that lets you debug, test, evaluate, and monitor chains built on any LLM framework and seamlessly integrates with LangChain.
 
-![LangChain Diagram](/img/langchain_stack.png)
+![LangChain Diagram](/svg/langchain_stack.svg)
 
 Together, these products simplify the entire application lifecycle:
 - **Develop**: Write your applications in LangChain/LangChain.js. Hit the ground running using Templates for reference.
@@ -49,7 +49,7 @@ LCEL is a declarative way to compose chains. LCEL was designed from day 1 to sup
 
 - **[Overview](/docs/expression_language/)**: LCEL and its benefits
 - **[Interface](/docs/expression_language/interface)**: The standard interface for LCEL objects
-- **[How-to](/docs/expression_language/interface)**: Key features of LCEL
+- **[How-to](/docs/expression_language/how_to)**: Key features of LCEL
 - **[Cookbook](/docs/expression_language/cookbook)**: Example code for accomplishing common tasks
 
 
@@ -79,7 +79,7 @@ Walkthroughs and techniques for common end-to-end use cases, like:
 ### [Integrations](/docs/integrations/providers/)
 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/providers/).
 
-### [Guides](/docs/guides/adapters/openai)
+### [Guides](/docs/guides/guides/debugging)
 Best practices for developing with LangChain.
 
 ### [API reference](https://api.python.langchain.com)

docs/docs/get_started/quickstart.mdx

@@ -344,7 +344,7 @@ category_chain = chat_prompt | ChatOpenAI() | CommaSeparatedListOutputParser()
 app = FastAPI(
   title="LangChain Server",
   version="1.0",
-  description="A simple api server using Langchain's Runnable interfaces",
+  description="A simple API server using LangChain's Runnable interfaces",
 )
 
 # 3. Adding chain route

docs/docs/guides/debugging.md

@@ -12,7 +12,7 @@ Platforms with tracing capabilities like [LangSmith](/docs/langsmith/) and [Wand
 
 For anyone building production-grade LLM applications, we highly recommend using a platform like this.
 
-![LangSmith run](/img/run_details.png)
+![LangSmith run](../../static/img/run_details.png)
 
 ## `set_debug` and `set_verbose`
 

docs/docs/guides/fallbacks.ipynb

@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 1,
    "id": "d3e893bf",
    "metadata": {},
    "outputs": [],
@@ -46,19 +46,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 2,
    "id": "dfdd8bf5",
    "metadata": {},
    "outputs": [],
    "source": [
     "from unittest.mock import patch\n",
     "\n",
-    "from openai.error import RateLimitError"
+    "import httpx\n",
+    "from openai import RateLimitError\n",
+    "\n",
+    "request = httpx.Request(\"GET\", \"/\")\n",
+    "response = httpx.Response(200, request=request)\n",
+    "error = RateLimitError(\"rate limit\", response=response, body=\"\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 3,
    "id": "e6fdffc1",
    "metadata": {},
    "outputs": [],
@@ -71,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 4,
    "id": "584461ab",
    "metadata": {},
    "outputs": [
@@ -85,7 +90,7 @@
    ],
    "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",
+    "with patch(\"openai.resources.chat.completions.Completions.create\", side_effect=error):\n",
     "    try:\n",
     "        print(openai_llm.invoke(\"Why did the chicken cross the road?\"))\n",
     "    except:\n",
@@ -108,7 +113,7 @@
    ],
    "source": [
     "# Now let's try with fallbacks to Anthropic\n",
-    "with patch(\"openai.ChatCompletion.create\", side_effect=RateLimitError()):\n",
+    "with patch(\"openai.resources.chat.completions.Completions.create\", side_effect=error):\n",
     "    try:\n",
     "        print(llm.invoke(\"Why did the chicken cross the road?\"))\n",
     "    except:\n",
@@ -150,7 +155,7 @@
     "    ]\n",
     ")\n",
     "chain = prompt | llm\n",
-    "with patch(\"openai.ChatCompletion.create\", side_effect=RateLimitError()):\n",
+    "with patch(\"openai.resources.chat.completions.Completions.create\", side_effect=error):\n",
     "    try:\n",
     "        print(chain.invoke({\"animal\": \"kangaroo\"}))\n",
     "    except:\n",
@@ -431,7 +436,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

docs/docs/guides/local_llms.ipynb

@@ -32,7 +32,7 @@
     "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",
+    "![Image description](../../static/img/OSS_LLM_overview.png)\n",
     "\n",
     "The relative performance of these models can be assessed using several leaderboards, including:\n",
     "\n",
@@ -55,7 +55,7 @@
     "\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",
+    "![Image description](../../static/img/llama-memory-weights.png)\n",
     "\n",
     "With less precision, we radically decrease the memory needed to store the LLM in memory.\n",
     "\n",
@@ -63,7 +63,7 @@
     "\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",
+    "![Image description](../../static/img/llama_t_put.png)\n",
     "\n",
     "## Quickstart\n",
     "\n",

docs/docs/guides/privacy/presidio_data_anonymization/qa_privacy_protection.ipynb

@@ -60,7 +60,7 @@
     "\n",
     " Firstly, the wallet contains my credit card with number 4111 1111 1111 1111, which is registered under my name and linked to my bank account, PL61109010140000071219812874.\n",
     "\n",
-    " Additionally, the wallet had a driver's license - DL No: 999000680 issued to my name. It also houses my Social Security Number, 602-76-4532. \n",
+    " Additionally, the wallet had a driver's license - DL No: 999000680 issued to my name. It also houses my Social Security Number, 602-76-4532.\n",
     "\n",
     " What's more, I had my polish identity card there, with the number ABC123456.\n",
     "\n",
@@ -68,7 +68,7 @@
     "\n",
     " In case any information arises regarding my wallet, please reach out to me on my phone number, 999-888-7777, or through my personal email, johndoe@example.com.\n",
     "\n",
-    " Please consider this information to be highly confidential and respect my privacy. \n",
+    " Please consider this information to be highly confidential and respect my privacy.\n",
     "\n",
     " The bank has been informed about the stolen credit card and necessary actions have been taken from their end. They will be reachable at their official email, support@bankname.com.\n",
     " My representative there is Victoria Cherry (her business phone: 987-654-3210).\n",
@@ -667,7 +667,11 @@
     "from langchain.chat_models.openai import ChatOpenAI\n",
     "from langchain.prompts import ChatPromptTemplate\n",
     "from langchain.schema.output_parser import StrOutputParser\n",
-    "from langchain.schema.runnable import RunnableLambda, RunnableMap, RunnablePassthrough\n",
+    "from langchain.schema.runnable import (\n",
+    "    RunnableLambda,\n",
+    "    RunnableParallel,\n",
+    "    RunnablePassthrough,\n",
+    ")\n",
     "\n",
     "# 6. Create anonymizer chain\n",
     "template = \"\"\"Answer the question based only on the following context:\n",
@@ -680,7 +684,7 @@
     "model = ChatOpenAI(temperature=0.3)\n",
     "\n",
     "\n",
-    "_inputs = RunnableMap(\n",
+    "_inputs = RunnableParallel(\n",
     "    question=RunnablePassthrough(),\n",
     "    # It is important to remember about question anonymization\n",
     "    anonymized_question=RunnableLambda(anonymizer.anonymize),\n",
@@ -882,7 +886,7 @@
     "\n",
     "\n",
     "chain_with_deanonymization = (\n",
-    "    RunnableMap({\"question\": RunnablePassthrough()})\n",
+    "    RunnableParallel({\"question\": RunnablePassthrough()})\n",
     "    | {\n",
     "        \"context\": itemgetter(\"question\")\n",
     "        | retriever\n",

docs/docs/guides/safety/amazon_comprehend_chain.ipynb

@@ -7,7 +7,9 @@
    "source": [
     "# Amazon Comprehend Moderation Chain\n",
     "\n",
-    "This notebook shows how to use [Amazon Comprehend](https://aws.amazon.com/comprehend/) to detect and handle `Personally Identifiable Information` (`PII`) and toxicity.\n",
+    ">[Amazon Comprehend](https://aws.amazon.com/comprehend/) is a natural-language processing (NLP) service that uses machine learning to uncover valuable insights and connections in text.\n",
+    "\n",
+    "This notebook shows how to use `Amazon Comprehend` to detect and handle `Personally Identifiable Information` (`PII`) and toxicity.\n",
     "\n",
     "## Setting up"
    ]
@@ -1417,7 +1419,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/guides/safety/hugging_face_prompt_injection.ipynb

@@ -8,7 +8,7 @@
     "# Hugging Face prompt injection identification\n",
     "\n",
     "This notebook shows how to prevent prompt injection attacks using the text classification model from `HuggingFace`.\n",
-    "It exploits the *deberta* model trained to identify prompt injections: https://huggingface.co/deepset/deberta-v3-base-injection"
+    "By default it uses a *deberta* model trained to identify prompt injections. In this walkthrough we'll use https://huggingface.co/laiyer/deberta-v3-base-prompt-injection."
    ]
   },
   {
@@ -21,19 +21,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "aea25588-3c3f-4506-9094-221b3a0d519b",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "58ab3557623a495d8cc3c3e32a61938f",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "'hugging_face_injection_identifier'"
+       "Downloading config.json:   0%|          | 0.00/994 [00:00<?, ?B/s]"
       ]
      },
-     "execution_count": 1,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3bf062f02d304ab5a485a2a228b4cf41",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading model.safetensors:   0%|          | 0.00/738M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -41,7 +59,10 @@
     "    HuggingFaceInjectionIdentifier,\n",
     ")\n",
     "\n",
-    "injection_identifier = HuggingFaceInjectionIdentifier()\n",
+    "# Using https://huggingface.co/laiyer/deberta-v3-base-prompt-injection\n",
+    "injection_identifier = HuggingFaceInjectionIdentifier(\n",
+    "    model=\"laiyer/deberta-v3-base-prompt-injection\"\n",
+    ")\n",
     "injection_identifier.name"
    ]
   },
@@ -299,9 +320,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv"
   },
   "language_info": {
    "codemirror_mode": {
@@ -313,7 +334,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/integrations/adapters/openai-old.ipynb

@@ -5,7 +5,9 @@
    "id": "700a516b",
    "metadata": {},
    "source": [
-    "# OpenAI Adapter\n",
+    "# OpenAI Adapter(Old)\n",
+    "\n",
+    "**Please ensure OpenAI library is less than 1.0.0; otherwise, refer to the newer doc [OpenAI Adapter](./openai).**\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",
@@ -49,18 +51,6 @@
     "Original OpenAI call"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "e1d27dfa",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "result = openai.ChatCompletion.create(\n",
-    "    messages=messages, model=\"gpt-3.5-turbo\", temperature=0\n",
-    ")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 15,
@@ -79,6 +69,9 @@
     }
    ],
    "source": [
+    "result = openai.ChatCompletion.create(\n",
+    "    messages=messages, model=\"gpt-3.5-turbo\", temperature=0\n",
+    ")\n",
     "result[\"choices\"][0][\"message\"].to_dict_recursive()"
    ]
   },
@@ -90,18 +83,6 @@
     "LangChain OpenAI wrapper call"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "87c2d515",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lc_result = lc_openai.ChatCompletion.create(\n",
-    "    messages=messages, model=\"gpt-3.5-turbo\", temperature=0\n",
-    ")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 17,
@@ -120,6 +101,9 @@
     }
    ],
    "source": [
+    "lc_result = lc_openai.ChatCompletion.create(\n",
+    "    messages=messages, model=\"gpt-3.5-turbo\", temperature=0\n",
+    ")\n",
     "lc_result[\"choices\"][0][\"message\"]"
    ]
   },
@@ -131,18 +115,6 @@
     "Swapping out model providers"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "7a2c011c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lc_result = lc_openai.ChatCompletion.create(\n",
-    "    messages=messages, model=\"claude-2\", temperature=0, provider=\"ChatAnthropic\"\n",
-    ")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 19,
@@ -161,6 +133,9 @@
     }
    ],
    "source": [
+    "lc_result = lc_openai.ChatCompletion.create(\n",
+    "    messages=messages, model=\"claude-2\", temperature=0, provider=\"ChatAnthropic\"\n",
+    ")\n",
     "lc_result[\"choices\"][0][\"message\"]"
    ]
   },
@@ -302,7 +277,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

docs/docs/integrations/adapters/openai.ipynb

@@ -0,0 +1,318 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "700a516b",
+   "metadata": {},
+   "source": [
+    "# OpenAI Adapter\n",
+    "\n",
+    "**Please ensure OpenAI library is version 1.0.0 or higher; otherwise, refer to the older doc [OpenAI Adapter(Old)](./openai-old).**\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": [
+    "## chat.completions.create"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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": 3,
+   "id": "012d81ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'content': 'Hello! How can I assist you today?',\n",
+       " 'role': 'assistant',\n",
+       " 'function_call': None,\n",
+       " 'tool_calls': None}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result = openai.chat.completions.create(\n",
+    "    messages=messages, model=\"gpt-3.5-turbo\", temperature=0\n",
+    ")\n",
+    "result.choices[0].message.model_dump()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db5b5500",
+   "metadata": {},
+   "source": [
+    "LangChain OpenAI wrapper call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c67a5ac8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': 'Hello! How can I help you today?'}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lc_result = lc_openai.chat.completions.create(\n",
+    "    messages=messages, model=\"gpt-3.5-turbo\", temperature=0\n",
+    ")\n",
+    "\n",
+    "lc_result.choices[0].message  # Attribute access"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "37a6e461-8608-47f6-ac45-12ad753c062a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': 'Hello! How can I help you today?'}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lc_result[\"choices\"][0][\"message\"]  # Also compatible with index access"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "034ba845",
+   "metadata": {},
+   "source": [
+    "Swapping out model providers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f7c94827",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lc_result = lc_openai.chat.completions.create(\n",
+    "    messages=messages, model=\"claude-2\", temperature=0, provider=\"ChatAnthropic\"\n",
+    ")\n",
+    "lc_result.choices[0].message"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb3f181d",
+   "metadata": {},
+   "source": [
+    "## chat.completions.stream"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7b8cd18",
+   "metadata": {},
+   "source": [
+    "Original OpenAI call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "fd8cb1ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'content': '', 'function_call': None, 'role': 'assistant', 'tool_calls': None}\n",
+      "{'content': 'Hello', 'function_call': None, 'role': None, 'tool_calls': None}\n",
+      "{'content': '!', 'function_call': None, 'role': None, 'tool_calls': None}\n",
+      "{'content': ' How', 'function_call': None, 'role': None, 'tool_calls': None}\n",
+      "{'content': ' can', 'function_call': None, 'role': None, 'tool_calls': None}\n",
+      "{'content': ' I', 'function_call': None, 'role': None, 'tool_calls': None}\n",
+      "{'content': ' assist', 'function_call': None, 'role': None, 'tool_calls': None}\n",
+      "{'content': ' you', 'function_call': None, 'role': None, 'tool_calls': None}\n",
+      "{'content': ' today', 'function_call': None, 'role': None, 'tool_calls': None}\n",
+      "{'content': '?', 'function_call': None, 'role': None, 'tool_calls': None}\n",
+      "{'content': None, 'function_call': None, 'role': None, 'tool_calls': None}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in openai.chat.completions.create(\n",
+    "    messages=messages, model=\"gpt-3.5-turbo\", temperature=0, stream=True\n",
+    "):\n",
+    "    print(c.choices[0].delta.model_dump())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b2a076b",
+   "metadata": {},
+   "source": [
+    "LangChain OpenAI wrapper call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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.chat.completions.create(\n",
+    "    messages=messages, model=\"gpt-3.5-turbo\", temperature=0, 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": 9,
+   "id": "68f0214e",
+   "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.chat.completions.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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/callbacks/argilla.ipynb

@@ -7,8 +7,6 @@
    "source": [
     "# Argilla\n",
     "\n",
-    "![Argilla - Open-source data platform for LLMs](https://argilla.io/og.png)\n",
-    "\n",
     ">[Argilla](https://argilla.io/) is an open-source data curation platform for LLMs.\n",
     "> Using Argilla, everyone can build robust language models through faster data curation \n",
     "> using both human and machine feedback. We provide support for each step in the MLOps cycle, \n",
@@ -410,7 +408,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

docs/docs/integrations/callbacks/context.ipynb

@@ -7,12 +7,9 @@
    "source": [
     "# Context\n",
     "\n",
-    "![Context - User Analytics for LLM Powered Products](https://with.context.ai/langchain.png)\n",
+    ">[Context](https://context.ai/) provides user analytics for LLM-powered products and features.\n",
     "\n",
-    "[Context](https://context.ai/) provides user analytics for LLM powered products and features.\n",
-    "\n",
-    "With Context, you can start understanding your users and improving their experiences in less than 30 minutes.\n",
-    "\n"
+    "With `Context`, you can start understanding your users and improving their experiences in less than 30 minutes.\n"
    ]
   },
   {
@@ -89,11 +86,9 @@
    "metadata": {},
    "source": [
     "## Usage\n",
-    "### Using the Context callback within a chat model\n",
+    "### Context callback within a chat model\n",
     "\n",
-    "The Context callback handler can be used to directly record transcripts between users and AI assistants.\n",
-    "\n",
-    "#### Example"
+    "The Context callback handler can be used to directly record transcripts between users and AI assistants."
    ]
   },
   {
@@ -132,7 +127,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Using the Context callback within Chains\n",
+    "### Context callback within Chains\n",
     "\n",
     "The Context callback handler can also be used to record the inputs and outputs of chains. Note that intermediate steps of the chain are not recorded - only the starting inputs and final outputs.\n",
     "\n",
@@ -149,9 +144,7 @@
     ">handler = ContextCallbackHandler(token)\n",
     ">chat = ChatOpenAI(temperature=0.9, callbacks=[callback])\n",
     ">chain = LLMChain(llm=chat, prompt=chat_prompt_template, callbacks=[callback])\n",
-    ">```\n",
-    "\n",
-    "#### Example"
+    ">```\n"
    ]
   },
   {
@@ -203,7 +196,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

docs/docs/integrations/callbacks/infino.ipynb

@@ -7,12 +7,14 @@
    "source": [
     "# Infino\n",
     "\n",
+    ">[Infino](https://github.com/infinohq/infino) is a scalable telemetry store designed for logs, metrics, and traces. Infino can function as a standalone observability solution or as the storage layer in your observability stack.\n",
+    "\n",
     "This example shows how one can track the following while calling OpenAI and ChatOpenAI models via `LangChain` and [Infino](https://github.com/infinohq/infino):\n",
     "\n",
-    "* prompt input,\n",
-    "* response from `ChatGPT` or any other `LangChain` model,\n",
-    "* latency,\n",
-    "* errors,\n",
+    "* prompt input\n",
+    "* response from `ChatGPT` or any other `LangChain` model\n",
+    "* latency\n",
+    "* errors\n",
     "* number of tokens consumed"
    ]
   },
@@ -454,7 +456,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/callbacks/labelstudio.ipynb

@@ -4,6 +4,9 @@
    "cell_type": "markdown",
    "metadata": {
     "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    },
     "pycharm": {
      "name": "#%% md\n"
     }
@@ -11,17 +14,14 @@
    "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",
+    ">[Label Studio](https://labelstud.io/guide/get_started) 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",
+    "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",
+    "- Aggregate all input prompts, conversations, and responses in a single `Label Studio` 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."
+    "- Evaluate model responses through human feedback. `Label Studio` provides an interface for humans to review and provide feedback on model responses, allowing evaluation and iteration."
    ]
   },
   {
@@ -362,9 +362,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "labelops",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "labelops"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -376,9 +376,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.16"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 1
+ "nbformat_minor": 4
 }

docs/docs/integrations/callbacks/llmonitor.md

@@ -1,6 +1,6 @@
 # LLMonitor
 
-[LLMonitor](https://llmonitor.com?utm_source=langchain&utm_medium=py&utm_campaign=docs) is an open-source observability platform that provides cost and usage analytics, user tracking, tracing and evaluation tools.
+>[LLMonitor](https://llmonitor.com?utm_source=langchain&utm_medium=py&utm_campaign=docs) is an open-source observability platform that provides cost and usage analytics, user tracking, tracing and evaluation tools.
 
 <video controls width='100%' >
   <source src='https://llmonitor.com/videos/demo-annotated.mp4'/>

docs/docs/integrations/callbacks/promptlayer.ipynb

@@ -7,11 +7,10 @@
    "source": [
     "# PromptLayer\n",
     "\n",
-    "![PromptLayer](https://promptlayer.com/text_logo.png)\n",
     "\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",
+    ">[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 (e.g. [`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."
    ]
@@ -51,7 +50,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Usage\n",
+    "## Usage\n",
     "\n",
     "Getting started with `PromptLayerCallbackHandler` is fairly simple, it takes two optional arguments:\n",
     "1. `pl_tags` - an optional list of strings that will be tracked as tags on PromptLayer.\n",
@@ -63,7 +62,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Simple OpenAI Example\n",
+    "## Simple OpenAI Example\n",
     "\n",
     "In this simple example we use `PromptLayerCallbackHandler` with `ChatOpenAI`. We add a PromptLayer tag named `chatopenai`"
    ]
@@ -99,7 +98,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### GPT4All Example"
+    "## GPT4All Example"
    ]
   },
   {
@@ -125,9 +124,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Full Featured Example\n",
+    "## Full Featured Example\n",
     "\n",
-    "In this example we unlock more of the power of PromptLayer.\n",
+    "In this example, we unlock more of the power of `PromptLayer`.\n",
     "\n",
     "PromptLayer allows you to visually create, version, and track prompt templates. Using the [Prompt Registry](https://docs.promptlayer.com/features/prompt-registry), we can programmatically fetch the prompt template called `example`.\n",
     "\n",
@@ -182,7 +181,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -196,7 +195,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8 (default, Apr 13 2021, 12:59:45) \n[Clang 10.0.0 ]"
+   "version": "3.10.12"
   },
   "vscode": {
    "interpreter": {

docs/docs/integrations/callbacks/sagemaker_tracking.ipynb

@@ -7,14 +7,15 @@
    "source": [
     "# SageMaker Tracking\n",
     "\n",
-    "This notebook shows how LangChain Callback can be used to log and track prompts and other LLM hyperparameters into SageMaker Experiments. Here, we use different scenarios to showcase the capability:\n",
+    ">[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that is used to quickly and easily build, train and deploy machine learning (ML) models. \n",
+    "\n",
+    ">[Amazon SageMaker Experiments](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html) is a capability of `Amazon SageMaker` that lets you organize, track, compare and evaluate ML experiments and model versions.\n",
+    "\n",
+    "This notebook shows how LangChain Callback can be used to log and track prompts and other LLM hyperparameters into `SageMaker Experiments`. Here, we use different scenarios to showcase the capability:\n",
     "* **Scenario 1**: *Single LLM* - A case where a single LLM model is used to generate output based on a given prompt.\n",
     "* **Scenario 2**: *Sequential Chain* - A case where a sequential chain of two LLM models is used.\n",
     "* **Scenario 3**: *Agent with Tools (Chain of Thought)* - A case where multiple tools (search and math) are used in addition to an LLM.\n",
     "\n",
-    "[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that is used to quickly and easily build, train and deploy machine learning (ML) models. \n",
-    "\n",
-    "[Amazon SageMaker Experiments](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html) is a capability of Amazon SageMaker that lets you organize, track, compare and evaluate ML experiments and model versions.\n",
     "\n",
     "In this notebook, we will create a single experiment to log the prompts from each scenario."
    ]
@@ -899,9 +900,9 @@
   ],
   "instance_type": "ml.t3.large",
   "kernelspec": {
-   "display_name": "conda_pytorch_p310",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda_pytorch_p310"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -913,7 +914,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.10"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/callbacks/trubrics.ipynb

@@ -9,12 +9,13 @@
    "source": [
     "# Trubrics\n",
     "\n",
-    "![Trubrics](https://miro.medium.com/v2/resize:fit:720/format:webp/1*AhYbKO-v8F4u3hx2aDIqKg.png)\n",
     "\n",
-    "[Trubrics](https://trubrics.com) is an LLM user analytics platform that lets you collect, analyse and manage user\n",
-    "prompts & feedback on AI models. In this guide we will go over how to setup the `TrubricsCallbackHandler`. \n",
+    ">[Trubrics](https://trubrics.com) is an LLM user analytics platform that lets you collect, analyse and manage user\n",
+    "prompts & feedback on AI models.\n",
+    ">\n",
+    ">Check out [Trubrics repo](https://github.com/trubrics/trubrics-sdk) for more information on `Trubrics`.\n",
     "\n",
-    "Check out [our repo](https://github.com/trubrics/trubrics-sdk) for more information on Trubrics."
+    "In this guide, we will go over how to set up the `TrubricsCallbackHandler`. \n"
    ]
   },
   {
@@ -347,9 +348,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "langchain",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "langchain"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -361,7 +362,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/anthropic.ipynb

@@ -1,11 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "a016701c",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Anthropic\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bf733a38-db84-4363-89e2-de6735c37230",
    "metadata": {},
    "source": [
-    "# Anthropic\n",
+    "# ChatAnthropic\n",
     "\n",
     "This notebook covers how to get started with Anthropic chat models."
    ]

docs/docs/integrations/chat/anyscale.ipynb

@@ -1,12 +1,22 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "31895fc4",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Anyscale\n",
+    "---"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
    "id": "642fd21c-600a-47a1-be96-6e1438b421a9",
    "metadata": {},
    "source": [
-    "# Anyscale\n",
+    "# ChatAnyscale\n",
     "\n",
     "This notebook demonstrates the use of `langchain.chat_models.ChatAnyscale` for [Anyscale Endpoints](https://endpoints.anyscale.com/).\n",
     "\n",
@@ -33,7 +43,7 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       " ········\n"

docs/docs/integrations/chat/azure_chat_openai.ipynb

@@ -1,13 +1,25 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "641f8cb0",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Azure OpenAI\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "38f26d7a",
    "metadata": {},
    "source": [
-    "# Azure OpenAI\n",
+    "# AzureChatOpenAI\n",
     "\n",
-    "This notebook goes over how to connect to an Azure hosted OpenAI endpoint. We recommend having version `openai>=1` installed."
+    ">[Azure OpenAI Service](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview) provides REST API access to OpenAI's powerful language models including the GPT-4, GPT-3.5-Turbo, and Embeddings model series. These models can be easily adapted to your specific task including but not limited to content generation, summarization, semantic search, and natural language to code translation. Users can access the service through REST APIs, Python SDK, or a web-based interface in the Azure OpenAI Studio.\n",
+    "\n",
+    "This notebook goes over how to connect to an Azure-hosted OpenAI endpoint. We recommend having version `openai>=1` installed."
    ]
   },
   {
@@ -162,7 +174,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/azureml_chat_endpoint.ipynb

@@ -1,14 +1,25 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Azure ML Endpoint\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# AzureML Chat Online Endpoint\n",
+    "# AzureMLChatOnlineEndpoint\n",
     "\n",
-    "[AzureML](https://azure.microsoft.com/en-us/products/machine-learning/) is a platform used to build, train, and deploy machine learning models. Users can explore the types of models to deploy in the Model Catalog, which provides Azure Foundation Models and OpenAI Models. Azure Foundation Models include various open-source models and popular Hugging Face models. Users can also import models of their liking into AzureML.\n",
+    ">[Azure Machine Learning](https://azure.microsoft.com/en-us/products/machine-learning/) is a platform used to build, train, and deploy machine learning models. Users can explore the types of models to deploy in the Model Catalog, which provides Azure Foundation Models and OpenAI Models. `Azure Foundation Models` include various open-source models and popular Hugging Face models. Users can also import models of their liking into AzureML.\n",
+    ">\n",
+    ">[Azure Machine Learning Online Endpoints](https://learn.microsoft.com/en-us/azure/machine-learning/concept-endpoints). After you train machine learning models or pipelines, you need to deploy them to production so that others can use them for inference. Inference is the process of applying new input data to the machine learning model or pipeline to generate outputs. While these outputs are typically referred to as \"predictions,\" inferencing can be used to generate outputs for other machine learning tasks, such as classification and clustering. In `Azure Machine Learning`, you perform inferencing by using endpoints and deployments. `Endpoints` and `Deployments` allow you to decouple the interface of your production workload from the implementation that serves it.\n",
     "\n",
-    "This notebook goes over how to use a chat model hosted on an `AzureML online endpoint`"
+    "This notebook goes over how to use a chat model hosted on an `Azure Machine Learning Endpoint`."
    ]
   },
   {
@@ -91,7 +102,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/baichuan.ipynb

@@ -1,10 +1,19 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Baichuan Chat\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Baichuan Chat\n",
+    "# ChatBaichuan\n",
     "\n",
     "Baichuan chat models API by Baichuan Intelligent Technology. For more information, see [https://platform.baichuan-ai.com/docs/api](https://platform.baichuan-ai.com/docs/api)"
    ]
@@ -63,7 +72,9 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "AIMessage(content='首先，我们需要确定闰年的二月有多少天。闰年的二月有29天。\\n\\n然后，我们可以计算你的月薪：\\n\\n日薪 = 月薪 / (当月天数)\\n\\n所以，你的月薪 = 日薪 * 当月天数\\n\\n将数值代入公式：\\n\\n月薪 = 8元/天 * 29天 = 232元\\n\\n因此，你在闰年的二月的月薪是232元。')"
+      "text/plain": [
+       "AIMessage(content='首先，我们需要确定闰年的二月有多少天。闰年的二月有29天。\\n\\n然后，我们可以计算你的月薪：\\n\\n日薪 = 月薪 / (当月天数)\\n\\n所以，你的月薪 = 日薪 * 当月天数\\n\\n将数值代入公式：\\n\\n月薪 = 8元/天 * 29天 = 232元\\n\\n因此，你在闰年的二月的月薪是232元。')"
+      ]
      },
      "execution_count": 3,
      "metadata": {},
@@ -76,16 +87,23 @@
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "## For ChatBaichuan with Streaming"
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "## For ChatBaichuan with Streaming"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-17T15:14:25.870044Z",
+     "start_time": "2023-10-17T15:14:25.863381Z"
+    },
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "chat = ChatBaichuan(\n",
@@ -93,22 +111,24 @@
     "    baichuan_secret_key=\"YOUR_SECRET_KEY\",\n",
     "    streaming=True,\n",
     ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-10-17T15:14:25.870044Z",
-     "start_time": "2023-10-17T15:14:25.863381Z"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-17T15:14:27.153546Z",
+     "start_time": "2023-10-17T15:14:25.868470Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "AIMessageChunk(content='首先，我们需要确定闰年的二月有多少天。闰年的二月有29天。\\n\\n然后，我们可以计算你的月薪：\\n\\n日薪 = 月薪 / (当月天数)\\n\\n所以，你的月薪 = 日薪 * 当月天数\\n\\n将数值代入公式：\\n\\n月薪 = 8元/天 * 29天 = 232元\\n\\n因此，你在闰年的二月的月薪是232元。')"
+      "text/plain": [
+       "AIMessageChunk(content='首先，我们需要确定闰年的二月有多少天。闰年的二月有29天。\\n\\n然后，我们可以计算你的月薪：\\n\\n日薪 = 月薪 / (当月天数)\\n\\n所以，你的月薪 = 日薪 * 当月天数\\n\\n将数值代入公式：\\n\\n月薪 = 8元/天 * 29天 = 232元\\n\\n因此，你在闰年的二月的月薪是232元。')"
+      ]
      },
      "execution_count": 6,
      "metadata": {},
@@ -117,14 +137,7 @@
    ],
    "source": [
     "chat([HumanMessage(content=\"我日薪8块钱，请问在闰年的二月，我月薪多少\")])"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-10-17T15:14:27.153546Z",
-     "start_time": "2023-10-17T15:14:25.868470Z"
-    }
-   }
+   ]
   }
  ],
  "metadata": {

docs/docs/integrations/chat/baidu_qianfan_endpoint.ipynb

@@ -1,11 +1,20 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Baidu Qianfan\n",
+    "---"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Baidu Qianfan\n",
+    "# QianfanChatEndpoint\n",
     "\n",
     "Baidu AI Cloud Qianfan Platform is a one-stop large model development and service operation platform for enterprise developers. Qianfan not only provides including the model of Wenxin Yiyan (ERNIE-Bot) and the third-party open-source models, but also provides various AI development tools and the whole set of development environment, which facilitates customers to use and develop large model applications easily.\n",
     "\n",

docs/docs/integrations/chat/bedrock.ipynb

@@ -1,13 +1,31 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "fbc66410",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Bedrock Chat\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bf733a38-db84-4363-89e2-de6735c37230",
    "metadata": {},
    "source": [
-    "# Bedrock Chat\n",
+    "# BedrockChat\n",
     "\n",
-    "[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case"
+    ">[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that offers a choice of \n",
+    "> high-performing foundation models (FMs) from leading AI companies like `AI21 Labs`, `Anthropic`, `Cohere`, \n",
+    "> `Meta`, `Stability AI`, and `Amazon` via a single API, along with a broad set of capabilities you need to \n",
+    "> build generative AI applications with security, privacy, and responsible AI. Using `Amazon Bedrock`, \n",
+    "> you can easily experiment with and evaluate top FMs for your use case, privately customize them with \n",
+    "> your data using techniques such as fine-tuning and `Retrieval Augmented Generation` (`RAG`), and build \n",
+    "> agents that execute tasks using your enterprise systems and data sources. Since `Amazon Bedrock` is \n",
+    "> serverless, you don't have to manage any infrastructure, and you can securely integrate and deploy \n",
+    "> generative AI capabilities into your applications using the AWS services you are already familiar with.\n"
    ]
   },
   {
@@ -131,7 +149,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat/cohere.ipynb

@@ -1,11 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "53fbf15f",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Cohere\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bf733a38-db84-4363-89e2-de6735c37230",
    "metadata": {},
    "source": [
-    "# Cohere\n",
+    "# ChatCohere\n",
     "\n",
     "This notebook covers how to get started with Cohere chat models."
    ]

docs/docs/integrations/chat/ernie.ipynb

@@ -1,13 +1,34 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Ernie Bot Chat\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# ERNIE-Bot Chat\n",
+    "# ErnieBotChat\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."
+    "This notebook covers how to get started with ErnieBot chat models.\n",
+    "\n",
+    "**Note:** We recommend users using this class to switch to [Baidu Qianfan](./baidu_qianfan_endpoint). they are 3 why we recommend users to use `QianfanChatEndpoint`:\n",
+    "1. `QianfanChatEndpoint` support more LLM in the Qianfan platform.\n",
+    "2. `QianfanChatEndpoint` support streaming mode.\n",
+    "3. `QianfanChatEndpoint` support function calling usgage.\n",
+    "\n",
+    "Some tips for migration:\n",
+    "- change `ernie_client_id` to `qianfan_ak`, also change `ernie_client_secret` to `qianfan_sk`.\n",
+    "- install `qianfan` package. \n",
+    "    ```\n",
+    "    pip install qianfan\n",
+    "    ```"
    ]
   },
   {

docs/docs/integrations/chat/everlyai.ipynb

@@ -1,11 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "5e45f35c",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: EverlyAI\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "642fd21c-600a-47a1-be96-6e1438b421a9",
    "metadata": {},
    "source": [
-    "# EverlyAI\n",
+    "# ChatEverlyAI\n",
     "\n",
     ">[EverlyAI](https://everlyai.xyz) allows you to run your ML models at scale in the cloud. It also provides API access to [several LLM models](https://everlyai.xyz).\n",
     "\n",

docs/docs/integrations/chat/fireworks.ipynb

@@ -1,12 +1,22 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "529aeba9",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Fireworks\n",
+    "---"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
    "id": "642fd21c-600a-47a1-be96-6e1438b421a9",
    "metadata": {},
    "source": [
-    "# Fireworks\n",
+    "# ChatFireworks\n",
     "\n",
     ">[Fireworks](https://app.fireworks.ai/) accelerates product development on generative AI by creating an innovative AI experiment and production platform. \n",
     "\n",

docs/docs/integrations/chat/google_vertex_ai_palm.ipynb

@@ -1,11 +1,20 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Google Cloud Vertex AI\n",
+    "---"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Google Cloud Vertex AI \n",
+    "# ChatVertexAI\n",
     "\n",
     "Note: This is separate from the Google PaLM integration. Google has chosen to offer an enterprise version of PaLM through GCP, and this supports the models made available through there. \n",
     "\n",

docs/docs/integrations/chat/hunyuan.ipynb

@@ -1,10 +1,19 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Tencent Hunyuan\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Tencent Hunyuan\n",
+    "# ChatHunyuan\n",
     "\n",
     "Hunyuan chat model API by Tencent. For more information, see [https://cloud.tencent.com/document/product/1729](https://cloud.tencent.com/document/product/1729)"
    ]
@@ -36,7 +45,7 @@
    "outputs": [],
    "source": [
     "chat = ChatHunyuan(\n",
-    "    hunyuan_app_id=\"YOUR_APP_ID\",\n",
+    "    hunyuan_app_id=111111111,\n",
     "    hunyuan_secret_id=\"YOUR_SECRET_ID\",\n",
     "    hunyuan_secret_key=\"YOUR_SECRET_KEY\",\n",
     ")"
@@ -54,7 +63,9 @@
    "outputs": [
     {
      "data": {
-      "text/plain": "AIMessage(content=\"J'aime programmer.\")"
+      "text/plain": [
+       "AIMessage(content=\"J'aime programmer.\")"
+      ]
      },
      "execution_count": 3,
      "metadata": {},
@@ -73,16 +84,23 @@
   },
   {
    "cell_type": "markdown",
-   "source": [
-    "## For ChatHunyuan with Streaming"
-   ],
    "metadata": {
     "collapsed": false
-   }
+   },
+   "source": [
+    "## For ChatHunyuan with Streaming"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-19T10:20:41.507720Z",
+     "start_time": "2023-10-19T10:20:41.496456Z"
+    },
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "chat = ChatHunyuan(\n",
@@ -91,22 +109,24 @@
     "    hunyuan_secret_key=\"YOUR_SECRET_KEY\",\n",
     "    streaming=True,\n",
     ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-10-19T10:20:41.507720Z",
-     "start_time": "2023-10-19T10:20:41.496456Z"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-10-19T10:20:46.275673Z",
+     "start_time": "2023-10-19T10:20:44.241097Z"
+    },
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "AIMessageChunk(content=\"J'aime programmer.\")"
+      "text/plain": [
+       "AIMessageChunk(content=\"J'aime programmer.\")"
+      ]
      },
      "execution_count": 3,
      "metadata": {},
@@ -121,26 +141,19 @@
     "        )\n",
     "    ]\n",
     ")"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "ExecuteTime": {
-     "end_time": "2023-10-19T10:20:46.275673Z",
-     "start_time": "2023-10-19T10:20:44.241097Z"
-    }
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "outputs": [],
-   "source": [],
    "metadata": {
-    "collapsed": false,
     "ExecuteTime": {
      "start_time": "2023-10-19T10:19:56.233477Z"
-    }
-   }
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

docs/docs/integrations/chat/konko.ipynb

@@ -1,10 +1,19 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Konko\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Konko\n",
+    "# ChatKonko\n",
     "\n",
     ">[Konko](https://www.konko.ai/) API is a fully managed Web API designed to help application developers:\n",
     "\n",

docs/docs/integrations/chat/litellm.ipynb

@@ -1,12 +1,22 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "59148044",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: LiteLLM\n",
+    "---"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
    "id": "bf733a38-db84-4363-89e2-de6735c37230",
    "metadata": {},
    "source": [
-    "# 🚅 LiteLLM\n",
+    "# ChatLiteLLM\n",
     "\n",
     "[LiteLLM](https://github.com/BerriAI/litellm) is a library that simplifies calling Anthropic, Azure, Huggingface, Replicate, etc. \n",
     "\n",

docs/docs/integrations/chat/llama2_chat.ipynb

@@ -0,0 +1,739 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "7320f16b",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Llama 2 Chat\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90a1faf2",
+   "metadata": {},
+   "source": [
+    "# Llama2Chat\n",
+    "\n",
+    "This notebook shows how to augment Llama-2 `LLM`s with the `Llama2Chat` wrapper to support the [Llama-2 chat prompt format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2). Several `LLM` implementations in LangChain can be used as interface to Llama-2 chat models. These include [HuggingFaceTextGenInference](https://python.langchain.com/docs/integrations/llms/huggingface_textgen_inference), [LlamaCpp](https://python.langchain.com/docs/use_cases/question_answering/how_to/local_retrieval_qa), [GPT4All](https://python.langchain.com/docs/integrations/llms/gpt4all), ..., to mention a few examples. \n",
+    "\n",
+    "`Llama2Chat` is a generic wrapper that implements `BaseChatModel` and can therefore be used in applications as [chat model](https://python.langchain.com/docs/modules/model_io/models/chat/). `Llama2Chat` converts a list of [chat messages](https://python.langchain.com/docs/modules/model_io/models/chat/#messages) into the [required chat prompt format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2) and forwards the formatted prompt as `str` to the wrapped `LLM`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "36c03540",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains import LLMChain\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain_experimental.chat_models import Llama2Chat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c76910f",
+   "metadata": {},
+   "source": [
+    "For the chat application examples below, we'll use the following chat `prompt_template`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9bbfaf3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.prompts.chat import (\n",
+    "    ChatPromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
+    "    MessagesPlaceholder,\n",
+    ")\n",
+    "from langchain.schema import SystemMessage\n",
+    "\n",
+    "template_messages = [\n",
+    "    SystemMessage(content=\"You are a helpful assistant.\"),\n",
+    "    MessagesPlaceholder(variable_name=\"chat_history\"),\n",
+    "    HumanMessagePromptTemplate.from_template(\"{text}\"),\n",
+    "]\n",
+    "prompt_template = ChatPromptTemplate.from_messages(template_messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f3343b7",
+   "metadata": {},
+   "source": [
+    "## Chat with Llama-2 via `HuggingFaceTextGenInference` LLM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ff99380",
+   "metadata": {},
+   "source": [
+    "A [HuggingFaceTextGenInference](https://python.langchain.com/docs/integrations/llms/huggingface_textgen_inference) LLM encapsulates access to a [text-generation-inference](https://github.com/huggingface/text-generation-inference) server. In the following example, the inference server serves a [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf) model. It can be started locally with:\n",
+    "\n",
+    "```bash\n",
+    "docker run \\\n",
+    "  --rm \\\n",
+    "  --gpus all \\\n",
+    "  --ipc=host \\\n",
+    "  -p 8080:80 \\\n",
+    "  -v ~/.cache/huggingface/hub:/data \\\n",
+    "  -e HF_API_TOKEN=${HF_API_TOKEN} \\\n",
+    "  ghcr.io/huggingface/text-generation-inference:0.9 \\\n",
+    "  --hostname 0.0.0.0 \\\n",
+    "  --model-id meta-llama/Llama-2-13b-chat-hf \\\n",
+    "  --quantize bitsandbytes \\\n",
+    "  --num-shard 4\n",
+    "```\n",
+    "\n",
+    "This works on a machine with 4 x RTX 3080ti cards, for example. Adjust the `--num_shard` value to the number of GPUs available. The `HF_API_TOKEN` environment variable holds the Hugging Face API token."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "238095fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !pip3 install text-generation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79c4ace9",
+   "metadata": {},
+   "source": [
+    "Create a `HuggingFaceTextGenInference` instance that connects to the local inference server and wrap it into `Llama2Chat`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "7a9f6de2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.llms import HuggingFaceTextGenInference\n",
+    "\n",
+    "llm = HuggingFaceTextGenInference(\n",
+    "    inference_server_url=\"http://127.0.0.1:8080/\",\n",
+    "    max_new_tokens=512,\n",
+    "    top_k=50,\n",
+    "    temperature=0.1,\n",
+    "    repetition_penalty=1.03,\n",
+    ")\n",
+    "\n",
+    "model = Llama2Chat(llm=llm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f646a2b",
+   "metadata": {},
+   "source": [
+    "Then you are ready to use the chat `model` together with `prompt_template` and conversation `memory` in an `LLMChain`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "54b5d1d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n",
+    "chain = LLMChain(llm=model, prompt=prompt_template, memory=memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e6717947",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Sure, I'd be happy to help! Here are a few popular locations to consider visiting in Vienna:\n",
+      "\n",
+      "1. Schönbrunn Palace\n",
+      "2. St. Stephen's Cathedral\n",
+      "3. Hofburg Palace\n",
+      "4. Belvedere Palace\n",
+      "5. Prater Park\n",
+      "6. Vienna State Opera\n",
+      "7. Albertina Museum\n",
+      "8. Museum of Natural History\n",
+      "9. Kunsthistorisches Museum\n",
+      "10. Ringstrasse\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    chain.run(\n",
+    "        text=\"What can I see in Vienna? Propose a few locations. Names only, no details.\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "17bf10d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Certainly! St. Stephen's Cathedral (Stephansdom) is one of the most recognizable landmarks in Vienna and a must-see attraction for visitors. This stunning Gothic cathedral is located in the heart of the city and is known for its intricate stone carvings, colorful stained glass windows, and impressive dome.\n",
+      "\n",
+      "The cathedral was built in the 12th century and has been the site of many important events throughout history, including the coronation of Holy Roman emperors and the funeral of Mozart. Today, it is still an active place of worship and offers guided tours, concerts, and special events. Visitors can climb up the south tower for panoramic views of the city or attend a service to experience the beautiful music and chanting.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(chain.run(text=\"Tell me more about #2.\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a297e09",
+   "metadata": {},
+   "source": [
+    "## Chat with Llama-2 via `LlamaCPP` LLM"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "52c1a0b9",
+   "metadata": {},
+   "source": [
+    "For using a Llama-2 chat model with a [LlamaCPP](https://python.langchain.com/docs/integrations/llms/llamacpp) `LMM`, install the `llama-cpp-python` library using [these installation instructions](https://python.langchain.com/docs/integrations/llms/llamacpp#installation). The following example uses a quantized [llama-2-7b-chat.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GGUF/resolve/main/llama-2-7b-chat.Q4_0.gguf) model stored locally at `~/Models/llama-2-7b-chat.Q4_0.gguf`. \n",
+    "\n",
+    "After creating a `LlamaCpp` instance, the `llm` is again wrapped into `Llama2Chat`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "07c0d04e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from /home/martin/Models/llama-2-7b-chat.Q4_0.gguf (version GGUF V2)\n",
+      "llama_model_loader: - tensor    0:                token_embd.weight q4_0     [  4096, 32000,     1,     1 ]\n",
+      "llama_model_loader: - tensor    1:           blk.0.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor    2:            blk.0.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor    3:            blk.0.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor    4:              blk.0.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor    5:            blk.0.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor    6:              blk.0.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor    7:         blk.0.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor    8:              blk.0.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor    9:              blk.0.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   10:           blk.1.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   11:            blk.1.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   12:            blk.1.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   13:              blk.1.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   14:            blk.1.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   15:              blk.1.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   16:         blk.1.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   17:              blk.1.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   18:              blk.1.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   19:          blk.10.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   20:           blk.10.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   21:           blk.10.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   22:             blk.10.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   23:           blk.10.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   24:             blk.10.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   25:        blk.10.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   26:             blk.10.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   27:             blk.10.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   28:          blk.11.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   29:           blk.11.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   30:           blk.11.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   31:             blk.11.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   32:           blk.11.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   33:             blk.11.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   34:        blk.11.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   35:             blk.11.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   36:             blk.11.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   37:          blk.12.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   38:           blk.12.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   39:           blk.12.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   40:             blk.12.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   41:           blk.12.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   42:             blk.12.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   43:        blk.12.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   44:             blk.12.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   45:             blk.12.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   46:          blk.13.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   47:           blk.13.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   48:           blk.13.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   49:             blk.13.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   50:           blk.13.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   51:             blk.13.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   52:        blk.13.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   53:             blk.13.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   54:             blk.13.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   55:          blk.14.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   56:           blk.14.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   57:           blk.14.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   58:             blk.14.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   59:           blk.14.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   60:             blk.14.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   61:        blk.14.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   62:             blk.14.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   63:             blk.14.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   64:          blk.15.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   65:           blk.15.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   66:           blk.15.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   67:             blk.15.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   68:           blk.15.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   69:             blk.15.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   70:        blk.15.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   71:             blk.15.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   72:             blk.15.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   73:          blk.16.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   74:           blk.16.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   75:           blk.16.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   76:             blk.16.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   77:           blk.16.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   78:             blk.16.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   79:        blk.16.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   80:             blk.16.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   81:             blk.16.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   82:          blk.17.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   83:           blk.17.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   84:           blk.17.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   85:             blk.17.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   86:           blk.17.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   87:             blk.17.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   88:        blk.17.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   89:             blk.17.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   90:             blk.17.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   91:          blk.18.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   92:           blk.18.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   93:           blk.18.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   94:             blk.18.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor   95:           blk.18.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor   96:             blk.18.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   97:        blk.18.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   98:             blk.18.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor   99:             blk.18.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  100:          blk.19.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  101:           blk.19.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  102:           blk.19.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  103:             blk.19.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  104:           blk.19.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  105:             blk.19.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  106:        blk.19.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  107:             blk.19.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  108:             blk.19.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  109:           blk.2.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  110:            blk.2.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  111:            blk.2.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  112:              blk.2.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  113:            blk.2.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  114:              blk.2.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  115:         blk.2.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  116:              blk.2.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  117:              blk.2.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  118:          blk.20.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  119:           blk.20.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  120:           blk.20.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  121:             blk.20.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  122:           blk.20.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  123:             blk.20.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  124:        blk.20.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  125:             blk.20.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  126:             blk.20.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  127:          blk.21.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  128:           blk.21.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  129:           blk.21.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  130:             blk.21.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  131:           blk.21.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  132:             blk.21.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  133:        blk.21.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  134:             blk.21.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  135:             blk.21.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  136:          blk.22.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  137:           blk.22.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  138:           blk.22.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  139:             blk.22.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  140:           blk.22.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  141:             blk.22.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  142:        blk.22.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  143:             blk.22.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  144:             blk.22.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  145:          blk.23.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  146:           blk.23.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  147:           blk.23.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  148:             blk.23.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  149:           blk.23.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  150:             blk.23.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  151:        blk.23.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  152:             blk.23.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  153:             blk.23.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  154:           blk.3.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  155:            blk.3.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  156:            blk.3.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  157:              blk.3.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  158:            blk.3.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  159:              blk.3.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  160:         blk.3.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  161:              blk.3.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  162:              blk.3.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  163:           blk.4.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  164:            blk.4.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  165:            blk.4.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  166:              blk.4.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  167:            blk.4.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  168:              blk.4.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  169:         blk.4.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  170:              blk.4.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  171:              blk.4.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  172:           blk.5.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  173:            blk.5.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  174:            blk.5.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  175:              blk.5.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  176:            blk.5.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  177:              blk.5.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  178:         blk.5.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  179:              blk.5.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  180:              blk.5.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  181:           blk.6.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  182:            blk.6.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  183:            blk.6.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  184:              blk.6.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  185:            blk.6.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  186:              blk.6.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  187:         blk.6.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  188:              blk.6.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  189:              blk.6.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  190:           blk.7.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  191:            blk.7.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  192:            blk.7.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  193:              blk.7.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  194:            blk.7.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  195:              blk.7.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  196:         blk.7.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  197:              blk.7.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  198:              blk.7.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  199:           blk.8.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  200:            blk.8.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  201:            blk.8.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  202:              blk.8.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  203:            blk.8.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  204:              blk.8.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  205:         blk.8.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  206:              blk.8.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  207:              blk.8.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  208:           blk.9.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  209:            blk.9.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  210:            blk.9.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  211:              blk.9.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  212:            blk.9.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  213:              blk.9.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  214:         blk.9.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  215:              blk.9.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  216:              blk.9.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  217:                    output.weight q6_K     [  4096, 32000,     1,     1 ]\n",
+      "llama_model_loader: - tensor  218:          blk.24.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  219:           blk.24.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  220:           blk.24.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  221:             blk.24.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  222:           blk.24.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  223:             blk.24.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  224:        blk.24.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  225:             blk.24.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  226:             blk.24.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  227:          blk.25.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  228:           blk.25.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  229:           blk.25.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  230:             blk.25.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  231:           blk.25.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  232:             blk.25.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  233:        blk.25.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  234:             blk.25.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  235:             blk.25.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  236:          blk.26.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  237:           blk.26.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  238:           blk.26.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  239:             blk.26.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  240:           blk.26.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  241:             blk.26.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  242:        blk.26.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  243:             blk.26.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  244:             blk.26.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  245:          blk.27.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  246:           blk.27.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  247:           blk.27.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  248:             blk.27.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  249:           blk.27.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  250:             blk.27.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  251:        blk.27.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  252:             blk.27.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  253:             blk.27.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  254:          blk.28.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  255:           blk.28.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  256:           blk.28.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  257:             blk.28.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  258:           blk.28.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  259:             blk.28.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  260:        blk.28.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  261:             blk.28.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  262:             blk.28.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  263:          blk.29.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  264:           blk.29.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  265:           blk.29.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  266:             blk.29.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  267:           blk.29.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  268:             blk.29.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  269:        blk.29.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  270:             blk.29.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  271:             blk.29.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  272:          blk.30.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  273:           blk.30.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  274:           blk.30.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  275:             blk.30.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  276:           blk.30.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  277:             blk.30.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  278:        blk.30.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  279:             blk.30.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  280:             blk.30.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  281:          blk.31.attn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  282:           blk.31.ffn_down.weight q4_0     [ 11008,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  283:           blk.31.ffn_gate.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  284:             blk.31.ffn_up.weight q4_0     [  4096, 11008,     1,     1 ]\n",
+      "llama_model_loader: - tensor  285:           blk.31.ffn_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - tensor  286:             blk.31.attn_k.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  287:        blk.31.attn_output.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  288:             blk.31.attn_q.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  289:             blk.31.attn_v.weight q4_0     [  4096,  4096,     1,     1 ]\n",
+      "llama_model_loader: - tensor  290:               output_norm.weight f32      [  4096,     1,     1,     1 ]\n",
+      "llama_model_loader: - kv   0:                       general.architecture str     \n",
+      "llama_model_loader: - kv   1:                               general.name str     \n",
+      "llama_model_loader: - kv   2:                       llama.context_length u32     \n",
+      "llama_model_loader: - kv   3:                     llama.embedding_length u32     \n",
+      "llama_model_loader: - kv   4:                          llama.block_count u32     \n",
+      "llama_model_loader: - kv   5:                  llama.feed_forward_length u32     \n",
+      "llama_model_loader: - kv   6:                 llama.rope.dimension_count u32     \n",
+      "llama_model_loader: - kv   7:                 llama.attention.head_count u32     \n",
+      "llama_model_loader: - kv   8:              llama.attention.head_count_kv u32     \n",
+      "llama_model_loader: - kv   9:     llama.attention.layer_norm_rms_epsilon f32     \n",
+      "llama_model_loader: - kv  10:                          general.file_type u32     \n",
+      "llama_model_loader: - kv  11:                       tokenizer.ggml.model str     \n",
+      "llama_model_loader: - kv  12:                      tokenizer.ggml.tokens arr     \n",
+      "llama_model_loader: - kv  13:                      tokenizer.ggml.scores arr     \n",
+      "llama_model_loader: - kv  14:                  tokenizer.ggml.token_type arr     \n",
+      "llama_model_loader: - kv  15:                tokenizer.ggml.bos_token_id u32     \n",
+      "llama_model_loader: - kv  16:                tokenizer.ggml.eos_token_id u32     \n",
+      "llama_model_loader: - kv  17:            tokenizer.ggml.unknown_token_id u32     \n",
+      "llama_model_loader: - kv  18:               general.quantization_version u32     \n",
+      "llama_model_loader: - type  f32:   65 tensors\n",
+      "llama_model_loader: - type q4_0:  225 tensors\n",
+      "llama_model_loader: - type q6_K:    1 tensors\n",
+      "llm_load_vocab: special tokens definition check successful ( 259/32000 ).\n",
+      "llm_load_print_meta: format           = GGUF V2\n",
+      "llm_load_print_meta: arch             = llama\n",
+      "llm_load_print_meta: vocab type       = SPM\n",
+      "llm_load_print_meta: n_vocab          = 32000\n",
+      "llm_load_print_meta: n_merges         = 0\n",
+      "llm_load_print_meta: n_ctx_train      = 4096\n",
+      "llm_load_print_meta: n_embd           = 4096\n",
+      "llm_load_print_meta: n_head           = 32\n",
+      "llm_load_print_meta: n_head_kv        = 32\n",
+      "llm_load_print_meta: n_layer          = 32\n",
+      "llm_load_print_meta: n_rot            = 128\n",
+      "llm_load_print_meta: n_gqa            = 1\n",
+      "llm_load_print_meta: f_norm_eps       = 0.0e+00\n",
+      "llm_load_print_meta: f_norm_rms_eps   = 1.0e-06\n",
+      "llm_load_print_meta: f_clamp_kqv      = 0.0e+00\n",
+      "llm_load_print_meta: f_max_alibi_bias = 0.0e+00\n",
+      "llm_load_print_meta: n_ff             = 11008\n",
+      "llm_load_print_meta: rope scaling     = linear\n",
+      "llm_load_print_meta: freq_base_train  = 10000.0\n",
+      "llm_load_print_meta: freq_scale_train = 1\n",
+      "llm_load_print_meta: n_yarn_orig_ctx  = 4096\n",
+      "llm_load_print_meta: rope_finetuned   = unknown\n",
+      "llm_load_print_meta: model type       = 7B\n",
+      "llm_load_print_meta: model ftype      = mostly Q4_0\n",
+      "llm_load_print_meta: model params     = 6.74 B\n",
+      "llm_load_print_meta: model size       = 3.56 GiB (4.54 BPW) \n",
+      "llm_load_print_meta: general.name   = LLaMA v2\n",
+      "llm_load_print_meta: BOS token = 1 '<s>'\n",
+      "llm_load_print_meta: EOS token = 2 '</s>'\n",
+      "llm_load_print_meta: UNK token = 0 '<unk>'\n",
+      "llm_load_print_meta: LF token  = 13 '<0x0A>'\n",
+      "llm_load_tensors: ggml ctx size =    0.11 MB\n",
+      "llm_load_tensors: mem required  = 3647.97 MB\n",
+      "..................................................................................................\n",
+      "llama_new_context_with_model: n_ctx      = 512\n",
+      "llama_new_context_with_model: freq_base  = 10000.0\n",
+      "llama_new_context_with_model: freq_scale = 1\n",
+      "llama_new_context_with_model: kv self size  =  256.00 MB\n",
+      "llama_build_graph: non-view tensors processed: 740/740\n",
+      "llama_new_context_with_model: compute buffer total size = 2.66 MB\n",
+      "AVX = 1 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 0 | AVX512_VNNI = 1 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | \n"
+     ]
+    }
+   ],
+   "source": [
+    "from os.path import expanduser\n",
+    "\n",
+    "from langchain.llms import LlamaCpp\n",
+    "\n",
+    "model_path = expanduser(\"~/Models/llama-2-7b-chat.Q4_0.gguf\")\n",
+    "\n",
+    "llm = LlamaCpp(\n",
+    "    model_path=model_path,\n",
+    "    streaming=False,\n",
+    ")\n",
+    "model = Llama2Chat(llm=llm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50498d96",
+   "metadata": {},
+   "source": [
+    "and used in the same way as in the previous example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "90782b96",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "memory = ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n",
+    "chain = LLMChain(llm=model, prompt=prompt_template, memory=memory)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "2160b26d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Of course! Vienna is a beautiful city with a rich history and culture. Here are some of the top tourist attractions you might want to consider visiting:\n",
+      "1. Schönbrunn Palace\n",
+      "2. St. Stephen's Cathedral\n",
+      "3. Hofburg Palace\n",
+      "4. Belvedere Palace\n",
+      "5. Prater Park\n",
+      "6. MuseumsQuartier\n",
+      "7. Ringstrasse\n",
+      "8. Vienna State Opera\n",
+      "9. Kunsthistorisches Museum\n",
+      "10. Imperial Palace\n",
+      "\n",
+      "These are just a few of the many amazing places to see in Vienna. Each one has its own unique history and charm, so I hope you enjoy exploring this beautiful city!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "llama_print_timings:        load time =     250.46 ms\n",
+      "llama_print_timings:      sample time =      56.40 ms /   144 runs   (    0.39 ms per token,  2553.37 tokens per second)\n",
+      "llama_print_timings: prompt eval time =    1444.25 ms /    47 tokens (   30.73 ms per token,    32.54 tokens per second)\n",
+      "llama_print_timings:        eval time =    8832.02 ms /   143 runs   (   61.76 ms per token,    16.19 tokens per second)\n",
+      "llama_print_timings:       total time =   10645.94 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\n",
+    "    chain.run(\n",
+    "        text=\"What can I see in Vienna? Propose a few locations. Names only, no details.\"\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d9ce06e3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Llama.generate: prefix-match hit\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Of course! St. Stephen's Cathedral (also known as Stephansdom) is a stunning Gothic-style cathedral located in the heart of Vienna, Austria. It is one of the most recognizable landmarks in the city and is considered a symbol of Vienna.\n",
+      "Here are some interesting facts about St. Stephen's Cathedral:\n",
+      "1. History: The construction of St. Stephen's Cathedral began in the 12th century on the site of a former Romanesque church, and it took over 600 years to complete. The cathedral has been renovated and expanded several times throughout its history, with the most significant renovation taking place in the 19th century.\n",
+      "2. Architecture: St. Stephen's Cathedral is built in the Gothic style, characterized by its tall spires, pointed arches, and intricate stone carvings. The cathedral features a mix of Romanesque, Gothic, and Baroque elements, making it a unique blend of styles.\n",
+      "3. Design: The cathedral's design is based on the plan of a cross with a long nave and two shorter arms extending from it. The main altar is\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "llama_print_timings:        load time =     250.46 ms\n",
+      "llama_print_timings:      sample time =     100.60 ms /   256 runs   (    0.39 ms per token,  2544.73 tokens per second)\n",
+      "llama_print_timings: prompt eval time =    5128.71 ms /   160 tokens (   32.05 ms per token,    31.20 tokens per second)\n",
+      "llama_print_timings:        eval time =   16193.02 ms /   255 runs   (   63.50 ms per token,    15.75 tokens per second)\n",
+      "llama_print_timings:       total time =   21988.57 ms\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(chain.run(text=\"Tell me more about #2.\"))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/chat/llama_api.ipynb

@@ -1,11 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "71b5cfca",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Llama API\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "90a1faf2",
    "metadata": {},
    "source": [
-    "# Llama API\n",
+    "# ChatLlamaAPI\n",
     "\n",
     "This notebook shows how to use LangChain with [LlamaAPI](https://llama-api.com/) - a hosted version of Llama2 that adds in support for function calling."
    ]

docs/docs/integrations/chat/minimax.ipynb

@@ -1,11 +1,20 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: MiniMax\n",
+    "---"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# MiniMax\n",
+    "# MiniMaxChat\n",
     "\n",
     "[Minimax](https://api.minimax.chat) is a Chinese startup that provides LLM service for companies and individuals.\n",
     "\n",

docs/docs/integrations/chat/ollama.ipynb

@@ -1,10 +1,19 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Ollama\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Ollama\n",
+    "# ChatOllama\n",
     "\n",
     "[Ollama](https://ollama.ai/) allows you to run open-source large language models, such as LLaMA2, locally.\n",
     "\n",
@@ -119,6 +128,159 @@
     "chat_model(messages)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extraction\n",
+    " \n",
+    "Update your version of Ollama and supply the [`format`](https://github.com/jmorganca/ollama/blob/main/docs/api.md#json-mode) flag.\n",
+    "\n",
+    "We can enforce the model to produce JSON.\n",
+    "\n",
+    "**Note:** You can also try out the experimental [OllamaFunctions](https://python.langchain.com/docs/integrations/chat/ollama_functions) wrapper for convenience."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain.chat_models import ChatOllama\n",
+    "\n",
+    "chat_model = ChatOllama(\n",
+    "    model=\"llama2\",\n",
+    "    format=\"json\",\n",
+    "    callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Sure! Here's a JSON response with the colors of the sky at different times of the day:\n",
+      " Begriffe und Abkürzungen:\n",
+      "\n",
+      "* `time`: The time of day (in 24-hour format)\n",
+      "* `sky_color`: The color of the sky at that time (as a hex code)\n",
+      "\n",
+      "Here are the colors of the sky at different times of the day:\n",
+      "```json\n",
+      "[\n",
+      "  {\n",
+      "    \"time\": \"6am\",\n",
+      "    \"sky_color\": \"#0080c0\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"time\": \"9am\",\n",
+      "    \"sky_color\": \"#3498db\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"time\": \"12pm\",\n",
+      "    \"sky_color\": \"#ef7c00\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"time\": \"3pm\",\n",
+      "    \"sky_color\": \"#9564b6\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"time\": \"6pm\",\n",
+      "    \"sky_color\": \"#e78ac3\"\n",
+      "  },\n",
+      "  {\n",
+      "    \"time\": \"9pm\",\n",
+      "    \"sky_color\": \"#5f006a\"\n",
+      "  }\n",
+      "]\n",
+      "```\n",
+      "In this response, the `time` property is a string in 24-hour format, representing the time of day. The `sky_color` property is a hex code representing the color of the sky at that time. For example, at 6am, the sky is blue (#0080c0), while at 9pm, it's dark blue (#5f006a)."
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.schema import HumanMessage\n",
+    "\n",
+    "messages = [\n",
+    "    HumanMessage(\n",
+    "        content=\"What color is the sky at different times of the day? Respond using JSON\"\n",
+    "    )\n",
+    "]\n",
+    "\n",
+    "chat_model_response = chat_model(messages)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Sure! Based on the JSON schema you provided, here's the information we can gather about a person named John who is 35 years old and loves pizza:\n",
+      "\n",
+      "**Name:** John\n",
+      "\n",
+      "**Age:** 35 (integer)\n",
+      "\n",
+      "**Favorite food:** Pizza (string)\n",
+      "\n",
+      "So, the JSON object for John would look like this:\n",
+      "```json\n",
+      "{\n",
+      "  \"name\": \"John\",\n",
+      "  \"age\": 35,\n",
+      "  \"fav_food\": \"pizza\"\n",
+      "}\n",
+      "```\n",
+      "Note that we cannot provide additional information about John beyond what is specified in the schema. For example, we do not have any information about his gender, occupation, or address, as those fields are not included in the schema."
+     ]
+    }
+   ],
+   "source": [
+    "import json\n",
+    "\n",
+    "from langchain.schema import HumanMessage\n",
+    "\n",
+    "json_schema = {\n",
+    "    \"title\": \"Person\",\n",
+    "    \"description\": \"Identifying information about a person.\",\n",
+    "    \"type\": \"object\",\n",
+    "    \"properties\": {\n",
+    "        \"name\": {\"title\": \"Name\", \"description\": \"The person's name\", \"type\": \"string\"},\n",
+    "        \"age\": {\"title\": \"Age\", \"description\": \"The person's age\", \"type\": \"integer\"},\n",
+    "        \"fav_food\": {\n",
+    "            \"title\": \"Fav Food\",\n",
+    "            \"description\": \"The person's favorite food\",\n",
+    "            \"type\": \"string\",\n",
+    "        },\n",
+    "    },\n",
+    "    \"required\": [\"name\", \"age\"],\n",
+    "}\n",
+    "\n",
+    "messages = [\n",
+    "    HumanMessage(\n",
+    "        content=\"Please tell me about a person using the following JSON schema:\"\n",
+    "    ),\n",
+    "    HumanMessage(content=json.dumps(json_schema, indent=2)),\n",
+    "    HumanMessage(\n",
+    "        content=\"Now, considering the schema, tell me about a person named John who is 35 years old and loves pizza.\"\n",
+    "    ),\n",
+    "]\n",
+    "\n",
+    "chat_model_response = chat_model(messages)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -375,5 +537,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/docs/integrations/chat/ollama_functions.ipynb

@@ -0,0 +1,180 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Ollama Functions\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# OllamaFunctions\n",
+    "\n",
+    "This notebook shows how to use an experimental wrapper around Ollama that gives it the same API as OpenAI Functions.\n",
+    "\n",
+    "Note that more powerful and capable models will perform better with complex schema and/or multiple functions. The examples below use Mistral.\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",
+    "Follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance.\n",
+    "\n",
+    "## Usage\n",
+    "\n",
+    "You can initialize OllamaFunctions in a similar way to how you'd initialize a standard ChatOllama instance:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain_experimental.llms.ollama_functions import OllamaFunctions\n",
+    "\n",
+    "model = OllamaFunctions(model=\"mistral\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can then bind functions defined with JSON Schema parameters and a `function_call` parameter to force the model to call the given function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = model.bind(\n",
+    "    functions=[\n",
+    "        {\n",
+    "            \"name\": \"get_current_weather\",\n",
+    "            \"description\": \"Get the current weather in a given location\",\n",
+    "            \"parameters\": {\n",
+    "                \"type\": \"object\",\n",
+    "                \"properties\": {\n",
+    "                    \"location\": {\n",
+    "                        \"type\": \"string\",\n",
+    "                        \"description\": \"The city and state, \" \"e.g. San Francisco, CA\",\n",
+    "                    },\n",
+    "                    \"unit\": {\n",
+    "                        \"type\": \"string\",\n",
+    "                        \"enum\": [\"celsius\", \"fahrenheit\"],\n",
+    "                    },\n",
+    "                },\n",
+    "                \"required\": [\"location\"],\n",
+    "            },\n",
+    "        }\n",
+    "    ],\n",
+    "    function_call={\"name\": \"get_current_weather\"},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calling a function with this model then results in JSON output matching the provided schema:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='', additional_kwargs={'function_call': {'name': 'get_current_weather', 'arguments': '{\"location\": \"Boston, MA\", \"unit\": \"celsius\"}'}})"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.schema import HumanMessage\n",
+    "\n",
+    "model.invoke(\"what is the weather in Boston?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using for extraction\n",
+    "\n",
+    "One useful thing you can do with function calling here is extracting properties from a given input in a structured format:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'Alex', 'height': 5, 'hair_color': 'blonde'},\n",
+       " {'name': 'Claudia', 'height': 6, 'hair_color': 'brunette'}]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.chains import create_extraction_chain\n",
+    "\n",
+    "# Schema\n",
+    "schema = {\n",
+    "    \"properties\": {\n",
+    "        \"name\": {\"type\": \"string\"},\n",
+    "        \"height\": {\"type\": \"integer\"},\n",
+    "        \"hair_color\": {\"type\": \"string\"},\n",
+    "    },\n",
+    "    \"required\": [\"name\", \"height\"],\n",
+    "}\n",
+    "\n",
+    "# Input\n",
+    "input = \"\"\"Alex is 5 feet tall. Claudia is 1 feet taller than Alex and jumps higher than him. Claudia is a brunette and Alex is blonde.\"\"\"\n",
+    "\n",
+    "# Run chain\n",
+    "llm = OllamaFunctions(model=\"mistral\", temperature=0)\n",
+    "chain = create_extraction_chain(schema, llm)\n",
+    "chain.run(input)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

docs/docs/integrations/chat/openai.ipynb

@@ -1,11 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "afaf8039",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: OpenAI\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "e49f1e0d",
    "metadata": {},
    "source": [
-    "# OpenAI\n",
+    "# ChatOpenAI\n",
     "\n",
     "This notebook covers how to get started with OpenAI chat models."
    ]

docs/docs/integrations/chat/pai_eas_chat_endpoint.ipynb

@@ -1,10 +1,19 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: AliCloud PAI EAS\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# AliCloud PAI EAS\n",
+    "# PaiEasChatEndpoint\n",
     "Machine Learning Platform for AI of Alibaba Cloud is a machine learning or deep learning engineering platform intended for enterprises and developers. It provides easy-to-use, cost-effective, high-performance, and easy-to-scale plug-ins that can be applied to various industry scenarios. With over 140 built-in optimization algorithms, Machine Learning Platform for AI provides whole-process AI engineering capabilities including data labeling (PAI-iTAG), model building (PAI-Designer and PAI-DSW), model training (PAI-DLC), compilation optimization, and inference deployment (PAI-EAS). PAI-EAS supports different types of hardware resources, including CPUs and GPUs, and features high throughput and low latency. It allows you to deploy large-scale complex models with a few clicks and perform elastic scale-ins and scale-outs in real time. It also provides a comprehensive O&M and monitoring system."
    ]
   },

docs/docs/integrations/chat/promptlayer_chatopenai.ipynb

@@ -1,12 +1,22 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "ce3672d3",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: PromptLayer ChatOpenAI\n",
+    "---"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
    "id": "959300d4",
    "metadata": {},
    "source": [
-    "# PromptLayer ChatOpenAI\n",
+    "# PromptLayerChatOpenAI\n",
     "\n",
     "This example showcases how to connect to [PromptLayer](https://www.promptlayer.com) to start recording your ChatOpenAI requests."
    ]

docs/docs/integrations/chat/tongyi.ipynb

@@ -1,5 +1,14 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Tongyi Qwen\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -9,7 +18,7 @@
     }
    },
    "source": [
-    "# Tongyi Qwen\n",
+    "# ChatTongyi\n",
     "Tongyi Qwen is a large language model developed by Alibaba's Damo Academy. It is capable of understanding user intent through natural language understanding and semantic analysis, based on user input in natural language. It provides services and assistance to users in different domains and tasks. By providing clear and detailed instructions, you can obtain results that better align with your expectations.\n",
     "In this notebook, we will introduce how to use langchain with [Tongyi](https://www.aliyun.com/product/dashscope) mainly in `Chat` corresponding\n",
     " to the package `langchain/chat_models` in langchain"
@@ -41,7 +50,7 @@
    },
    "outputs": [
     {
-     "name": "stdin",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
       " ········\n"

docs/docs/integrations/chat/vllm.ipynb

@@ -1,5 +1,15 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "eb65deaa",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: vLLM Chat\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "eb7e5679-aa06-47e4-a1a3-b6b70e604017",

docs/docs/integrations/chat/volcengine_maas.ipynb

@@ -0,0 +1,191 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "66107bdd",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Volc Enging Maas\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "404758628c7b20f6",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# VolcEngineMaasChat\n",
+    "\n",
+    "This notebook provides you with a guide on how to get started with volc engine maas chat models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2cd2ebd9d023c4d3",
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "# Install the package\n",
+    "!pip install volcengine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "51e7f967cb78f5b7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-27T10:43:37.131292Z",
+     "start_time": "2023-11-27T10:43:37.127250Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.chat_models import VolcEngineMaasChat\n",
+    "from langchain.schema import HumanMessage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "139667d44689f9e0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-27T10:43:49.911867Z",
+     "start_time": "2023-11-27T10:43:49.908329Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "chat = VolcEngineMaasChat(volc_engine_maas_ak=\"your ak\", volc_engine_maas_sk=\"your sk\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e84ebc4feedcc739",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "or you can set access_key and secret_key in your environment variables\n",
+    "```bash\n",
+    "export VOLC_ACCESSKEY=YOUR_AK\n",
+    "export VOLC_SECRETKEY=YOUR_SK\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "35da18414ad17aa0",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-27T10:43:53.101852Z",
+     "start_time": "2023-11-27T10:43:51.741041Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='好的，这是一个笑话：\\n\\n为什么鸟儿不会玩电脑游戏？\\n\\n因为它们没有翅膀！')"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat([HumanMessage(content=\"给我讲个笑话\")])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a55e5a9ed80ec49e",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "# volc engine maas chat with stream"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "b4e4049980ac68ef",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-27T10:43:55.120405Z",
+     "start_time": "2023-11-27T10:43:55.114707Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "chat = VolcEngineMaasChat(\n",
+    "    volc_engine_maas_ak=\"your ak\",\n",
+    "    volc_engine_maas_sk=\"your sk\",\n",
+    "    streaming=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "fe709a4ffb5c811d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-11-27T10:43:58.775294Z",
+     "start_time": "2023-11-27T10:43:56.799401Z"
+    },
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='好的，这是一个笑话：\\n\\n三岁的女儿说她会造句了，妈妈让她用“年轻”造句，女儿说：“妈妈减肥，一年轻了好几斤”。')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chat([HumanMessage(content=\"给我讲个笑话\")])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/chat/yandex.ipynb

@@ -1,11 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "b4154fbe",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: YandexGPT\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "af63c9db-e4bd-4d3b-a4d7-7927f5541734",
    "metadata": {},
    "source": [
-    "# YandexGPT\n",
+    "# ChatYandexGPT\n",
     "\n",
     "This notebook goes over how to use Langchain with [YandexGPT](https://cloud.yandex.com/en/services/yandexgpt) chat model.\n",
     "\n",

docs/docs/integrations/chat_loaders/discord.ipynb

@@ -30,7 +30,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Overwriting discord_chats.txt\n"
+      "Writing discord_chats.txt\n"
      ]
     }
    ],
@@ -240,14 +240,14 @@
     {
      "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)]}]"
+       "[{'messages': [AIMessage(content='Love music! Do you like jazz?', additional_kwargs={'sender': 'talkingtower', 'events': [{'message_time': '08/15/2023 11:10 AM\\n'}]}),\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'}]}),\n",
+       "   AIMessage(content='Indeed! Great choice. 🎷', additional_kwargs={'sender': 'talkingtower', 'events': [{'message_time': 'Yesterday at 5:03 AM\\n'}]}),\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'}]}),\n",
+       "   AIMessage(content=\"Sounds fun! Let's explore.\", additional_kwargs={'sender': 'talkingtower', 'events': [{'message_time': 'Today at 2:38 PM\\n'}]}),\n",
+       "   HumanMessage(content='Enjoy the tour! See you around.', additional_kwargs={'sender': 'reporterbob', 'events': [{'message_time': 'Today at 2:56 PM\\n'}]}),\n",
+       "   AIMessage(content='Thank you! Goodbye! 👋', additional_kwargs={'sender': 'talkingtower', 'events': [{'message_time': 'Today at 3:00 PM\\n'}]}),\n",
+       "   HumanMessage(content='Farewell! Happy exploring.', additional_kwargs={'sender': 'reporterbob', 'events': [{'message_time': 'Today at 3:02 PM\\n'}]})]}]"
       ]
      },
      "execution_count": 5,
@@ -279,7 +279,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Thank you! Have a wonderful day! 🌟"
+      "Thank you! Have a great day!"
      ]
     }
    ],
@@ -317,7 +317,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat_loaders/facebook.ipynb

@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "647f2158-a42e-4634-b283-b8492caf542a",
    "metadata": {},
    "outputs": [
@@ -91,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "a0869bc6",
    "metadata": {},
    "outputs": [],
@@ -114,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "id": "f61ee277",
    "metadata": {},
    "outputs": [],
@@ -126,19 +126,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "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)]"
+       "[HumanMessage(content=\"Hi Hermione! How's your summer going so far?\", additional_kwargs={'sender': 'Harry Potter'}),\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'}),\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'})]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -150,7 +150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "id": "8a3ee473",
    "metadata": {},
    "outputs": [],
@@ -162,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "id": "9f41e122",
    "metadata": {},
    "outputs": [
@@ -172,7 +172,7 @@
        "9"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "id": "5a78030d-b757-4bbe-8a6c-841056f46df7",
    "metadata": {},
    "outputs": [],
@@ -209,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 9,
    "id": "ff35b028-78bf-4c5b-9ec6-939fe67de7f7",
    "metadata": {},
    "outputs": [],
@@ -220,19 +220,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 10,
    "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)]"
+       "[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'}),\n",
+       " HumanMessage(content=\"What is it, Potter? I'm quite busy at the moment.\", additional_kwargs={'sender': 'Severus Snape'}),\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'})]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -253,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 11,
    "id": "21372331",
    "metadata": {},
    "outputs": [],
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 12,
    "id": "92c5ae7a",
    "metadata": {},
    "outputs": [
@@ -282,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 13,
    "id": "dfcbd181",
    "metadata": {
     "scrolled": true
@@ -299,7 +299,7 @@
        "  '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,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -321,7 +321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 14,
    "id": "13cd290a-b1e9-4686-bb5e-d99de8b8612b",
    "metadata": {},
    "outputs": [
@@ -331,7 +331,7 @@
        "100"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -364,7 +364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 15,
    "id": "95ce3f63-3c80-44b2-9060-534ad74e16fa",
    "metadata": {},
    "outputs": [],
@@ -374,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 16,
    "id": "ab9e28eb",
    "metadata": {},
    "outputs": [
@@ -382,7 +382,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "File file-zCyNBeg4snpbBL7VkvsuhCz8 ready afer 30.55 seconds.\n"
+      "File file-ULumAXLEFw3vB6bb9uy6DNVC ready after 0.00 seconds.\n"
      ]
     }
    ],
@@ -399,16 +399,16 @@
     "    my_file.write((json.dumps({\"messages\": m}) + \"\\n\").encode(\"utf-8\"))\n",
     "\n",
     "my_file.seek(0)\n",
-    "training_file = openai.File.create(file=my_file, purpose=\"fine-tune\")\n",
+    "training_file = openai.files.create(file=my_file, purpose=\"fine-tune\")\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",
+    "status = openai.files.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",
+    "    status = openai.files.retrieve(training_file.id).status\n",
     "print(f\"File {training_file.id} ready after {time.time() - start_time:.2f} seconds.\")"
    ]
   },
@@ -422,12 +422,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 17,
    "id": "3f451425",
    "metadata": {},
    "outputs": [],
    "source": [
-    "job = openai.FineTuningJob.create(\n",
+    "job = openai.fine_tuning.jobs.create(\n",
     "    training_file=training_file.id,\n",
     "    model=\"gpt-3.5-turbo\",\n",
     ")"
@@ -443,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 18,
    "id": "bac1637a-c087-4523-ade1-c47f9bf4c6f4",
    "metadata": {},
    "outputs": [
@@ -451,23 +451,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Status=[running]... 908.87s\r"
+      "Status=[running]... 874.29s. 56.93s\r"
      ]
     }
    ],
    "source": [
-    "status = openai.FineTuningJob.retrieve(job.id).status\n",
+    "status = openai.fine_tuning.jobs.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",
+    "    job = openai.fine_tuning.jobs.retrieve(job.id)\n",
     "    status = job.status"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 19,
    "id": "535895e1-bc69-40e5-82ed-e24ed2baeeee",
    "metadata": {},
    "outputs": [
@@ -475,7 +475,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ft:gpt-3.5-turbo-0613:personal::7rDwkaOq\n"
+      "ft:gpt-3.5-turbo-0613:personal::8QnAzWMr\n"
      ]
     }
    ],
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 20,
    "id": "3925d60d",
    "metadata": {},
    "outputs": [],
@@ -510,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 21,
    "id": "7190cf2e-ab34-4ceb-bdad-45f24f069c29",
    "metadata": {},
    "outputs": [],
@@ -529,7 +529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 22,
    "id": "f02057e9-f914-40b1-9c9d-9432ff594b98",
    "metadata": {},
    "outputs": [
@@ -537,7 +537,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The usual - Potions, Transfiguration, Defense Against the Dark Arts. What about you?"
+      "I'm taking Charms, Defense Against the Dark Arts, Herbology, Potions, Transfiguration, and Ancient Runes. How about you?"
      ]
     }
    ],
@@ -545,14 +545,6 @@
     "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": {
@@ -571,7 +563,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat_loaders/imessage.ipynb

@@ -243,16 +243,16 @@
     "    my_file.write((json.dumps({\"messages\": m}) + \"\\n\").encode(\"utf-8\"))\n",
     "\n",
     "my_file.seek(0)\n",
-    "training_file = openai.File.create(file=my_file, purpose=\"fine-tune\")\n",
+    "training_file = openai.files.create(file=my_file, purpose=\"fine-tune\")\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",
+    "status = openai.files.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",
+    "    status = openai.files.retrieve(training_file.id).status\n",
     "print(f\"File {training_file.id} ready after {time.time() - start_time:.2f} seconds.\")"
    ]
   },
@@ -271,7 +271,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "job = openai.FineTuningJob.create(\n",
+    "job = openai.fine_tuning.jobs.create(\n",
     "    training_file=training_file.id,\n",
     "    model=\"gpt-3.5-turbo\",\n",
     ")"
@@ -300,12 +300,12 @@
     }
    ],
    "source": [
-    "status = openai.FineTuningJob.retrieve(job.id).status\n",
+    "status = openai.fine_tuning.jobs.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",
+    "    job = openai.fine_tuning.jobs.retrieve(job.id)\n",
     "    status = job.status"
    ]
   },
@@ -416,7 +416,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat_loaders/langsmith_dataset.ipynb

@@ -123,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "id": "817bc077-c18a-473b-94a4-a7d810d583a8",
    "metadata": {},
    "outputs": [],
@@ -145,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "id": "9e5ac127-b094-4584-9159-5a6d3d7315c7",
    "metadata": {},
    "outputs": [],
@@ -166,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "11d19e28-be49-4801-8065-1a58d13cd192",
    "metadata": {},
    "outputs": [
@@ -174,7 +174,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Status=[running]... 302.42s. 143.85s\r"
+      "Status=[running]... 429.55s. 46.34s\r"
      ]
     }
    ],
@@ -190,20 +190,20 @@
     "    my_file.write((json.dumps({\"messages\": dialog}) + \"\\n\").encode(\"utf-8\"))\n",
     "\n",
     "my_file.seek(0)\n",
-    "training_file = openai.File.create(file=my_file, purpose=\"fine-tune\")\n",
+    "training_file = openai.files.create(file=my_file, purpose=\"fine-tune\")\n",
     "\n",
-    "job = openai.FineTuningJob.create(\n",
+    "job = openai.fine_tuning.jobs.create(\n",
     "    training_file=training_file.id,\n",
     "    model=\"gpt-3.5-turbo\",\n",
     ")\n",
     "\n",
     "# Wait for the fine-tuning to complete (this may take some time)\n",
-    "status = openai.FineTuningJob.retrieve(job.id).status\n",
+    "status = openai.fine_tuning.jobs.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",
-    "    status = openai.FineTuningJob.retrieve(job.id).status\n",
+    "    status = openai.fine_tuning.jobs.retrieve(job.id).status\n",
     "\n",
     "# Now your model is fine-tuned!"
    ]
@@ -220,16 +220,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "3f472ca4-fa9b-485d-bd37-8ce3c59c44db",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Get the fine-tuned model ID\n",
-    "job = openai.FineTuningJob.retrieve(job.id)\n",
+    "job = openai.fine_tuning.jobs.retrieve(job.id)\n",
     "model_id = job.fine_tuned_model\n",
     "\n",
     "# Use the fine-tuned model in LangChain\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "\n",
     "model = ChatOpenAI(\n",
     "    model=model_id,\n",
     "    temperature=1,\n",
@@ -238,10 +240,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "7d3b5845-6385-42d1-9f7d-5ea798dc2cd9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AIMessage(content='[{\"s\": \"There were three ravens\", \"object\": \"tree\", \"relation\": \"sat on\"}, {\"s\": \"three ravens\", \"object\": \"a tree\", \"relation\": \"sat on\"}]')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "model.invoke(\"There were three ravens sat on a tree.\")"
    ]
@@ -271,7 +284,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

docs/docs/integrations/chat_loaders/langsmith_llm_runs.ipynb

@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "473adce5-c863-49e6-85c3-049e0ec2222e",
    "metadata": {},
    "outputs": [],
@@ -65,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "9a36d27f-2f3b-4148-b94a-9436fe8b00e0",
    "metadata": {},
    "outputs": [],
@@ -105,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "89bcc676-27e8-40dc-a4d6-92cf28e0db58",
    "metadata": {},
    "outputs": [
@@ -144,7 +144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "cd44ff01-22cf-431a-8bf4-29a758d1fcff",
    "metadata": {},
    "outputs": [],
@@ -169,18 +169,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "62da7d8f-5cfc-45a6-946e-2bcda2b0ba1f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Retrying langchain.chat_models.openai.ChatOpenAI.completion_with_retry.<locals>._completion_with_retry in 4.0 seconds as it raised ServiceUnavailableError: The server is overloaded or not ready yet..\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "math_questions = [\n",
     "    \"What's 45/9?\",\n",
@@ -219,7 +211,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "d6037992-050d-4ada-a061-860c124f0bf1",
    "metadata": {},
    "outputs": [],
@@ -231,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "0444919a-6f5a-4726-9916-4603b1420d0e",
    "metadata": {},
    "outputs": [],
@@ -266,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "817bc077-c18a-473b-94a4-a7d810d583a8",
    "metadata": {},
    "outputs": [],
@@ -288,7 +280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "9e5ac127-b094-4584-9159-5a6d3d7315c7",
    "metadata": {},
    "outputs": [],
@@ -309,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "11d19e28-be49-4801-8065-1a58d13cd192",
    "metadata": {},
    "outputs": [
@@ -317,7 +309,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Status=[running]... 346.26s. 31.70s\r"
+      "Status=[running]... 349.84s. 17.72s\r"
      ]
     }
    ],
@@ -333,20 +325,20 @@
     "    my_file.write((json.dumps({\"messages\": dialog}) + \"\\n\").encode(\"utf-8\"))\n",
     "\n",
     "my_file.seek(0)\n",
-    "training_file = openai.File.create(file=my_file, purpose=\"fine-tune\")\n",
+    "training_file = openai.files.create(file=my_file, purpose=\"fine-tune\")\n",
     "\n",
-    "job = openai.FineTuningJob.create(\n",
+    "job = openai.fine_tuning.jobs.create(\n",
     "    training_file=training_file.id,\n",
     "    model=\"gpt-3.5-turbo\",\n",
     ")\n",
     "\n",
     "# Wait for the fine-tuning to complete (this may take some time)\n",
-    "status = openai.FineTuningJob.retrieve(job.id).status\n",
+    "status = openai.fine_tuning.jobs.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",
-    "    status = openai.FineTuningJob.retrieve(job.id).status\n",
+    "    status = openai.fine_tuning.jobs.retrieve(job.id).status\n",
     "\n",
     "# Now your model is fine-tuned!"
    ]
@@ -363,16 +355,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "7f45b281-1dfa-43cb-bd28-99fa7e9f45d1",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Get the fine-tuned model ID\n",
-    "job = openai.FineTuningJob.retrieve(job.id)\n",
+    "job = openai.fine_tuning.jobs.retrieve(job.id)\n",
     "model_id = job.fine_tuned_model\n",
     "\n",
     "# Use the fine-tuned model in LangChain\n",
+    "from langchain.chat_models import ChatOpenAI\n",
+    "\n",
     "model = ChatOpenAI(\n",
     "    model=model_id,\n",
     "    temperature=1,\n",
@@ -381,17 +375,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 12,
    "id": "7d3b5845-6385-42d1-9f7d-5ea798dc2cd9",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content='{\\n  \"num1\": 56,\\n  \"num2\": 7,\\n  \"operation\": \"/\"\\n}')"
+       "AIMessage(content='Let me calculate that for you.')"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -425,7 +419,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/amazon_textract.ipynb

@@ -0,0 +1,884 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1f3cebbe-079a-4bfe-b1a1-07bdac882ce2",
+   "metadata": {},
+   "source": [
+    "# Amazon Textract \n",
+    "\n",
+    ">[Amazon Textract](https://docs.aws.amazon.com/managedservices/latest/userguide/textract.html) is a machine learning (ML) service that automatically extracts text, handwriting, and data from scanned documents.\n",
+    ">\n",
+    ">It goes beyond simple optical character recognition (OCR) to identify, understand, and extract data from forms and tables. Today, many companies manually extract data from scanned documents such as PDFs, images, tables, and forms, or through simple OCR software that requires manual configuration (which often must be updated when the form changes). To overcome these manual and expensive processes, `Textract` uses ML to read and process any type of document, accurately extracting text, handwriting, tables, and other data with no manual effort. \n",
+    "\n",
+    "This sample demonstrates the use of `Amazon Textract` in combination with LangChain as a DocumentLoader.\n",
+    "\n",
+    "`Textract` supports`PDF`, `TIF`F, `PNG` and `JPEG` format.\n",
+    "\n",
+    "`Textract` supports these [document sizes, languages and characters](https://docs.aws.amazon.com/textract/latest/dg/limits-document.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a1aa66d4-85f2-42ad-a8d3-de7cea8d6c35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install boto3 openai tiktoken python-dotenv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e4305a0d-37da-41f9-a52c-7d166d7dbabf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install \"amazon-textract-caller>=0.2.0\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "400b25c6-befa-4730-a201-39ff112c8858",
+   "metadata": {},
+   "source": [
+    "## Sample 1\n",
+    "\n",
+    "The first example uses a local file, which internally will be send to Amazon Textract sync API [DetectDocumentText](https://docs.aws.amazon.com/textract/latest/dg/API_DetectDocumentText.html). \n",
+    "\n",
+    "Local files or URL endpoints like HTTP:// are limited to one page documents for Textract.\n",
+    "Multi-page documents have to reside on S3. This sample file is a jpeg."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1becee92-e82f-42d4-9b4e-b23d77cbe88d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import AmazonTextractPDFLoader\n",
+    "\n",
+    "loader = AmazonTextractPDFLoader(\"example_data/alejandro_rosalez_sample-small.jpeg\")\n",
+    "documents = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d566dc56-c9a9-44ec-84fb-a81928f90d40",
+   "metadata": {},
+   "source": [
+    "Output from the file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1272ce8c-d298-4059-ac0a-780bf5f82302",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='Patient Information First Name: ALEJANDRO Last Name: ROSALEZ Date of Birth: 10/10/1982 Sex: M Marital Status: MARRIED Email Address: Address: 123 ANY STREET City: ANYTOWN State: CA Zip Code: 12345 Phone: 646-555-0111 Emergency Contact 1: First Name: CARLOS Last Name: SALAZAR Phone: 212-555-0150 Relationship to Patient: BROTHER Emergency Contact 2: First Name: JANE Last Name: DOE Phone: 650-555-0123 Relationship FRIEND to Patient: Did you feel fever or feverish lately? Yes No Are you having shortness of breath? Yes No Do you have a cough? Yes No Did you experience loss of taste or smell? Yes No Where you in contact with any confirmed COVID-19 positive patients? Yes No Did you travel in the past 14 days to any regions affected by COVID-19? Yes No Patient Information First Name: ALEJANDRO Last Name: ROSALEZ Date of Birth: 10/10/1982 Sex: M Marital Status: MARRIED Email Address: Address: 123 ANY STREET City: ANYTOWN State: CA Zip Code: 12345 Phone: 646-555-0111 Emergency Contact 1: First Name: CARLOS Last Name: SALAZAR Phone: 212-555-0150 Relationship to Patient: BROTHER Emergency Contact 2: First Name: JANE Last Name: DOE Phone: 650-555-0123 Relationship FRIEND to Patient: Did you feel fever or feverish lately? Yes No Are you having shortness of breath? Yes No Do you have a cough? Yes No Did you experience loss of taste or smell? Yes No Where you in contact with any confirmed COVID-19 positive patients? Yes No Did you travel in the past 14 days to any regions affected by COVID-19? Yes No ', metadata={'source': 'example_data/alejandro_rosalez_sample-small.jpeg', 'page': 1})]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "documents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4cf7f19c-3635-453a-9c76-4baf98b8d7f4",
+   "metadata": {},
+   "source": [
+    "## Sample 2\n",
+    "The next sample loads a file from an HTTPS endpoint. \n",
+    "It has to be single page, as Amazon Textract requires all multi-page documents to be stored on S3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "10374bfb-b325-451f-8bd0-c686710ab68c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from langchain.document_loaders import AmazonTextractPDFLoader\n",
+    "\n",
+    "loader = AmazonTextractPDFLoader(\n",
+    "    \"https://amazon-textract-public-content.s3.us-east-2.amazonaws.com/langchain/alejandro_rosalez_sample_1.jpg\"\n",
+    ")\n",
+    "documents = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "16a2b6a3-7514-4c2c-a427-6847169af473",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='Patient Information First Name: ALEJANDRO Last Name: ROSALEZ Date of Birth: 10/10/1982 Sex: M Marital Status: MARRIED Email Address: Address: 123 ANY STREET City: ANYTOWN State: CA Zip Code: 12345 Phone: 646-555-0111 Emergency Contact 1: First Name: CARLOS Last Name: SALAZAR Phone: 212-555-0150 Relationship to Patient: BROTHER Emergency Contact 2: First Name: JANE Last Name: DOE Phone: 650-555-0123 Relationship FRIEND to Patient: Did you feel fever or feverish lately? Yes No Are you having shortness of breath? Yes No Do you have a cough? Yes No Did you experience loss of taste or smell? Yes No Where you in contact with any confirmed COVID-19 positive patients? Yes No Did you travel in the past 14 days to any regions affected by COVID-19? Yes No Patient Information First Name: ALEJANDRO Last Name: ROSALEZ Date of Birth: 10/10/1982 Sex: M Marital Status: MARRIED Email Address: Address: 123 ANY STREET City: ANYTOWN State: CA Zip Code: 12345 Phone: 646-555-0111 Emergency Contact 1: First Name: CARLOS Last Name: SALAZAR Phone: 212-555-0150 Relationship to Patient: BROTHER Emergency Contact 2: First Name: JANE Last Name: DOE Phone: 650-555-0123 Relationship FRIEND to Patient: Did you feel fever or feverish lately? Yes No Are you having shortness of breath? Yes No Do you have a cough? Yes No Did you experience loss of taste or smell? Yes No Where you in contact with any confirmed COVID-19 positive patients? Yes No Did you travel in the past 14 days to any regions affected by COVID-19? Yes No ', metadata={'source': 'example_data/alejandro_rosalez_sample-small.jpeg', 'page': 1})]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "documents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a9cd8ec-e663-4dc7-9db1-d2f575253141",
+   "metadata": {},
+   "source": [
+    "## Sample 3\n",
+    "\n",
+    "Processing a multi-page document requires the document to be on S3. The sample document resides in a bucket in us-east-2 and Textract needs to be called in that same region to be successful, so we set the region_name on the client and pass that in to the loader to ensure Textract is called from us-east-2. You could also to have your notebook running in us-east-2, setting the AWS_DEFAULT_REGION set to us-east-2 or when running in a different environment, pass in a boto3 Textract client with that region name like in the cell below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "8185e3e6-9599-4a47-8969-d6dcef3e6404",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import boto3\n",
+    "\n",
+    "textract_client = boto3.client(\"textract\", region_name=\"us-east-2\")\n",
+    "\n",
+    "file_path = \"s3://amazon-textract-public-content/langchain/layout-parser-paper.pdf\"\n",
+    "loader = AmazonTextractPDFLoader(file_path, client=textract_client)\n",
+    "documents = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8901eec-070d-4fd6-9d65-52211d332441",
+   "metadata": {},
+   "source": [
+    "Now getting the number of pages to validate the response (printing out the full response would be quite long...). We expect 16 pages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "b23c01c8-cf69-4fe2-8141-4621edb7d79c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(documents)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3e41b4d-b159-4274-89be-80d8159134ef",
+   "metadata": {},
+   "source": [
+    "## Using the AmazonTextractPDFLoader in an LangChain chain (e. g. OpenAI)\n",
+    "\n",
+    "The AmazonTextractPDFLoader can be used in a chain the same way the other loaders are used.\n",
+    "Textract itself does have a [Query feature](https://docs.aws.amazon.com/textract/latest/dg/API_Query.html), which offers similar functionality to the QA chain in this sample, which is worth checking out as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "53c47b24-cc06-4256-9e5b-a82fc80bc55d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You can store your OPENAI_API_KEY in a .env file as well\n",
+    "# import os\n",
+    "# from dotenv import load_dotenv\n",
+    "\n",
+    "# load_dotenv()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a9ae004c-246c-4c7f-8458-191cd7424a9b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Or set the OpenAI key in the environment directly\n",
+    "import os\n",
+    "\n",
+    "os.environ[\"OPENAI_API_KEY\"] = \"your-OpenAI-API-key\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "d52b089c-10ca-45fb-8669-8a1c5fee10d5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' The authors are Zejiang Shen, Ruochen Zhang, Melissa Dell, Benjamin Charles Germain Lee, Jacob Carlson, Weining Li, Gardner, M., Grus, J., Neumann, M., Tafjord, O., Dasigi, P., Liu, N., Peters, M., Schmitz, M., Zettlemoyer, L., Lukasz Garncarek, Powalski, R., Stanislawek, T., Topolski, B., Halama, P., Gralinski, F., Graves, A., Fernández, S., Gomez, F., Schmidhuber, J., Harley, A.W., Ufkes, A., Derpanis, K.G., He, K., Gkioxari, G., Dollár, P., Girshick, R., He, K., Zhang, X., Ren, S., Sun, J., Kay, A., Lamiroy, B., Lopresti, D., Mears, J., Jakeway, E., Ferriter, M., Adams, C., Yarasavage, N., Thomas, D., Zwaard, K., Li, M., Cui, L., Huang,'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from langchain.chains.question_answering import load_qa_chain\n",
+    "from langchain.llms import OpenAI\n",
+    "\n",
+    "chain = load_qa_chain(llm=OpenAI(), chain_type=\"map_reduce\")\n",
+    "query = [\"Who are the autors?\"]\n",
+    "\n",
+    "chain.run(input_documents=documents, question=query)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1a09d18b-ab7b-468e-ae66-f92abf666b9b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "availableInstances": [
+   {
+    "_defaultOrder": 0,
+    "_isFastLaunch": true,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 4,
+    "name": "ml.t3.medium",
+    "vcpuNum": 2
+   },
+   {
+    "_defaultOrder": 1,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 8,
+    "name": "ml.t3.large",
+    "vcpuNum": 2
+   },
+   {
+    "_defaultOrder": 2,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 16,
+    "name": "ml.t3.xlarge",
+    "vcpuNum": 4
+   },
+   {
+    "_defaultOrder": 3,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 32,
+    "name": "ml.t3.2xlarge",
+    "vcpuNum": 8
+   },
+   {
+    "_defaultOrder": 4,
+    "_isFastLaunch": true,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 8,
+    "name": "ml.m5.large",
+    "vcpuNum": 2
+   },
+   {
+    "_defaultOrder": 5,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 16,
+    "name": "ml.m5.xlarge",
+    "vcpuNum": 4
+   },
+   {
+    "_defaultOrder": 6,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 32,
+    "name": "ml.m5.2xlarge",
+    "vcpuNum": 8
+   },
+   {
+    "_defaultOrder": 7,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 64,
+    "name": "ml.m5.4xlarge",
+    "vcpuNum": 16
+   },
+   {
+    "_defaultOrder": 8,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 128,
+    "name": "ml.m5.8xlarge",
+    "vcpuNum": 32
+   },
+   {
+    "_defaultOrder": 9,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 192,
+    "name": "ml.m5.12xlarge",
+    "vcpuNum": 48
+   },
+   {
+    "_defaultOrder": 10,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 256,
+    "name": "ml.m5.16xlarge",
+    "vcpuNum": 64
+   },
+   {
+    "_defaultOrder": 11,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 384,
+    "name": "ml.m5.24xlarge",
+    "vcpuNum": 96
+   },
+   {
+    "_defaultOrder": 12,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 8,
+    "name": "ml.m5d.large",
+    "vcpuNum": 2
+   },
+   {
+    "_defaultOrder": 13,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 16,
+    "name": "ml.m5d.xlarge",
+    "vcpuNum": 4
+   },
+   {
+    "_defaultOrder": 14,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 32,
+    "name": "ml.m5d.2xlarge",
+    "vcpuNum": 8
+   },
+   {
+    "_defaultOrder": 15,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 64,
+    "name": "ml.m5d.4xlarge",
+    "vcpuNum": 16
+   },
+   {
+    "_defaultOrder": 16,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 128,
+    "name": "ml.m5d.8xlarge",
+    "vcpuNum": 32
+   },
+   {
+    "_defaultOrder": 17,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 192,
+    "name": "ml.m5d.12xlarge",
+    "vcpuNum": 48
+   },
+   {
+    "_defaultOrder": 18,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 256,
+    "name": "ml.m5d.16xlarge",
+    "vcpuNum": 64
+   },
+   {
+    "_defaultOrder": 19,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 384,
+    "name": "ml.m5d.24xlarge",
+    "vcpuNum": 96
+   },
+   {
+    "_defaultOrder": 20,
+    "_isFastLaunch": false,
+    "category": "General purpose",
+    "gpuNum": 0,
+    "hideHardwareSpecs": true,
+    "memoryGiB": 0,
+    "name": "ml.geospatial.interactive",
+    "supportedImageNames": [
+     "sagemaker-geospatial-v1-0"
+    ],
+    "vcpuNum": 0
+   },
+   {
+    "_defaultOrder": 21,
+    "_isFastLaunch": true,
+    "category": "Compute optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 4,
+    "name": "ml.c5.large",
+    "vcpuNum": 2
+   },
+   {
+    "_defaultOrder": 22,
+    "_isFastLaunch": false,
+    "category": "Compute optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 8,
+    "name": "ml.c5.xlarge",
+    "vcpuNum": 4
+   },
+   {
+    "_defaultOrder": 23,
+    "_isFastLaunch": false,
+    "category": "Compute optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 16,
+    "name": "ml.c5.2xlarge",
+    "vcpuNum": 8
+   },
+   {
+    "_defaultOrder": 24,
+    "_isFastLaunch": false,
+    "category": "Compute optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 32,
+    "name": "ml.c5.4xlarge",
+    "vcpuNum": 16
+   },
+   {
+    "_defaultOrder": 25,
+    "_isFastLaunch": false,
+    "category": "Compute optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 72,
+    "name": "ml.c5.9xlarge",
+    "vcpuNum": 36
+   },
+   {
+    "_defaultOrder": 26,
+    "_isFastLaunch": false,
+    "category": "Compute optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 96,
+    "name": "ml.c5.12xlarge",
+    "vcpuNum": 48
+   },
+   {
+    "_defaultOrder": 27,
+    "_isFastLaunch": false,
+    "category": "Compute optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 144,
+    "name": "ml.c5.18xlarge",
+    "vcpuNum": 72
+   },
+   {
+    "_defaultOrder": 28,
+    "_isFastLaunch": false,
+    "category": "Compute optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 192,
+    "name": "ml.c5.24xlarge",
+    "vcpuNum": 96
+   },
+   {
+    "_defaultOrder": 29,
+    "_isFastLaunch": true,
+    "category": "Accelerated computing",
+    "gpuNum": 1,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 16,
+    "name": "ml.g4dn.xlarge",
+    "vcpuNum": 4
+   },
+   {
+    "_defaultOrder": 30,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 1,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 32,
+    "name": "ml.g4dn.2xlarge",
+    "vcpuNum": 8
+   },
+   {
+    "_defaultOrder": 31,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 1,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 64,
+    "name": "ml.g4dn.4xlarge",
+    "vcpuNum": 16
+   },
+   {
+    "_defaultOrder": 32,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 1,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 128,
+    "name": "ml.g4dn.8xlarge",
+    "vcpuNum": 32
+   },
+   {
+    "_defaultOrder": 33,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 4,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 192,
+    "name": "ml.g4dn.12xlarge",
+    "vcpuNum": 48
+   },
+   {
+    "_defaultOrder": 34,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 1,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 256,
+    "name": "ml.g4dn.16xlarge",
+    "vcpuNum": 64
+   },
+   {
+    "_defaultOrder": 35,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 1,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 61,
+    "name": "ml.p3.2xlarge",
+    "vcpuNum": 8
+   },
+   {
+    "_defaultOrder": 36,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 4,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 244,
+    "name": "ml.p3.8xlarge",
+    "vcpuNum": 32
+   },
+   {
+    "_defaultOrder": 37,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 8,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 488,
+    "name": "ml.p3.16xlarge",
+    "vcpuNum": 64
+   },
+   {
+    "_defaultOrder": 38,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 8,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 768,
+    "name": "ml.p3dn.24xlarge",
+    "vcpuNum": 96
+   },
+   {
+    "_defaultOrder": 39,
+    "_isFastLaunch": false,
+    "category": "Memory Optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 16,
+    "name": "ml.r5.large",
+    "vcpuNum": 2
+   },
+   {
+    "_defaultOrder": 40,
+    "_isFastLaunch": false,
+    "category": "Memory Optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 32,
+    "name": "ml.r5.xlarge",
+    "vcpuNum": 4
+   },
+   {
+    "_defaultOrder": 41,
+    "_isFastLaunch": false,
+    "category": "Memory Optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 64,
+    "name": "ml.r5.2xlarge",
+    "vcpuNum": 8
+   },
+   {
+    "_defaultOrder": 42,
+    "_isFastLaunch": false,
+    "category": "Memory Optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 128,
+    "name": "ml.r5.4xlarge",
+    "vcpuNum": 16
+   },
+   {
+    "_defaultOrder": 43,
+    "_isFastLaunch": false,
+    "category": "Memory Optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 256,
+    "name": "ml.r5.8xlarge",
+    "vcpuNum": 32
+   },
+   {
+    "_defaultOrder": 44,
+    "_isFastLaunch": false,
+    "category": "Memory Optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 384,
+    "name": "ml.r5.12xlarge",
+    "vcpuNum": 48
+   },
+   {
+    "_defaultOrder": 45,
+    "_isFastLaunch": false,
+    "category": "Memory Optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 512,
+    "name": "ml.r5.16xlarge",
+    "vcpuNum": 64
+   },
+   {
+    "_defaultOrder": 46,
+    "_isFastLaunch": false,
+    "category": "Memory Optimized",
+    "gpuNum": 0,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 768,
+    "name": "ml.r5.24xlarge",
+    "vcpuNum": 96
+   },
+   {
+    "_defaultOrder": 47,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 1,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 16,
+    "name": "ml.g5.xlarge",
+    "vcpuNum": 4
+   },
+   {
+    "_defaultOrder": 48,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 1,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 32,
+    "name": "ml.g5.2xlarge",
+    "vcpuNum": 8
+   },
+   {
+    "_defaultOrder": 49,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 1,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 64,
+    "name": "ml.g5.4xlarge",
+    "vcpuNum": 16
+   },
+   {
+    "_defaultOrder": 50,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 1,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 128,
+    "name": "ml.g5.8xlarge",
+    "vcpuNum": 32
+   },
+   {
+    "_defaultOrder": 51,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 1,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 256,
+    "name": "ml.g5.16xlarge",
+    "vcpuNum": 64
+   },
+   {
+    "_defaultOrder": 52,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 4,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 192,
+    "name": "ml.g5.12xlarge",
+    "vcpuNum": 48
+   },
+   {
+    "_defaultOrder": 53,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 4,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 384,
+    "name": "ml.g5.24xlarge",
+    "vcpuNum": 96
+   },
+   {
+    "_defaultOrder": 54,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 8,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 768,
+    "name": "ml.g5.48xlarge",
+    "vcpuNum": 192
+   },
+   {
+    "_defaultOrder": 55,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 8,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 1152,
+    "name": "ml.p4d.24xlarge",
+    "vcpuNum": 96
+   },
+   {
+    "_defaultOrder": 56,
+    "_isFastLaunch": false,
+    "category": "Accelerated computing",
+    "gpuNum": 8,
+    "hideHardwareSpecs": false,
+    "memoryGiB": 1152,
+    "name": "ml.p4de.24xlarge",
+    "vcpuNum": 96
+   }
+  ],
+  "instance_type": "ml.t3.medium",
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/document_loaders/azure_ai_data.ipynb

@@ -0,0 +1,174 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a634365e",
+   "metadata": {},
+   "source": [
+    "# Azure AI Data\n",
+    "\n",
+    ">[Azure AI Studio](https://ai.azure.com/) provides the capability to upload data assets to cloud storage and register existing data assets from the following sources:\n",
+    "\n",
+    "- Microsoft OneLake\n",
+    "- Azure Blob Storage\n",
+    "- Azure Data Lake gen 2\n",
+    "\n",
+    "The benefit of this approach over `AzureBlobStorageContainerLoader` and `AzureBlobStorageFileLoader` is that authentication is handled seamlessly to cloud storage. You can use either *identity-based* data access control to the data or *credential-based* (e.g. SAS token, account key). In the case of credential-based data access you do not need to specify secrets in your code or set up key vaults - the system handles that for you.\n",
+    "\n",
+    "This notebook covers how to load document objects from a data asset in AI Studio."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "49815096",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!pip install azureml-fsspec, azure-ai-generative"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2f0cd6a5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from azure.ai.resources.client import AIClient\n",
+    "from azure.identity import DefaultAzureCredential\n",
+    "from langchain.document_loaders import AzureAIDataLoader"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "08d40b11-e87a-426e-a6b0-89f24e47ce2c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a connection to your project\n",
+    "client = AIClient(\n",
+    "    credential=DefaultAzureCredential(),\n",
+    "    subscription_id=\"<subscription_id>\",\n",
+    "    resource_group_name=\"<resource_group_name>\",\n",
+    "    project_name=\"<project_name>\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "321cc7f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get the latest version of your data asset\n",
+    "data_asset = client.data.get(name=\"<data_asset_name>\", label=\"latest\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25d91cea-c5f2-4a53-ac19-442810451ec6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load the data asset\n",
+    "loader = AzureAIDataLoader(url=data_asset.path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2b11d155",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmpaa9xl6ch/fake.docx'}, lookup_index=0)]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0690c40a",
+   "metadata": {},
+   "source": [
+    "## Specifying a glob pattern\n",
+    "You can also specify a glob pattern for more finegrained control over what files to load. In the example below, only files with a `pdf` extension will be loaded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "72d44781",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loader = AzureAIDataLoader(url=data_asset.path, glob=\"*.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2d3c32db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Document(page_content='Lorem ipsum dolor sit amet.', lookup_str='', metadata={'source': '/var/folders/y6/8_bzdg295ld6s1_97_12m4lr0000gn/T/tmpujbkzf_l/fake.docx'}, lookup_index=0)]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "loader.load()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "885dc280",
+   "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": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/document_loaders/docugami.ipynb

@@ -21,8 +21,8 @@
    },
    "outputs": [],
    "source": [
-    "# You need the lxml package to use the DocugamiLoader (run pip install directly without \"poetry run\" if you are not using poetry)\n",
-    "!poetry run pip install lxml --quiet"
+    "# You need the dgml-utils package to use the DocugamiLoader (run pip install directly without \"poetry run\" if you are not using poetry)\n",
+    "!poetry run pip install dgml-utils==0.3.0 --upgrade --quiet"
    ]
   },
   {
@@ -43,8 +43,8 @@
     "Appropriate chunking of your documents is critical for retrieval from documents. Many chunking techniques exist, including simple ones that rely on whitespace and recursive chunk splitting based on character length. Docugami offers a different approach:\n",
     "\n",
     "1. **Intelligent Chunking:** Docugami breaks down every document into a hierarchical semantic XML tree of chunks of varying sizes, from single words or numerical values to entire sections. These chunks follow the semantic contours of the document, providing a more meaningful representation than arbitrary length or simple whitespace-based chunking.\n",
-    "2. **Structured Representation:** In addition, the XML tree indicates the structural contours of every document, using attributes denoting headings, paragraphs, lists, tables, and other common elements, and does that consistently across all supported document formats, such as scanned PDFs or DOCX files. It appropriately handles long-form document characteristics like page headers/footers or multi-column flows for clean text extraction.\n",
-    "3. **Semantic Annotations:** Chunks are annotated with semantic tags that are coherent across the document set, facilitating consistent hierarchical queries across multiple documents, even if they are written and formatted differently. For example, in set of lease agreements, you can easily identify key provisions like the Landlord, Tenant, or Renewal Date, as well as more complex information such as the wording of any sub-lease provision or whether a specific jurisdiction has an exception section within a Termination Clause.\n",
+    "2. **Semantic Annotations:** Chunks are annotated with semantic tags that are coherent across the document set, facilitating consistent hierarchical queries across multiple documents, even if they are written and formatted differently. For example, in set of lease agreements, you can easily identify key provisions like the Landlord, Tenant, or Renewal Date, as well as more complex information such as the wording of any sub-lease provision or whether a specific jurisdiction has an exception section within a Termination Clause.\n",
+    "3. **Structured Representation:** In addition, the XML tree indicates the structural contours of every document, using attributes denoting headings, paragraphs, lists, tables, and other common elements, and does that consistently across all supported document formats, such as scanned PDFs or DOCX files. It appropriately handles long-form document characteristics like page headers/footers or multi-column flows for clean text extraction.\n",
     "4. **Additional Metadata:** Chunks are also annotated with additional metadata, if a user has been using Docugami. This additional metadata can be used for high-accuracy Document QA without context window restrictions. See detailed code walk-through below.\n"
    ]
   },
@@ -65,52 +65,42 @@
    "source": [
     "## Load Documents\n",
     "\n",
-    "If the DOCUGAMI_API_KEY environment variable is set, there is no need to pass it in to the loader explicitly otherwise you can pass it in as the `access_token` parameter.\n",
-    "\n",
-    "The DocugamiLoader has a default minimum chunk size of 32. Chunks smaller than that are appended to subsequent chunks. Set min_chunk_size to 0 to get all structural chunks regardless of size."
+    "If the DOCUGAMI_API_KEY environment variable is set, there is no need to pass it in to the loader explicitly otherwise you can pass it in as the `access_token` parameter."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "DOCUGAMI_API_KEY = os.environ.get(\"DOCUGAMI_API_KEY\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='MUTUAL NON-DISCLOSURE AGREEMENT This  Mutual Non-Disclosure Agreement  (this “ Agreement ”) is entered into and made effective as of  April  4 ,  2018  between  Docugami Inc. , a  Delaware  corporation , whose address is  150  Lake Street South ,  Suite  221 ,  Kirkland ,  Washington  98033 , and  Caleb Divine , an individual, whose address is  1201  Rt  300 ,  Newburgh  NY  12550 .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:ThisMutualNon-disclosureAgreement', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'ThisMutualNon-disclosureAgreement'}),\n",
-       " Document(page_content='The above named parties desire to engage in discussions regarding a potential agreement or other transaction between the parties (the “Purpose”). In connection with such discussions, it may be necessary for the parties to disclose to each other certain confidential information or materials to enable them to evaluate whether to enter into such agreement or transaction.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Discussions', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'Discussions'}),\n",
-       " Document(page_content='In consideration of the foregoing, the parties agree as follows:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Consideration', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'Consideration'}),\n",
-       " Document(page_content='1. Confidential Information . For purposes of this  Agreement , “ Confidential Information ” means any information or materials disclosed by  one  party  to the other party that: (i) if disclosed in writing or in the form of tangible materials, is marked “confidential” or “proprietary” at the time of such disclosure; (ii) if disclosed orally or by visual presentation, is identified as “confidential” or “proprietary” at the time of such disclosure, and is summarized in a writing sent by the disclosing party to the receiving party within  thirty  ( 30 ) days  after any such disclosure; or (iii) due to its nature or the circumstances of its disclosure, a person exercising reasonable business judgment would understand to be confidential or proprietary.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Purposes/docset:ConfidentialInformation-section/docset:ConfidentialInformation[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ConfidentialInformation'}),\n",
-       " Document(page_content=\"2. Obligations and  Restrictions . Each party agrees: (i) to maintain the  other party's Confidential Information  in strict confidence; (ii) not to disclose  such Confidential Information  to any third party; and (iii) not to use  such Confidential Information  for any purpose except for the Purpose. Each party may disclose the  other party’s Confidential Information  to its employees and consultants who have a bona fide need to know  such Confidential Information  for the Purpose, but solely to the extent necessary to pursue the  Purpose  and for no other purpose; provided, that each such employee and consultant first executes a written agreement (or is otherwise already bound by a written agreement) that contains use and nondisclosure restrictions at least as protective of the  other party’s Confidential Information  as those set forth in this  Agreement .\", metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Obligations/docset:ObligationsAndRestrictions-section/docset:ObligationsAndRestrictions', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ObligationsAndRestrictions'}),\n",
-       " Document(page_content='3. Exceptions. The obligations and restrictions in Section  2  will not apply to any information or materials that:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Exceptions/docset:Exceptions-section/docset:Exceptions[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Exceptions'}),\n",
-       " Document(page_content='(i) were, at the date of disclosure, or have subsequently become, generally known or available to the public through no act or failure to act by the receiving party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:TheDate/docset:TheDate', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheDate'}),\n",
-       " Document(page_content='(ii) were rightfully known by the receiving party prior to receiving such information or materials from the disclosing party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:SuchInformation/docset:TheReceivingParty', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheReceivingParty'}),\n",
-       " Document(page_content='(iii) are rightfully acquired by the receiving party from a third party who has the right to disclose such information or materials without breach of any confidentiality obligation to the disclosing party;', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheDate/docset:TheReceivingParty/docset:TheReceivingParty', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheReceivingParty'}),\n",
-       " Document(page_content='4. Compelled Disclosure . Nothing in this  Agreement  will be deemed to restrict a party from disclosing the  other party’s Confidential Information  to the extent required by any order, subpoena, law, statute or regulation; provided, that the party required to make such a disclosure uses reasonable efforts to give the other party reasonable advance notice of such required disclosure in order to enable the other party to prevent or limit such disclosure.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Disclosure/docset:CompelledDisclosure-section/docset:CompelledDisclosure', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'CompelledDisclosure'}),\n",
-       " Document(page_content='5. Return of  Confidential Information . Upon the completion or abandonment of the Purpose, and in any event upon the disclosing party’s request, the receiving party will promptly return to the disclosing party all tangible items and embodiments containing or consisting of the  disclosing party’s Confidential Information  and all copies thereof (including electronic copies), and any notes, analyses, compilations, studies, interpretations, memoranda or other documents (regardless of the form thereof) prepared by or on behalf of the receiving party that contain or are based upon the  disclosing party’s Confidential Information .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheCompletion/docset:ReturnofConfidentialInformation-section/docset:ReturnofConfidentialInformation', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'ReturnofConfidentialInformation'}),\n",
-       " Document(page_content='6. No  Obligations . Each party retains the right to determine whether to disclose any  Confidential Information  to the other party.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:NoObligations/docset:NoObligations-section/docset:NoObligations[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'NoObligations'}),\n",
-       " Document(page_content='7. No Warranty. ALL  CONFIDENTIAL INFORMATION  IS PROVIDED BY THE  DISCLOSING PARTY  “AS  IS ”.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:NoWarranty/docset:NoWarranty-section/docset:NoWarranty[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'NoWarranty'}),\n",
-       " Document(page_content='8. Term. This  Agreement  will remain in effect for a period of  seven  ( 7 ) years  from the date of last disclosure of  Confidential Information  by either party, at which time it will terminate.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:ThisAgreement/docset:Term-section/docset:Term', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Term'}),\n",
-       " Document(page_content='9. Equitable Relief . Each party acknowledges that the unauthorized use or disclosure of the  disclosing party’s Confidential Information  may cause the disclosing party to incur irreparable harm and significant damages, the degree of which may be difficult to ascertain. Accordingly, each party agrees that the disclosing party will have the right to seek immediate equitable relief to enjoin any unauthorized use or disclosure of  its Confidential Information , in addition to any other rights and remedies that it may have at law or otherwise.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:EquitableRelief/docset:EquitableRelief-section/docset:EquitableRelief[2]', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'EquitableRelief'}),\n",
-       " Document(page_content='10. Non-compete. To the maximum extent permitted by applicable law, during the  Term  of this  Agreement  and for a period of  one  ( 1 ) year  thereafter,  Caleb  Divine  may not market software products or do business that directly or indirectly competes with  Docugami  software products .', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:TheMaximumExtent/docset:Non-compete-section/docset:Non-compete', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Non-compete'}),\n",
-       " Document(page_content='11. Miscellaneous. This  Agreement  will be governed and construed in accordance with the laws of the  State  of  Washington , excluding its body of law controlling conflict of laws. This  Agreement  is the complete and exclusive understanding and agreement between the parties regarding the subject matter of this  Agreement  and supersedes all prior agreements, understandings and communications, oral or written, between the parties regarding the subject matter of this  Agreement . If any provision of this  Agreement  is held invalid or unenforceable by a court of competent jurisdiction, that provision of this  Agreement  will be enforced to the maximum extent permissible and the other provisions of this  Agreement  will remain in full force and effect. Neither party may assign this  Agreement , in whole or in part, by operation of law or otherwise, without the other party’s prior written consent, and any attempted assignment without such consent will be void. This  Agreement  may be executed in counterparts, each of which will be deemed an original, but all of which together will constitute one and the same instrument.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:MutualNon-disclosure/docset:MUTUALNON-DISCLOSUREAGREEMENT-section/docset:MUTUALNON-DISCLOSUREAGREEMENT/docset:Consideration/docset:Purposes/docset:Accordance/docset:Miscellaneous-section/docset:Miscellaneous', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'div', 'tag': 'Miscellaneous'}),\n",
-       " Document(page_content='[SIGNATURE PAGE FOLLOWS] IN  WITNESS  WHEREOF, the parties hereto have executed this  Mutual Non-Disclosure Agreement  by their duly authorized officers or representatives as of the date first set forth above.', metadata={'xpath': '/docset:MutualNon-disclosure/docset:Witness/docset:TheParties/docset:TheParties', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': 'p', 'tag': 'TheParties'}),\n",
-       " Document(page_content='DOCUGAMI INC . : \\n\\n Caleb Divine : \\n\\n Signature:  Signature:  Name: \\n\\n Jean Paoli  Name:  Title: \\n\\n CEO  Title:', metadata={'xpath': '/docset:MutualNon-disclosure/docset:Witness/docset:TheParties/docset:DocugamiInc/docset:DocugamiInc/xhtml:table', 'id': '43rj0ds7s0ur', 'source': 'NDA simple layout.docx', 'structure': '', 'tag': 'table'})]"
+       "120"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "DOCUGAMI_API_KEY = os.environ.get(\"DOCUGAMI_API_KEY\")\n",
+    "docset_id = \"26xpy3aes7xp\"\n",
+    "document_ids = [\"d7jqdzcj50sj\", \"cgd1eacfkchw\"]\n",
     "\n",
     "# To load all docs in the given docset ID, just don't provide document_ids\n",
-    "loader = DocugamiLoader(docset_id=\"ecxqpipcoe2p\", document_ids=[\"43rj0ds7s0ur\"])\n",
-    "docs = loader.load()\n",
-    "docs"
+    "loader = DocugamiLoader(docset_id=docset_id, document_ids=document_ids)\n",
+    "chunks = loader.load()\n",
+    "len(chunks)"
    ]
   },
   {
@@ -122,7 +112,39 @@
     "1. **id and source:** ID and Name of the file (PDF, DOC or DOCX) the chunk is sourced from within Docugami.\n",
     "2. **xpath:** XPath inside the XML representation of the document, for the chunk. Useful for source citations directly to the actual chunk inside the document XML.\n",
     "3. **structure:** Structural attributes of the chunk, e.g. h1, h2, div, table, td, etc. Useful to filter out certain kinds of chunks if needed by the caller.\n",
-    "4. **tag:** Semantic tag for the chunk, using various generative and extractive techniques. More details here: https://github.com/docugami/DFM-benchmarks"
+    "4. **tag:** Semantic tag for the chunk, using various generative and extractive techniques. More details here: https://github.com/docugami/DFM-benchmarks\n",
+    "\n",
+    "You can control chunking behavior by setting the following properties on the `DocugamiLoader` instance:\n",
+    "\n",
+    "1. You can set min and max chunk size, which the system tries to adhere to with minimal truncation. You can set `loader.min_text_length` and `loader.max_text_length` to control these.\n",
+    "2. By default, only the text for chunks is returned. However, Docugami's XML knowledge graph has additional rich information including semantic tags for entities inside the chunk. Set `loader.include_xml_tags = True` if you want the additional xml metadata on the returned chunks.\n",
+    "3. In addition, you can set `loader.parent_hierarchy_levels` if you want Docugami to return parent chunks in the chunks it returns. The child chunks point to the parent chunks via the `loader.parent_id_key` value. This is useful e.g. with the [MultiVector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector) for [small-to-big](https://www.youtube.com/watch?v=ihSiRrOUwmg) retrieval. See detailed example later in this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "page_content='MASTER SERVICES AGREEMENT\\n <ThisServicesAgreement> This Services Agreement (the “Agreement”) sets forth terms under which <Company>MagicSoft, Inc. </Company>a <Org><USState>Washington </USState>Corporation </Org>(“Company”) located at <CompanyAddress><CompanyStreetAddress><Company>600 </Company><Company>4th Ave</Company></CompanyStreetAddress>, <Company>Seattle</Company>, <Client>WA </Client><ProvideServices>98104 </ProvideServices></CompanyAddress>shall provide services to <Client>Daltech, Inc.</Client>, a <Company><USState>Washington </USState>Corporation </Company>(the “Client”) located at <ClientAddress><ClientStreetAddress><Client>701 </Client><Client>1st St</Client></ClientStreetAddress>, <Client>Kirkland</Client>, <State>WA </State><Client>98033</Client></ClientAddress>. This Agreement is effective as of <EffectiveDate>February 15, 2021 </EffectiveDate>(“Effective Date”). </ThisServicesAgreement>' metadata={'xpath': '/dg:chunk/docset:MASTERSERVICESAGREEMENT-section/dg:chunk', 'id': 'c28554d0af5114e2b102e6fc4dcbbde5', 'name': 'Master Services Agreement - Daltech.docx', 'source': 'Master Services Agreement - Daltech.docx', 'structure': 'h1 p', 'tag': 'chunk ThisServicesAgreement', 'Liability': '', 'Workers Compensation Insurance': '$1,000,000', 'Limit': '$1,000,000', 'Commercial General Liability Insurance': '$2,000,000', 'Technology Professional Liability Errors Omissions Policy': '$5,000,000', 'Excess Liability Umbrella Coverage': '$9,000,000', 'Client': 'Daltech, Inc.', 'Services Agreement Date': 'INITIAL STATEMENT  OF WORK (SOW)  The purpose of this SOW is to describe the Software and Services that Company will initially provide to  Daltech, Inc.  the “Client”) under the terms and conditions of the  Services Agreement  entered into between the parties on  June 15, 2021', 'Completion of the Services by Company Date': 'February 15, 2022', 'Charge': 'one hundred percent (100%)', 'Company': 'MagicSoft, Inc.', 'Effective Date': 'February 15, 2021', 'Start Date': '03/15/2021', 'Scheduled Onsite Visits Are Cancelled': 'ten (10) working days', 'Limit on Liability': '', 'Liability Cap': '', 'Business Automobile Liability': 'Business Automobile Liability  covering all vehicles that Company owns, hires or leases with a limit of no less than  $1,000,000  (combined single limit for bodily injury and property damage) for each accident.', 'Contractual Liability Coverage': 'Commercial General Liability insurance including  Contractual Liability Coverage , with coverage for products liability, completed operations, property damage and bodily injury, including  death , with an aggregate limit of no less than  $2,000,000 . This policy shall name Client as an additional insured with respect to the provision of services provided under this Agreement. This policy shall include a waiver of subrogation against Client.', 'Technology Professional Liability Errors Omissions': 'Technology Professional Liability Errors & Omissions policy (which includes Cyber Risk coverage and Computer Security and Privacy Liability coverage) with a limit of no less than  $5,000,000  per occurrence and in the aggregate.'}\n",
+      "page_content='A. STANDARD SOFTWARE AND SERVICES AGREEMENT\\n 1. Deliverables.\\n Company shall provide Client with software, technical support, product management, development, and <_testRef>testing </_testRef>services (“Services”) to the Client as described on one or more Statements of Work signed by Company and Client that reference this Agreement (“SOW” or “Statement of Work”). Company shall perform Services in a prompt manner and have the final product or service (“Deliverable”) ready for Client no later than the due date specified in the applicable SOW (“Completion Date”). This due date is subject to change in accordance with the Change Order process defined in the applicable SOW. Client shall assist Company by promptly providing all information requests known or available and relevant to the Services in a timely manner.' metadata={'xpath': '/dg:chunk/docset:MASTERSERVICESAGREEMENT-section/docset:MASTERSERVICESAGREEMENT/dg:chunk[1]/docset:Standard/dg:chunk[1]/dg:chunk[1]', 'id': 'de60160d328df10fa2637637c803d2d4', 'name': 'Master Services Agreement - Daltech.docx', 'source': 'Master Services Agreement - Daltech.docx', 'structure': 'lim h1 lim h1 div', 'tag': 'chunk', 'Liability': '', 'Workers Compensation Insurance': '$1,000,000', 'Limit': '$1,000,000', 'Commercial General Liability Insurance': '$2,000,000', 'Technology Professional Liability Errors Omissions Policy': '$5,000,000', 'Excess Liability Umbrella Coverage': '$9,000,000', 'Client': 'Daltech, Inc.', 'Services Agreement Date': 'INITIAL STATEMENT  OF WORK (SOW)  The purpose of this SOW is to describe the Software and Services that Company will initially provide to  Daltech, Inc.  the “Client”) under the terms and conditions of the  Services Agreement  entered into between the parties on  June 15, 2021', 'Completion of the Services by Company Date': 'February 15, 2022', 'Charge': 'one hundred percent (100%)', 'Company': 'MagicSoft, Inc.', 'Effective Date': 'February 15, 2021', 'Start Date': '03/15/2021', 'Scheduled Onsite Visits Are Cancelled': 'ten (10) working days', 'Limit on Liability': '', 'Liability Cap': '', 'Business Automobile Liability': 'Business Automobile Liability  covering all vehicles that Company owns, hires or leases with a limit of no less than  $1,000,000  (combined single limit for bodily injury and property damage) for each accident.', 'Contractual Liability Coverage': 'Commercial General Liability insurance including  Contractual Liability Coverage , with coverage for products liability, completed operations, property damage and bodily injury, including  death , with an aggregate limit of no less than  $2,000,000 . This policy shall name Client as an additional insured with respect to the provision of services provided under this Agreement. This policy shall include a waiver of subrogation against Client.', 'Technology Professional Liability Errors Omissions': 'Technology Professional Liability Errors & Omissions policy (which includes Cyber Risk coverage and Computer Security and Privacy Liability coverage) with a limit of no less than  $5,000,000  per occurrence and in the aggregate.'}\n",
+      "page_content='2. Onsite Services.\\n 2.1 Onsite visits will be charged on a <Frequency>daily </Frequency>basis (minimum <OnsiteVisits>8 hours</OnsiteVisits>).' metadata={'xpath': '/dg:chunk/docset:MASTERSERVICESAGREEMENT-section/docset:MASTERSERVICESAGREEMENT/dg:chunk[1]/docset:Standard/dg:chunk[3]/dg:chunk[1]', 'id': 'db18315b437ac2de6b555d2d8ef8f893', 'name': 'Master Services Agreement - Daltech.docx', 'source': 'Master Services Agreement - Daltech.docx', 'structure': 'lim h1 lim p', 'tag': 'chunk', 'Liability': '', 'Workers Compensation Insurance': '$1,000,000', 'Limit': '$1,000,000', 'Commercial General Liability Insurance': '$2,000,000', 'Technology Professional Liability Errors Omissions Policy': '$5,000,000', 'Excess Liability Umbrella Coverage': '$9,000,000', 'Client': 'Daltech, Inc.', 'Services Agreement Date': 'INITIAL STATEMENT  OF WORK (SOW)  The purpose of this SOW is to describe the Software and Services that Company will initially provide to  Daltech, Inc.  the “Client”) under the terms and conditions of the  Services Agreement  entered into between the parties on  June 15, 2021', 'Completion of the Services by Company Date': 'February 15, 2022', 'Charge': 'one hundred percent (100%)', 'Company': 'MagicSoft, Inc.', 'Effective Date': 'February 15, 2021', 'Start Date': '03/15/2021', 'Scheduled Onsite Visits Are Cancelled': 'ten (10) working days', 'Limit on Liability': '', 'Liability Cap': '', 'Business Automobile Liability': 'Business Automobile Liability  covering all vehicles that Company owns, hires or leases with a limit of no less than  $1,000,000  (combined single limit for bodily injury and property damage) for each accident.', 'Contractual Liability Coverage': 'Commercial General Liability insurance including  Contractual Liability Coverage , with coverage for products liability, completed operations, property damage and bodily injury, including  death , with an aggregate limit of no less than  $2,000,000 . This policy shall name Client as an additional insured with respect to the provision of services provided under this Agreement. This policy shall include a waiver of subrogation against Client.', 'Technology Professional Liability Errors Omissions': 'Technology Professional Liability Errors & Omissions policy (which includes Cyber Risk coverage and Computer Security and Privacy Liability coverage) with a limit of no less than  $5,000,000  per occurrence and in the aggregate.'}\n",
+      "page_content='2.2 <Expenses>Time and expenses will be charged based on actuals unless otherwise described in an Order Form or accompanying SOW. </Expenses>' metadata={'xpath': '/dg:chunk/docset:MASTERSERVICESAGREEMENT-section/docset:MASTERSERVICESAGREEMENT/dg:chunk[1]/docset:Standard/dg:chunk[3]/dg:chunk[2]/docset:ADailyBasis/dg:chunk[2]/dg:chunk', 'id': '506220fa472d5c48c8ee3db78c1122c1', 'name': 'Master Services Agreement - Daltech.docx', 'source': 'Master Services Agreement - Daltech.docx', 'structure': 'lim p', 'tag': 'chunk Expenses', 'Liability': '', 'Workers Compensation Insurance': '$1,000,000', 'Limit': '$1,000,000', 'Commercial General Liability Insurance': '$2,000,000', 'Technology Professional Liability Errors Omissions Policy': '$5,000,000', 'Excess Liability Umbrella Coverage': '$9,000,000', 'Client': 'Daltech, Inc.', 'Services Agreement Date': 'INITIAL STATEMENT  OF WORK (SOW)  The purpose of this SOW is to describe the Software and Services that Company will initially provide to  Daltech, Inc.  the “Client”) under the terms and conditions of the  Services Agreement  entered into between the parties on  June 15, 2021', 'Completion of the Services by Company Date': 'February 15, 2022', 'Charge': 'one hundred percent (100%)', 'Company': 'MagicSoft, Inc.', 'Effective Date': 'February 15, 2021', 'Start Date': '03/15/2021', 'Scheduled Onsite Visits Are Cancelled': 'ten (10) working days', 'Limit on Liability': '', 'Liability Cap': '', 'Business Automobile Liability': 'Business Automobile Liability  covering all vehicles that Company owns, hires or leases with a limit of no less than  $1,000,000  (combined single limit for bodily injury and property damage) for each accident.', 'Contractual Liability Coverage': 'Commercial General Liability insurance including  Contractual Liability Coverage , with coverage for products liability, completed operations, property damage and bodily injury, including  death , with an aggregate limit of no less than  $2,000,000 . This policy shall name Client as an additional insured with respect to the provision of services provided under this Agreement. This policy shall include a waiver of subrogation against Client.', 'Technology Professional Liability Errors Omissions': 'Technology Professional Liability Errors & Omissions policy (which includes Cyber Risk coverage and Computer Security and Privacy Liability coverage) with a limit of no less than  $5,000,000  per occurrence and in the aggregate.'}\n",
+      "page_content='2.3 <RegularWorkingHours>All work will be executed during regular working hours <RegularWorkingHours>Monday</RegularWorkingHours>-<Weekday>Friday </Weekday><RegularWorkingHours><RegularWorkingHours>0800</RegularWorkingHours>-<Number>1900</Number></RegularWorkingHours>. For work outside of these hours on weekdays, Company will charge <Charge>one hundred percent (100%) </Charge>of the regular hourly rate and <Charge>two hundred percent (200%) </Charge>for Saturdays, Sundays and public holidays applicable to Company. </RegularWorkingHours>' metadata={'xpath': '/dg:chunk/docset:MASTERSERVICESAGREEMENT-section/docset:MASTERSERVICESAGREEMENT/dg:chunk[1]/docset:Standard/dg:chunk[3]/dg:chunk[2]/docset:ADailyBasis/dg:chunk[3]/dg:chunk', 'id': 'dac7a3ded61b5c4f3e59771243ea46c1', 'name': 'Master Services Agreement - Daltech.docx', 'source': 'Master Services Agreement - Daltech.docx', 'structure': 'lim p', 'tag': 'chunk RegularWorkingHours', 'Liability': '', 'Workers Compensation Insurance': '$1,000,000', 'Limit': '$1,000,000', 'Commercial General Liability Insurance': '$2,000,000', 'Technology Professional Liability Errors Omissions Policy': '$5,000,000', 'Excess Liability Umbrella Coverage': '$9,000,000', 'Client': 'Daltech, Inc.', 'Services Agreement Date': 'INITIAL STATEMENT  OF WORK (SOW)  The purpose of this SOW is to describe the Software and Services that Company will initially provide to  Daltech, Inc.  the “Client”) under the terms and conditions of the  Services Agreement  entered into between the parties on  June 15, 2021', 'Completion of the Services by Company Date': 'February 15, 2022', 'Charge': 'one hundred percent (100%)', 'Company': 'MagicSoft, Inc.', 'Effective Date': 'February 15, 2021', 'Start Date': '03/15/2021', 'Scheduled Onsite Visits Are Cancelled': 'ten (10) working days', 'Limit on Liability': '', 'Liability Cap': '', 'Business Automobile Liability': 'Business Automobile Liability  covering all vehicles that Company owns, hires or leases with a limit of no less than  $1,000,000  (combined single limit for bodily injury and property damage) for each accident.', 'Contractual Liability Coverage': 'Commercial General Liability insurance including  Contractual Liability Coverage , with coverage for products liability, completed operations, property damage and bodily injury, including  death , with an aggregate limit of no less than  $2,000,000 . This policy shall name Client as an additional insured with respect to the provision of services provided under this Agreement. This policy shall include a waiver of subrogation against Client.', 'Technology Professional Liability Errors Omissions': 'Technology Professional Liability Errors & Omissions policy (which includes Cyber Risk coverage and Computer Security and Privacy Liability coverage) with a limit of no less than  $5,000,000  per occurrence and in the aggregate.'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "loader.min_text_length = 64\n",
+    "loader.include_xml_tags = True\n",
+    "chunks = loader.load()\n",
+    "\n",
+    "for chunk in chunks[:5]:\n",
+    "    print(chunk)"
    ]
   },
   {
@@ -136,27 +158,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "!poetry run pip -q install openai tiktoken chromadb"
+    "!poetry run pip install --upgrade openai tiktoken chromadb hnswlib --quiet"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4674\n"
+     ]
+    }
+   ],
    "source": [
-    "from langchain.chains import RetrievalQA\n",
-    "from langchain.embeddings import OpenAIEmbeddings\n",
-    "from langchain.llms import OpenAI\n",
-    "from langchain.vectorstores import Chroma\n",
-    "\n",
     "# For this example, we already have a processed docset for a set of lease documents\n",
-    "loader = DocugamiLoader(docset_id=\"wh2kned25uqm\")\n",
-    "documents = loader.load()"
+    "loader = DocugamiLoader(docset_id=\"zo954yqy53wp\")\n",
+    "chunks = loader.load()\n",
+    "\n",
+    "# strip semantic metadata intentionally, to test how things work without semantic metadata\n",
+    "for chunk in chunks:\n",
+    "    stripped_metadata = chunk.metadata.copy()\n",
+    "    for key in chunk.metadata:\n",
+    "        if key not in [\"name\", \"xpath\", \"id\", \"structure\"]:\n",
+    "            # remove semantic metadata\n",
+    "            del stripped_metadata[key]\n",
+    "    chunk.metadata = stripped_metadata\n",
+    "\n",
+    "print(len(chunks))"
    ]
   },
   {
@@ -170,12 +206,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.chains import RetrievalQA\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.llms.openai import OpenAI\n",
+    "from langchain.vectorstores.chroma import Chroma\n",
+    "\n",
     "embedding = OpenAIEmbeddings()\n",
-    "vectordb = Chroma.from_documents(documents=documents, embedding=embedding)\n",
+    "vectordb = Chroma.from_documents(documents=chunks, embedding=embedding)\n",
     "retriever = vectordb.as_retriever()\n",
     "qa_chain = RetrievalQA.from_chain_type(\n",
     "    llm=OpenAI(), chain_type=\"stuff\", retriever=retriever, return_source_documents=True\n",
@@ -184,21 +225,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "{'query': 'What can tenants do with signage on their properties?',\n",
-       " 'result': \" Tenants can place or attach signs (digital or otherwise) to their premises with written permission from the landlord. The signs must conform to all applicable laws, ordinances, etc. governing the same. Tenants can also have their name listed in the building's directory at the landlord's cost.\",\n",
-       " 'source_documents': [Document(page_content='ARTICLE VI  SIGNAGE 6.01  Signage . Tenant  may place or attach to the  Premises signs  (digital or otherwise) or other such identification as needed after receiving written permission from the  Landlord , which permission shall not be unreasonably withheld. Any damage caused to the Premises by the  Tenant ’s erecting or removing such signs shall be repaired promptly by the  Tenant  at the  Tenant ’s expense . Any signs or other form of identification allowed must conform to all applicable laws, ordinances, etc. governing the same.  Tenant  also agrees to have any window or glass identification completely removed and cleaned at its expense promptly upon vacating the Premises.', metadata={'Landlord': 'BUBBA CENTER PARTNERSHIP', 'Lease Date': 'April  24  \\n\\n ,', 'Lease Parties': 'This  OFFICE LEASE AGREEMENT  (this \"Lease\") is made and entered into by and between  BUBBA CENTER PARTNERSHIP  (\" Landlord \"), and  Truetone Lane LLC , a  Delaware  limited liability company  (\" Tenant \").', 'Tenant': 'Truetone Lane LLC', 'id': 'v1bvgaozfkak', 'source': 'TruTone Lane 2.docx', 'structure': 'div', 'tag': '_601Signage', 'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:Article/docset:ARTICLEVISIGNAGE-section/docset:_601Signage-section/docset:_601Signage'}),\n",
-       "  Document(page_content='Signage.  Tenant  may place or attach to the  Premises signs  (digital or otherwise) or other such identification as needed after receiving written permission from the  Landlord , which permission shall not be unreasonably withheld. Any damage caused to the Premises by the  Tenant ’s erecting or removing such signs shall be repaired promptly by the  Tenant  at the  Tenant ’s expense . Any signs or other form of identification allowed must conform to all applicable laws, ordinances, etc. governing the same.  Tenant  also agrees to have any window or glass identification completely removed and cleaned at its expense promptly upon vacating the Premises. \\n\\n                                                          ARTICLE VII  UTILITIES 7.01', metadata={'Landlord': 'GLORY ROAD LLC', 'Lease Date': 'April  30  ,  2020', 'Lease Parties': 'This  OFFICE LEASE AGREEMENT  (this  \"Lease\") is made and entered into by and between  GLORY ROAD LLC  (\" Landlord \"), and  Truetone Lane LLC , a  Delaware  limited liability company  (\" Tenant \").', 'Tenant': 'Truetone Lane LLC', 'id': 'g2fvhekmltza', 'source': 'TruTone Lane 6.pdf', 'structure': 'lim', 'tag': 'chunk', 'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:Article/docset:ArticleIiiUse/docset:ARTICLEIIIUSEANDCAREOFPREMISES-section/docset:ARTICLEIIIUSEANDCAREOFPREMISES/docset:AnyTime/docset:Addition/dg:chunk'}),\n",
-       "  Document(page_content='Landlord , its agents, servants, employees, licensees, invitees, and contractors during the last year of the term of this  Lease  at any and all times during regular business hours, after  24  hour  notice  to tenant, to pass and repass on and through the Premises, or such portion thereof as may be necessary, in order that they or any of them may gain access to the Premises for the purpose of showing the  Premises  to potential new tenants or real estate brokers. In addition,  Landlord  shall be entitled to place a \"FOR  RENT \" or \"FOR LEASE\" sign (not exceeding  8.5 ” x  11 ”) in the front window of the Premises during the  last  six  months  of the term of this  Lease .', metadata={'Landlord': 'BIRCH STREET ,  LLC', 'Lease Date': 'October  15 ,  2021', 'Lease Parties': 'The provisions of this rider are hereby incorporated into and made a part of the  Lease  dated as of  October  15 ,  2021  between  BIRCH STREET ,  LLC , having an address at  c/o Birch Palace ,  6  Grace Avenue Suite  200 ,  Great Neck ,  New York  11021  (\" Landlord \"), and  Trutone Lane LLC , having an address at  4  Pearl Street ,  New York ,  New York  10012  (\" Tenant \") of Premises known as the ground floor space and lower level space, as per floor plan annexed hereto and made a part hereof as  Exhibit A  (“Premises”) at  4  Pearl Street ,  New York ,  New York  10012  in the  City  of  New York ,  Borough  of  Manhattan , to which this rider is annexed. If there is any conflict between the provisions of this rider and the remainder of this  Lease , the provisions of this rider shall govern.', 'Tenant': 'Trutone Lane LLC', 'id': 'omvs4mysdk6b', 'source': 'TruTone Lane 1.docx', 'structure': 'p', 'tag': 'Landlord', 'xpath': '/docset:Rider/docset:RIDERTOLEASE-section/docset:RIDERTOLEASE/docset:FixedRent/docset:TermYearPeriod/docset:Lease/docset:_42FLandlordSAccess-section/docset:_42FLandlordSAccess/docset:LandlordsRights/docset:Landlord'}),\n",
-       "  Document(page_content=\"24. SIGNS . No signage shall be placed by  Tenant  on any portion of the  Project . However,  Tenant  shall be permitted to place a sign bearing its name in a location approved by  Landlord  near the entrance to the  Premises  (at  Tenant's cost ) and will be furnished a single listing of its name in the  Building's directory  (at  Landlord 's cost ), all in accordance with the criteria adopted  from time to time  by  Landlord  for the  Project . Any changes or additional listings in the directory shall be furnished (subject to availability of space) for the  then Building Standard charge .\", metadata={'Landlord': 'Perry  &  Blair LLC', 'Lease Date': 'March  29th ,  2019', 'Lease Parties': 'THIS OFFICE LEASE  (the \"Lease\") is made and entered into as of  March  29th ,  2019 , by and between  Landlord  and  Tenant . \"Date of this Lease\" shall mean the date on which the last one of the  Landlord  and  Tenant  has signed this  Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'dsyfhh4vpeyf', 'source': 'Shorebucks LLC_CO.pdf', 'structure': 'div', 'tag': 'SIGNS', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:ThisLease-section/docset:ThisLease/docset:Guaranty-section/docset:Guaranty[2]/docset:TheTransfer/docset:TheTerms/docset:Indemnification/docset:INDEMNIFICATION-section/docset:INDEMNIFICATION/docset:Waiver/docset:Waiver/docset:Signs/docset:SIGNS-section/docset:SIGNS'})]}"
+       " 'result': ' Tenants can place or attach signage (digital or otherwise) to their property after receiving written permission from the landlord, which permission shall not be unreasonably withheld. The signage must conform to all applicable laws, ordinances, etc. governing the same, and tenants must remove all such signs by the termination of the lease.',\n",
+       " 'source_documents': [Document(page_content='6.01 Signage. Tenant may place or attach to the Premises signs (digital or otherwise) or other such identification as needed after receiving written permission from the Landlord, which permission shall not be unreasonably withheld. Any damage caused to the Premises by the Tenant’s erecting or removing such signs shall be repaired promptly by the Tenant at the Tenant’s expense. Any signs or other form of identification allowed must conform to all applicable laws, ordinances, etc. governing the same. Tenant also agrees to have any window or glass identification completely removed and cleaned at its expense promptly upon vacating the Premises. ARTICLE VII UTILITIES', metadata={'id': '1c290eea05915ba0f24c4a1ffc05d6f3', 'name': 'Sample Commercial Leases/TruTone Lane 6.pdf', 'structure': 'lim h1', 'xpath': '/dg:chunk/dg:chunk/dg:chunk[2]/dg:chunk[1]/docset:TheApprovedUse/dg:chunk[12]/dg:chunk[1]'}),\n",
+       "  Document(page_content='6.01 Signage. Tenant may place or attach to the Premises signs (digital or otherwise) or other such identification as needed after receiving written permission from the Landlord, which permission shall not be unreasonably withheld. Any damage caused to the Premises by the Tenant’s erecting or removing such signs shall be repaired promptly by the Tenant at the Tenant’s expense. Any signs or other form of identification allowed must conform to all applicable laws, ordinances, etc. governing the same. Tenant also agrees to have any window or glass identification completely removed and cleaned at its expense promptly upon vacating the Premises. ARTICLE VII UTILITIES', metadata={'id': '1c290eea05915ba0f24c4a1ffc05d6f3', 'name': 'Sample Commercial Leases/TruTone Lane 2.pdf', 'structure': 'lim h1', 'xpath': '/dg:chunk/dg:chunk/dg:chunk[2]/dg:chunk[1]/docset:TheApprovedUse/dg:chunk[12]/dg:chunk[1]'}),\n",
+       "  Document(page_content='Tenant may place or attach to the Premises signs (digital or otherwise) or other such identification as needed after receiving written permission from the Landlord, which permission shall not be unreasonably withheld. Any damage caused to the Premises by the Tenant’s erecting or removing such signs shall be repaired promptly by the Tenant at the Tenant’s expense. Any signs or other form of identification allowed must conform to all applicable laws, ordinances, etc. governing the same. Tenant also agrees to have any window or glass identification completely removed and cleaned at its expense promptly upon vacating the Premises.', metadata={'id': '58d268162ecc36d8633b7bc364afcb8c', 'name': 'Sample Commercial Leases/TruTone Lane 2.docx', 'structure': 'div', 'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/dg:chunk/docset:ARTICLEVISIGNAGE-section/docset:ARTICLEVISIGNAGE/docset:_601Signage'}),\n",
+       "  Document(page_content='8. SIGNS:\\n Tenant shall not install signs upon the Premises without Landlord’s prior written approval, which approval shall not be unreasonably withheld or delayed, and any such signage shall be subject to any applicable governmental laws, ordinances, regulations, and other requirements. Tenant shall remove all such signs by the terminations of this Lease. Such installations and removals shall be made in such a manner as to avoid injury or defacement of the Building and other improvements, and Tenant shall repair any injury or defacement, including without limitation discoloration caused by such installations and/or removal.', metadata={'id': '6b7d88f0c979c65d5db088fc177fa81f', 'name': 'Lease Agreements/Bioplex, Inc.pdf', 'structure': 'lim h1 div', 'xpath': '/dg:chunk/docset:WITNESSETH-section/docset:WITNESSETH/dg:chunk/docset:TheObligation/dg:chunk[8]/dg:chunk'})]}"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -212,7 +253,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Using Docugami to Add Metadata to Chunks for High Accuracy Document QA\n",
+    "## Using Docugami Knowledge Graph for High Accuracy Document QA\n",
     "\n",
     "One issue with large documents is that the correct answer to your question may depend on chunks that are far apart in the document. Typical chunking techniques, even with overlap, will struggle with providing the LLM sufficent context to answer such questions. With upcoming very large context LLMs, it may be possible to stuff a lot of tokens, perhaps even entire documents, inside the context but this will still hit limits at some point with very long documents, or a lot of documents.\n",
     "\n",
@@ -221,16 +262,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "' 9,753 square feet.'"
+       "\" I don't know.\""
       ]
      },
-     "execution_count": 8,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -240,28 +281,21 @@
     "chain_response[\"result\"]  # correct answer should be 13,500 sq ft"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "At first glance the answer may seem reasonable, but if you review the source chunks carefully for this answer, you will see that the chunking of the document did not end up putting the Landlord name and the rentable area in the same context, since they are far apart in the document. The retriever therefore ends up finding unrelated chunks from other documents not even related to the **DHA Group** landlord. That landlord happens to be mentioned on the first page of the file **Shorebucks LLC_NJ.pdf** file, and while one of the source chunks used by the chain is indeed from that doc that contains the correct answer (**13,500**), other source chunks from different docs are included, and the answer is therefore incorrect."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[Document(page_content='1.1 Landlord . DHA Group , a  Delaware  limited liability company  authorized to transact business in  New Jersey .', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March  29th ,  2019', 'Lease Parties': 'THIS OFFICE LEASE  (the \"Lease\") is made and entered into as of  March  29th ,  2019 , by and between  Landlord  and  Tenant . \"Date of this Lease\" shall mean the date on which the last one of the  Landlord  and  Tenant  has signed this  Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'DhaGroup', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:DhaGroup/docset:Landlord-section/docset:DhaGroup'}),\n",
-       " Document(page_content='WITNESSES: LANDLORD: DHA Group , a  Delaware  limited liability company', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March  29th ,  2019', 'Lease Parties': 'THIS OFFICE LEASE  (the \"Lease\") is made and entered into as of  March  29th ,  2019 , by and between  Landlord  and  Tenant . \"Date of this Lease\" shall mean the date on which the last one of the  Landlord  and  Tenant  has signed this  Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'p', 'tag': 'DhaGroup', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Guaranty-section/docset:Guaranty[2]/docset:SIGNATURESONNEXTPAGE-section/docset:INWITNESSWHEREOF-section/docset:INWITNESSWHEREOF/docset:Behalf/docset:Witnesses/xhtml:table/xhtml:tbody/xhtml:tr[3]/xhtml:td[2]/docset:DhaGroup'}),\n",
-       " Document(page_content=\"1.16 Landlord 's Notice Address . DHA  Group , Suite  1010 ,  111  Bauer Dr ,  Oakland ,  New Jersey ,  07436 , with a copy to the  Building  Management  Office  at the  Project , Attention:  On - Site  Property Manager .\", metadata={'Landlord': 'DHA Group', 'Lease Date': 'March  29th ,  2019', 'Lease Parties': 'THIS OFFICE LEASE  (the \"Lease\") is made and entered into as of  March  29th ,  2019 , by and between  Landlord  and  Tenant . \"Date of this Lease\" shall mean the date on which the last one of the  Landlord  and  Tenant  has signed this  Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'LandlordsNoticeAddress', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:PercentageRent/docset:NoticeAddress[2]/docset:LandlordsNoticeAddress-section/docset:LandlordsNoticeAddress[2]'}),\n",
-       " Document(page_content='1.6 Rentable Area  of the Premises. 9,753  square feet . This square footage figure includes an add-on factor for  Common Areas  in the Building and has been agreed upon by the parties as final and correct and is not subject to challenge or dispute by either party.', metadata={'Landlord': 'Perry  &  Blair LLC', 'Lease Date': 'March  29th ,  2019', 'Lease Parties': 'THIS OFFICE LEASE  (the \"Lease\") is made and entered into as of  March  29th ,  2019 , by and between  Landlord  and  Tenant . \"Date of this Lease\" shall mean the date on which the last one of the  Landlord  and  Tenant  has signed this  Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'dsyfhh4vpeyf', 'source': 'Shorebucks LLC_CO.pdf', 'structure': 'div', 'tag': 'RentableAreaofthePremises', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:PerryBlair/docset:PerryBlair/docset:Premises[2]/docset:RentableAreaofthePremises-section/docset:RentableAreaofthePremises'})]"
+       "[Document(page_content='1.6 Rentable Area of the Premises.', metadata={'id': '5b39a1ae84d51682328dca1467be211f', 'name': 'Sample Commercial Leases/Shorebucks LLC_WA.pdf', 'structure': 'lim h1', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/dg:chunk/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:CatalystGroup/dg:chunk[6]/dg:chunk'}),\n",
+       " Document(page_content='1.6 Rentable Area of the Premises.', metadata={'id': '5b39a1ae84d51682328dca1467be211f', 'name': 'Sample Commercial Leases/Shorebucks LLC_AZ.pdf', 'structure': 'lim h1', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE-section/docset:WITNESSETH-section/docset:WITNESSETH/dg:chunk/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:MenloGroup/dg:chunk[6]/dg:chunk'}),\n",
+       " Document(page_content='1.6 Rentable Area of the Premises.', metadata={'id': '5b39a1ae84d51682328dca1467be211f', 'name': 'Sample Commercial Leases/Shorebucks LLC_FL.pdf', 'structure': 'lim h1', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:Florida-section/docset:Florida/docset:Shorebucks/dg:chunk[5]/dg:chunk'}),\n",
+       " Document(page_content='1.6 Rentable Area of the Premises.', metadata={'id': '5b39a1ae84d51682328dca1467be211f', 'name': 'Sample Commercial Leases/Shorebucks LLC_TX.pdf', 'structure': 'lim h1', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/dg:chunk/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:LandmarkLlc/dg:chunk[6]/dg:chunk'})]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -270,43 +304,42 @@
     "chain_response[\"source_documents\"]"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "At first glance the answer may seem reasonable, but it is incorrect. If you review the source chunks carefully for this answer, you will see that the chunking of the document did not end up putting the Landlord name and the rentable area in the same context, and produced irrelevant chunks therefore the answer is incorrect (should be **13,500 sq ft**)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Docugami can help here. Chunks are annotated with additional metadata created using different techniques if a user has been [using Docugami](https://help.docugami.com/home/reports). More technical approaches will be added later.\n",
     "\n",
-    "Specifically, let's look at the additional metadata that is returned on the documents returned by docugami, in the form of some simple key/value pairs on all the text chunks:"
+    "Specifically, let's ask Docugami to return XML tags on its output, as well as additional metadata:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "{'xpath': '/docset:OFFICELEASEAGREEMENT-section/docset:OFFICELEASEAGREEMENT/docset:LeaseParties',\n",
-       " 'id': 'v1bvgaozfkak',\n",
-       " 'source': 'TruTone Lane 2.docx',\n",
-       " 'structure': 'p',\n",
-       " 'tag': 'LeaseParties',\n",
-       " 'Lease Date': 'April  24  \\n\\n ,',\n",
-       " 'Landlord': 'BUBBA CENTER PARTNERSHIP',\n",
-       " 'Tenant': 'Truetone Lane LLC',\n",
-       " 'Lease Parties': 'This  OFFICE LEASE AGREEMENT  (this \"Lease\") is made and entered into by and between  BUBBA CENTER PARTNERSHIP  (\" Landlord \"), and  Truetone Lane LLC , a  Delaware  limited liability company  (\" Tenant \").'}"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'xpath': '/docset:OFFICELEASE-section/dg:chunk', 'id': '47297e277e556f3ce8b570047304560b', 'name': 'Sample Commercial Leases/Shorebucks LLC_AZ.pdf', 'source': 'Sample Commercial Leases/Shorebucks LLC_AZ.pdf', 'structure': 'h1 h1 p', 'tag': 'chunk Lease', 'Lease Date': 'March  29th , 2019', 'Landlord': 'Menlo Group', 'Tenant': 'Shorebucks LLC', 'Premises Address': '1564  E Broadway Rd ,  Tempe ,  Arizona  85282', 'Term of Lease': '96  full calendar months', 'Square Feet': '16,159'}\n"
+     ]
     }
    ],
    "source": [
-    "loader = DocugamiLoader(docset_id=\"wh2kned25uqm\")\n",
-    "documents = loader.load()\n",
-    "documents[0].metadata"
+    "loader = DocugamiLoader(docset_id=\"zo954yqy53wp\")\n",
+    "loader.include_xml_tags = (\n",
+    "    True  # for additional semantics from the Docugami knowledge graph\n",
+    ")\n",
+    "chunks = loader.load()\n",
+    "print(chunks[0].metadata)"
    ]
   },
   {
@@ -318,12 +351,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!poetry run pip install --upgrade lark --quiet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain.chains.query_constructor.schema import AttributeInfo\n",
     "from langchain.retrievers.self_query.base import SelfQueryRetriever\n",
+    "from langchain.vectorstores.chroma import Chroma\n",
     "\n",
     "EXCLUDE_KEYS = [\"id\", \"xpath\", \"structure\"]\n",
     "metadata_field_info = [\n",
@@ -332,19 +375,23 @@
     "        description=f\"The {key} for this chunk\",\n",
     "        type=\"string\",\n",
     "    )\n",
-    "    for key in documents[0].metadata\n",
+    "    for key in chunks[0].metadata\n",
     "    if key.lower() not in EXCLUDE_KEYS\n",
     "]\n",
     "\n",
-    "\n",
     "document_content_description = \"Contents of this chunk\"\n",
     "llm = OpenAI(temperature=0)\n",
-    "vectordb = Chroma.from_documents(documents=documents, embedding=embedding)\n",
+    "\n",
+    "vectordb = Chroma.from_documents(documents=chunks, embedding=embedding)\n",
     "retriever = SelfQueryRetriever.from_llm(\n",
     "    llm, vectordb, document_content_description, metadata_field_info, verbose=True\n",
     ")\n",
     "qa_chain = RetrievalQA.from_chain_type(\n",
-    "    llm=OpenAI(), chain_type=\"stuff\", retriever=retriever, return_source_documents=True\n",
+    "    llm=OpenAI(),\n",
+    "    chain_type=\"stuff\",\n",
+    "    retriever=retriever,\n",
+    "    return_source_documents=True,\n",
+    "    verbose=True,\n",
     ")"
    ]
   },
@@ -357,36 +404,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/root/Source/github/docugami.langchain/libs/langchain/langchain/chains/llm.py:275: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "query='rentable area' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='Landlord', value='DHA Group') limit=None\n"
+      "\n",
+      "\n",
+      "\u001b[1m> Entering new RetrievalQA chain...\u001b[0m\n",
+      "\n",
+      "\u001b[1m> Finished chain.\u001b[0m\n"
      ]
     },
     {
      "data": {
       "text/plain": [
        "{'query': 'What is rentable area for the property owned by DHA Group?',\n",
-       " 'result': ' The rentable area for the property owned by DHA Group is 13,500 square feet.',\n",
-       " 'source_documents': [Document(page_content='1.6 Rentable Area  of the Premises. 13,500  square feet . This square footage figure includes an add-on factor for  Common Areas  in the Building and has been agreed upon by the parties as final and correct and is not subject to challenge or dispute by either party.', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March  29th ,  2019', 'Lease Parties': 'THIS OFFICE LEASE  (the \"Lease\") is made and entered into as of  March  29th ,  2019 , by and between  Landlord  and  Tenant . \"Date of this Lease\" shall mean the date on which the last one of the  Landlord  and  Tenant  has signed this  Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'RentableAreaofthePremises', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:Premises[2]/docset:RentableAreaofthePremises-section/docset:RentableAreaofthePremises'}),\n",
-       "  Document(page_content='1.6 Rentable Area  of the Premises. 13,500  square feet . This square footage figure includes an add-on factor for  Common Areas  in the Building and has been agreed upon by the parties as final and correct and is not subject to challenge or dispute by either party.', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March  29th ,  2019', 'Lease Parties': 'THIS OFFICE LEASE  (the \"Lease\") is made and entered into as of  March  29th ,  2019 , by and between  Landlord  and  Tenant . \"Date of this Lease\" shall mean the date on which the last one of the  Landlord  and  Tenant  has signed this  Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'RentableAreaofthePremises', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:TheTerms/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/docset:DhaGroup/docset:Premises[2]/docset:RentableAreaofthePremises-section/docset:RentableAreaofthePremises'}),\n",
-       "  Document(page_content='1.11 Percentage Rent . (a) 55 %  of  Gross Revenue  to  Landlord  until  Landlord  receives  Percentage Rent  in an amount equal to the  Annual Market Rent Hurdle  (as escalated); and', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March  29th ,  2019', 'Lease Parties': 'THIS OFFICE LEASE  (the \"Lease\") is made and entered into as of  March  29th ,  2019 , by and between  Landlord  and  Tenant . \"Date of this Lease\" shall mean the date on which the last one of the  Landlord  and  Tenant  has signed this  Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'p', 'tag': 'GrossRevenue', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:PercentageRent/docset:PercentageRent/docset:PercentageRent-section/docset:PercentageRent[2]/docset:PercentageRent/docset:GrossRevenue[1]/docset:GrossRevenue'}),\n",
-       "  Document(page_content='1.11 Percentage Rent . (a) 55 %  of  Gross Revenue  to  Landlord  until  Landlord  receives  Percentage Rent  in an amount equal to the  Annual Market Rent Hurdle  (as escalated); and', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March  29th ,  2019', 'Lease Parties': 'THIS OFFICE LEASE  (the \"Lease\") is made and entered into as of  March  29th ,  2019 , by and between  Landlord  and  Tenant . \"Date of this Lease\" shall mean the date on which the last one of the  Landlord  and  Tenant  has signed this  Lease .', 'Tenant': 'Shorebucks LLC', 'id': 'md8rieecquyv', 'source': 'Shorebucks LLC_NJ.pdf', 'structure': 'p', 'tag': 'GrossRevenue', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:THISOFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCreditTheRentCredit-section/docset:GrossRentCreditTheRentCredit/docset:Period/docset:ApplicableSalesTax/docset:PercentageRent/docset:PercentageRent/docset:PercentageRent/docset:PercentageRent-section/docset:PercentageRent[2]/docset:PercentageRent/docset:GrossRevenue[1]/docset:GrossRevenue'})]}"
+       " 'result': ' The rentable area of the property owned by DHA Group is 13,500 square feet.',\n",
+       " 'source_documents': [Document(page_content='1.6 Rentable Area of the Premises.', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March  29th , 2019', 'Premises Address': '111  Bauer Dr ,  Oakland ,  New Jersey ,  07436', 'Square Feet': '13,500', 'Tenant': 'Shorebucks LLC', 'Term of Lease': '84  full calendar  months', 'id': '5b39a1ae84d51682328dca1467be211f', 'name': 'Sample Commercial Leases/Shorebucks LLC_NJ.pdf', 'source': 'Sample Commercial Leases/Shorebucks LLC_NJ.pdf', 'structure': 'lim h1', 'tag': 'chunk', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/dg:chunk/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/dg:chunk[6]/dg:chunk'}),\n",
+       "  Document(page_content='<RentableAreaofthePremises><SquareFeet>13,500 </SquareFeet>square feet. This square footage figure includes an add-on factor for Common Areas in the Building and has been agreed upon by the parties as final and correct and is not subject to challenge or dispute by either party. </RentableAreaofthePremises>', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March  29th , 2019', 'Premises Address': '111  Bauer Dr ,  Oakland ,  New Jersey ,  07436', 'Square Feet': '13,500', 'Tenant': 'Shorebucks LLC', 'Term of Lease': '84  full calendar  months', 'id': '4c06903d087f5a83e486ee42cd702d31', 'name': 'Sample Commercial Leases/Shorebucks LLC_NJ.pdf', 'source': 'Sample Commercial Leases/Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'RentableAreaofthePremises', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/dg:chunk/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/docset:DhaGroup/dg:chunk[6]/docset:RentableAreaofthePremises-section/docset:RentableAreaofthePremises'}),\n",
+       "  Document(page_content='<TheTermAnnualMarketRent>shall mean (i) for the initial Lease Year (“Year 1”) <Money>$2,239,748.00 </Money>per year (i.e., the product of the Rentable Area of the Premises multiplied by <Money>$82.00</Money>) (the “Year 1 Market Rent Hurdle”); (ii) for the Lease Year thereafter, <Percent>one hundred three percent (103%) </Percent>of the Year 1 Market Rent Hurdle, and (iii) for each Lease Year thereafter until the termination or expiration of this Lease, the Annual Market Rent Threshold shall be <AnnualMarketRentThreshold>one hundred three percent (103%) </AnnualMarketRentThreshold>of the Annual Market Rent Threshold for the immediately prior Lease Year. </TheTermAnnualMarketRent>', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March  29th , 2019', 'Premises Address': '111  Bauer Dr ,  Oakland ,  New Jersey ,  07436', 'Square Feet': '13,500', 'Tenant': 'Shorebucks LLC', 'Term of Lease': '84  full calendar  months', 'id': '6b90beeadace5d4d12b25706fb48e631', 'name': 'Sample Commercial Leases/Shorebucks LLC_NJ.pdf', 'source': 'Sample Commercial Leases/Shorebucks LLC_NJ.pdf', 'structure': 'div', 'tag': 'TheTermAnnualMarketRent', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCredit-section/docset:GrossRentCredit/dg:chunk/dg:chunk/dg:chunk/dg:chunk[2]/docset:PercentageRent/dg:chunk[2]/dg:chunk[2]/docset:TenantSRevenue/dg:chunk[2]/docset:TenantSRevenue/dg:chunk[3]/docset:TheTermAnnualMarketRent-section/docset:TheTermAnnualMarketRent'}),\n",
+       "  Document(page_content='1.11 Percentage Rent.\\n (a) <GrossRevenue><Percent>55% </Percent>of Gross Revenue to Landlord until Landlord receives Percentage Rent in an amount equal to the Annual Market Rent Hurdle (as escalated); and </GrossRevenue>', metadata={'Landlord': 'DHA Group', 'Lease Date': 'March  29th , 2019', 'Premises Address': '111  Bauer Dr ,  Oakland ,  New Jersey ,  07436', 'Square Feet': '13,500', 'Tenant': 'Shorebucks LLC', 'Term of Lease': '84  full calendar  months', 'id': 'c8bb9cbedf65a578d9db3f25f519dd3d', 'name': 'Sample Commercial Leases/Shorebucks LLC_NJ.pdf', 'source': 'Sample Commercial Leases/Shorebucks LLC_NJ.pdf', 'structure': 'lim h1 lim p', 'tag': 'chunk GrossRevenue', 'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/docset:GrossRentCredit-section/docset:GrossRentCredit/dg:chunk/dg:chunk/dg:chunk/docset:PercentageRent/dg:chunk[1]/dg:chunk[1]'})]}"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -403,6 +446,198 @@
    "source": [
     "This time the answer is correct, since the self-querying retriever created a filter on the landlord attribute of the metadata, correctly filtering to document that specifically is about the DHA Group landlord. The resulting source chunks are all relevant to this landlord, and this improves answer accuracy even though the landlord is not directly mentioned in the specific chunk that contains the correct answer."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Advanced Topic: Small-to-Big Retrieval with Document Knowledge Graph Hierarchy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Documents are inherently semi-structured and the DocugamiLoader is able to navigate the semantic and structural contours of the document to provide parent chunk references on the chunks it returns. This is useful e.g. with the [MultiVector Retriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector) for [small-to-big](https://www.youtube.com/watch?v=ihSiRrOUwmg) retrieval.\n",
+    "\n",
+    "To get parent chunk references, you can set `loader.parent_hierarchy_levels` to a non-zero value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import Dict, List\n",
+    "\n",
+    "from langchain.document_loaders import DocugamiLoader\n",
+    "from langchain.schema.document import Document\n",
+    "\n",
+    "loader = DocugamiLoader(docset_id=\"zo954yqy53wp\")\n",
+    "loader.include_xml_tags = (\n",
+    "    True  # for additional semantics from the Docugami knowledge graph\n",
+    ")\n",
+    "loader.parent_hierarchy_levels = 3  # for expanded context\n",
+    "loader.max_text_length = (\n",
+    "    1024 * 8\n",
+    ")  # 8K chars are roughly 2K tokens (ref: https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them)\n",
+    "loader.include_project_metadata_in_doc_metadata = (\n",
+    "    False  # Not filtering on vector metadata, so remove to lighten the vectors\n",
+    ")\n",
+    "chunks: List[Document] = loader.load()\n",
+    "\n",
+    "# build separate maps of parent and child chunks\n",
+    "parents_by_id: Dict[str, Document] = {}\n",
+    "children_by_id: Dict[str, Document] = {}\n",
+    "for chunk in chunks:\n",
+    "    chunk_id = chunk.metadata.get(\"id\")\n",
+    "    parent_chunk_id = chunk.metadata.get(loader.parent_id_key)\n",
+    "    if not parent_chunk_id:\n",
+    "        # parent chunk\n",
+    "        parents_by_id[chunk_id] = chunk\n",
+    "    else:\n",
+    "        # child chunk\n",
+    "        children_by_id[chunk_id] = chunk"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PARENT CHUNK 7df09fbfc65bb8377054808aac2d16fd: page_content='OFFICE LEASE\\n THIS OFFICE LEASE\\n <Lease>(the \"Lease\") is made and entered into as of <LeaseDate>March 29th, 2019</LeaseDate>, by and between Landlord and Tenant. \"Date of this Lease\" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease. </Lease>\\nW I T N E S S E T H\\n <TheTerms> Subject to and on the terms and conditions of this Lease, Landlord leases to Tenant and Tenant hires from Landlord the Premises. </TheTerms>\\n1. BASIC LEASE INFORMATION AND DEFINED TERMS.\\nThe key business terms of this Lease and the defined terms used in this Lease are as follows:' metadata={'xpath': '/docset:OFFICELEASE-section/dg:chunk', 'id': '7df09fbfc65bb8377054808aac2d16fd', 'name': 'Sample Commercial Leases/Shorebucks LLC_NJ.pdf', 'source': 'Sample Commercial Leases/Shorebucks LLC_NJ.pdf', 'structure': 'h1 h1 p h1 p lim h1 p', 'tag': 'chunk Lease chunk TheTerms'}\n",
+      "CHUNK 47297e277e556f3ce8b570047304560b: page_content='OFFICE LEASE\\n THIS OFFICE LEASE\\n <Lease>(the \"Lease\") is made and entered into as of <LeaseDate>March 29th, 2019</LeaseDate>, by and between Landlord and Tenant. \"Date of this Lease\" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease. </Lease>' metadata={'xpath': '/docset:OFFICELEASE-section/dg:chunk', 'id': '47297e277e556f3ce8b570047304560b', 'name': 'Sample Commercial Leases/Shorebucks LLC_NJ.pdf', 'source': 'Sample Commercial Leases/Shorebucks LLC_NJ.pdf', 'structure': 'h1 h1 p', 'tag': 'chunk Lease', 'doc_id': '7df09fbfc65bb8377054808aac2d16fd'}\n",
+      "PARENT CHUNK bb84925da3bed22c30ea1bdc173ff54f: page_content='OFFICE LEASE\\n THIS OFFICE LEASE\\n <Lease>(the \"Lease\") is made and entered into as of <LeaseDate>January 8th, 2018</LeaseDate>, by and between Landlord and Tenant. \"Date of this Lease\" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease. </Lease>\\nW I T N E S S E T H\\n <TheTerms> Subject to and on the terms and conditions of this Lease, Landlord leases to Tenant and Tenant hires from Landlord the Premises. </TheTerms>\\n1. BASIC LEASE INFORMATION AND DEFINED TERMS.\\nThe key business terms of this Lease and the defined terms used in this Lease are as follows:\\n1.1 Landlord.\\n <Landlord>Catalyst Group LLC </Landlord>' metadata={'xpath': '/docset:OFFICELEASE-section/dg:chunk', 'id': 'bb84925da3bed22c30ea1bdc173ff54f', 'name': 'Sample Commercial Leases/Shorebucks LLC_WA.pdf', 'source': 'Sample Commercial Leases/Shorebucks LLC_WA.pdf', 'structure': 'h1 h1 p h1 p lim h1 p lim h1 div', 'tag': 'chunk Lease chunk TheTerms chunk Landlord'}\n",
+      "CHUNK 2f1746cbd546d1d61a9250c50de7a7fa: page_content='W I T N E S S E T H\\n <TheTerms> Subject to and on the terms and conditions of this Lease, Landlord leases to Tenant and Tenant hires from Landlord the Premises. </TheTerms>' metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:WITNESSETH-section/dg:chunk', 'id': '2f1746cbd546d1d61a9250c50de7a7fa', 'name': 'Sample Commercial Leases/Shorebucks LLC_WA.pdf', 'source': 'Sample Commercial Leases/Shorebucks LLC_WA.pdf', 'structure': 'h1 p', 'tag': 'chunk TheTerms', 'doc_id': 'bb84925da3bed22c30ea1bdc173ff54f'}\n",
+      "PARENT CHUNK 0b0d765b6e504a6ba54fa76b203e62ec: page_content='OFFICE LEASE\\n THIS OFFICE LEASE\\n <Lease>(the \"Lease\") is made and entered into as of <LeaseDate>January 8th, 2018</LeaseDate>, by and between Landlord and Tenant. \"Date of this Lease\" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease. </Lease>\\nW I T N E S S E T H\\n <TheTerms> Subject to and on the terms and conditions of this Lease, Landlord leases to Tenant and Tenant hires from Landlord the Premises. </TheTerms>\\n1. BASIC LEASE INFORMATION AND DEFINED TERMS.\\nThe key business terms of this Lease and the defined terms used in this Lease are as follows:\\n1.1 Landlord.\\n <Landlord>Catalyst Group LLC </Landlord>\\n1.2 Tenant.\\n <Tenant>Shorebucks LLC </Tenant>' metadata={'xpath': '/docset:OFFICELEASE-section/dg:chunk', 'id': '0b0d765b6e504a6ba54fa76b203e62ec', 'name': 'Sample Commercial Leases/Shorebucks LLC_WA.pdf', 'source': 'Sample Commercial Leases/Shorebucks LLC_WA.pdf', 'structure': 'h1 h1 p h1 p lim h1 p lim h1 div lim h1 div', 'tag': 'chunk Lease chunk TheTerms chunk Landlord chunk Tenant'}\n",
+      "CHUNK b362dfe776ec5a7a66451a8c7c220b59: page_content='1. BASIC LEASE INFORMATION AND DEFINED TERMS.' metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/dg:chunk/dg:chunk/docset:BasicLeaseInformation/dg:chunk', 'id': 'b362dfe776ec5a7a66451a8c7c220b59', 'name': 'Sample Commercial Leases/Shorebucks LLC_WA.pdf', 'source': 'Sample Commercial Leases/Shorebucks LLC_WA.pdf', 'structure': 'lim h1', 'tag': 'chunk', 'doc_id': '0b0d765b6e504a6ba54fa76b203e62ec'}\n",
+      "PARENT CHUNK c942010baaf76aa4d4657769492f6edb: page_content='OFFICE LEASE\\n THIS OFFICE LEASE\\n <Lease>(the \"Lease\") is made and entered into as of <LeaseDate>January 8th, 2018</LeaseDate>, by and between Landlord and Tenant. \"Date of this Lease\" shall mean the date on which the last one of the Landlord and Tenant has signed this Lease. </Lease>\\nW I T N E S S E T H\\n <TheTerms> Subject to and on the terms and conditions of this Lease, Landlord leases to Tenant and Tenant hires from Landlord the Premises. </TheTerms>\\n1. BASIC LEASE INFORMATION AND DEFINED TERMS.\\nThe key business terms of this Lease and the defined terms used in this Lease are as follows:\\n1.1 Landlord.\\n <Landlord>Catalyst Group LLC </Landlord>\\n1.2 Tenant.\\n <Tenant>Shorebucks LLC </Tenant>\\n1.3 Building.\\n <Building>The building containing the Premises located at <PremisesAddress><PremisesStreetAddress><MainStreet>600 </MainStreet><StreetName>Main Street</StreetName></PremisesStreetAddress>, <City>Bellevue</City>, <State>WA</State>, <Premises>98004</Premises></PremisesAddress>. The Building is located within the Project. </Building>' metadata={'xpath': '/docset:OFFICELEASE-section/dg:chunk', 'id': 'c942010baaf76aa4d4657769492f6edb', 'name': 'Sample Commercial Leases/Shorebucks LLC_WA.pdf', 'source': 'Sample Commercial Leases/Shorebucks LLC_WA.pdf', 'structure': 'h1 h1 p h1 p lim h1 p lim h1 div lim h1 div lim h1 div', 'tag': 'chunk Lease chunk TheTerms chunk Landlord chunk Tenant chunk Building'}\n",
+      "CHUNK a95971d693b7aa0f6640df1fbd18c2ba: page_content='The key business terms of this Lease and the defined terms used in this Lease are as follows:' metadata={'xpath': '/docset:OFFICELEASE-section/docset:OFFICELEASE-section/docset:OFFICELEASE/docset:WITNESSETH-section/docset:WITNESSETH/dg:chunk/dg:chunk/docset:BasicLeaseInformation/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS-section/docset:BASICLEASEINFORMATIONANDDEFINEDTERMS/dg:chunk', 'id': 'a95971d693b7aa0f6640df1fbd18c2ba', 'name': 'Sample Commercial Leases/Shorebucks LLC_WA.pdf', 'source': 'Sample Commercial Leases/Shorebucks LLC_WA.pdf', 'structure': 'p', 'tag': 'chunk', 'doc_id': 'c942010baaf76aa4d4657769492f6edb'}\n",
+      "PARENT CHUNK f34b649cde7fc4ae156849a56d690495: page_content='W I T N E S S E T H\\n <TheTerms> Subject to and on the terms and conditions of this Lease, Landlord leases to Tenant and Tenant hires from Landlord the Premises. </TheTerms>\\n1. BASIC LEASE INFORMATION AND DEFINED TERMS.\\n<BASICLEASEINFORMATIONANDDEFINEDTERMS>The key business terms of this Lease and the defined terms used in this Lease are as follows: </BASICLEASEINFORMATIONANDDEFINEDTERMS>\\n1.1 Landlord.\\n <Landlord><Landlord>Menlo Group</Landlord>, a <USState>Delaware </USState>limited liability company authorized to transact business in <USState>Arizona</USState>. </Landlord>\\n1.2 Tenant.\\n <Tenant>Shorebucks LLC </Tenant>\\n1.3 Building.\\n <Building>The building containing the Premises located at <PremisesAddress><PremisesStreetAddress><Premises>1564 </Premises><Premises>E Broadway Rd</Premises></PremisesStreetAddress>, <City>Tempe</City>, <USState>Arizona </USState><Premises>85282</Premises></PremisesAddress>. The Building is located within the Project. </Building>\\n1.4 Project.\\n <Project>The parcel of land and the buildings and improvements located on such land known as Shorebucks Office <ShorebucksOfficeAddress><ShorebucksOfficeStreetAddress><ShorebucksOffice>6 </ShorebucksOffice><ShorebucksOffice6>located at <Number>1564 </Number>E Broadway Rd</ShorebucksOffice6></ShorebucksOfficeStreetAddress>, <City>Tempe</City>, <USState>Arizona </USState><Number>85282</Number></ShorebucksOfficeAddress>. The Project is legally described in EXHIBIT \"A\" to this Lease. </Project>' metadata={'xpath': '/dg:chunk/docset:WITNESSETH-section/dg:chunk', 'id': 'f34b649cde7fc4ae156849a56d690495', 'name': 'Sample Commercial Leases/Shorebucks LLC_AZ.docx', 'source': 'Sample Commercial Leases/Shorebucks LLC_AZ.docx', 'structure': 'h1 p lim h1 div lim h1 div lim h1 div lim h1 div lim h1 div', 'tag': 'chunk TheTerms BASICLEASEINFORMATIONANDDEFINEDTERMS chunk Landlord chunk Tenant chunk Building chunk Project'}\n",
+      "CHUNK 21b4d9517f7ccdc0e3a028ce5043a2a0: page_content='1.1 Landlord.\\n <Landlord><Landlord>Menlo Group</Landlord>, a <USState>Delaware </USState>limited liability company authorized to transact business in <USState>Arizona</USState>. </Landlord>' metadata={'xpath': '/dg:chunk/docset:WITNESSETH-section/docset:WITNESSETH/dg:chunk[1]/dg:chunk[1]/dg:chunk/dg:chunk[2]/dg:chunk', 'id': '21b4d9517f7ccdc0e3a028ce5043a2a0', 'name': 'Sample Commercial Leases/Shorebucks LLC_AZ.docx', 'source': 'Sample Commercial Leases/Shorebucks LLC_AZ.docx', 'structure': 'lim h1 div', 'tag': 'chunk Landlord', 'doc_id': 'f34b649cde7fc4ae156849a56d690495'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Explore some of the parent chunk relationships\n",
+    "for id, chunk in list(children_by_id.items())[:5]:\n",
+    "    parent_chunk_id = chunk.metadata.get(loader.parent_id_key)\n",
+    "    if parent_chunk_id:\n",
+    "        # child chunks have the parent chunk id set\n",
+    "        print(f\"PARENT CHUNK {parent_chunk_id}: {parents_by_id[parent_chunk_id]}\")\n",
+    "        print(f\"CHUNK {id}: {chunk}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.retrievers.multi_vector import MultiVectorRetriever, SearchType\n",
+    "from langchain.storage import InMemoryStore\n",
+    "from langchain.vectorstores.chroma import Chroma\n",
+    "\n",
+    "# The vectorstore to use to index the child chunks\n",
+    "vectorstore = Chroma(collection_name=\"big2small\", embedding_function=OpenAIEmbeddings())\n",
+    "\n",
+    "# The storage layer for the parent documents\n",
+    "store = InMemoryStore()\n",
+    "\n",
+    "# The retriever (empty to start)\n",
+    "retriever = MultiVectorRetriever(\n",
+    "    vectorstore=vectorstore,\n",
+    "    docstore=store,\n",
+    "    search_type=SearchType.mmr,  # use max marginal relevance search\n",
+    "    search_kwargs={\"k\": 2},\n",
+    ")\n",
+    "\n",
+    "# Add child chunks to vector store\n",
+    "retriever.vectorstore.add_documents(list(children_by_id.values()))\n",
+    "\n",
+    "# Add parent chunks to docstore\n",
+    "retriever.docstore.mset(parents_by_id.items())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24. SIGNS.\n",
+      " <SIGNS>No signage shall be placed by Tenant on any portion of the Project. However, Tenant shall be permitted to place a sign bearing its name in a location approved by Landlord near the entrance to the Premises (at Tenant's cost) and will be furnished a single listing of its name in the Building's directory (at Landlord's cost), all in accordance with the criteria adopted <Frequency>from time to time </Frequency>by Landlord for the Project. Any changes or additional listings in the directory shall be furnished (subject to availability of space) for the then Building Standard charge. </SIGNS>\n",
+      "43090337ed2409e0da24ee07e2adbe94\n",
+      "<TheExterior> Tenant agrees that all signs, awnings, protective gates, security devices and other installations visible from the exterior of the Premises shall be subject to Landlord's prior written approval, shall be subject to the prior approval of the <Org>Landmarks </Org><Landmarks>Preservation Commission </Landmarks>of the City of <USState>New <Org>York</Org></USState>, if required, and shall not interfere with or block either of the adjacent stores, provided, however, that Landlord shall not unreasonably withhold consent for signs that Tenant desires to install. Tenant agrees that any permitted signs, awnings, protective gates, security devices, and other installations shall be installed at Tenant’s sole cost and expense professionally prepared and dignified and subject to Landlord's prior written approval, which shall not be unreasonably withheld, delayed or conditioned, and subject to such reasonable rules and restrictions as Landlord <Frequency>from time to time </Frequency>may impose. Tenant shall submit to Landlord drawings of the proposed signs and other installations, showing the size, color, illumination and general appearance thereof, together with a statement of the manner in which the same are to be affixed to the Premises. Tenant shall not commence the installation of the proposed signs and other installations unless and until Landlord shall have approved the same in writing. . Tenant shall not install any neon sign. The aforesaid signs shall be used solely for the purpose of identifying Tenant's business. No changes shall be made in the signs and other installations without first obtaining Landlord's prior written consent thereto, which consent shall not be unreasonably withheld, delayed or conditioned. Tenant shall, at its own cost and expense, obtain and exhibit to Landlord such permits or certificates of approval as Tenant may be required to obtain from any and all City, State and other authorities having jurisdiction covering the erection, installation, maintenance or use of said signs or other installations, and Tenant shall maintain the said signs and other installations together with any appurtenances thereto in good order and condition and to the satisfaction of the Landlord and in accordance with any and all orders, regulations, requirements and rules of any public authorities having jurisdiction thereover. Landlord consents to Tenant’s Initial Signage described in annexed Exhibit D. </TheExterior>\n",
+      "54ddfc3e47f41af7e747b2bc439ea96b\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Query vector store directly, should return chunks\n",
+    "found_chunks = vectorstore.similarity_search(\n",
+    "    \"what signs does Birch Street allow on their property?\", k=2\n",
+    ")\n",
+    "\n",
+    "for chunk in found_chunks:\n",
+    "    print(chunk.page_content)\n",
+    "    print(chunk.metadata[loader.parent_id_key])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "21. SERVICES AND UTILITIES.\n",
+      " <SERVICESANDUTILITIES>Landlord shall have no obligation to provide any utilities or services to the Premises other than passenger elevator service to the Premises. Tenant shall be solely responsible for and shall promptly pay all charges for water, electricity, or any other utility used or consumed in the Premises, including all costs associated with separately metering for the Premises. Tenant shall be responsible for repairs and maintenance to exit lighting, emergency lighting, and fire extinguishers for the Premises. Tenant is responsible for interior janitorial, pest control, and waste removal services. Landlord may at any time change the electrical utility provider for the Building. Tenant’s use of electrical, HVAC, or other services furnished by Landlord shall not exceed, either in voltage, rated capacity, use, or overall load, that which Landlord deems to be standard for the Building. In no event shall Landlord be liable for damages resulting from the failure to furnish any service, and any interruption or failure shall in no manner entitle Tenant to any remedies including abatement of Rent. If at any time during the Lease Term the Project has any type of card access system for the Parking Areas or the Building, Tenant shall purchase access cards for all occupants of the Premises from Landlord at a Building Standard charge and shall comply with Building Standard terms relating to access to the Parking Areas and the Building. </SERVICESANDUTILITIES>\n",
+      "22. SECURITY DEPOSIT.\n",
+      " <SECURITYDEPOSIT>The Security Deposit shall be held by Landlord as security for Tenant's full and faithful performance of this Lease including the payment of Rent. Tenant grants Landlord a security interest in the Security Deposit. The Security Deposit may be commingled with other funds of Landlord and Landlord shall have no liability for payment of any interest on the Security Deposit. Landlord may apply the Security Deposit to the extent required to cure any default by Tenant. If Landlord so applies the Security Deposit, Tenant shall deliver to Landlord the amount necessary to replenish the Security Deposit to its original sum within <Deliver>five days </Deliver>after notice from Landlord. The Security Deposit shall not be deemed an advance payment of Rent or a measure of damages for any default by Tenant, nor shall it be a defense to any action that Landlord may bring against Tenant. </SECURITYDEPOSIT>\n",
+      "23. GOVERNMENTAL REGULATIONS.\n",
+      " <GOVERNMENTALREGULATIONS>Tenant, at Tenant's sole cost and expense, shall promptly comply (and shall cause all subtenants and licensees to comply) with all laws, codes, and ordinances of governmental authorities, including the Americans with Disabilities Act of <AmericanswithDisabilitiesActDate>1990 </AmericanswithDisabilitiesActDate>as amended (the \"ADA\"), and all recorded covenants and restrictions affecting the Project, pertaining to Tenant, its conduct of business, and its use and occupancy of the Premises, including the performance of any work to the Common Areas required because of Tenant's specific use (as opposed to general office use) of the Premises or Alterations to the Premises made by Tenant. </GOVERNMENTALREGULATIONS>\n",
+      "24. SIGNS.\n",
+      " <SIGNS>No signage shall be placed by Tenant on any portion of the Project. However, Tenant shall be permitted to place a sign bearing its name in a location approved by Landlord near the entrance to the Premises (at Tenant's cost) and will be furnished a single listing of its name in the Building's directory (at Landlord's cost), all in accordance with the criteria adopted <Frequency>from time to time </Frequency>by Landlord for the Project. Any changes or additional listings in the directory shall be furnished (subject to availability of space) for the then Building Standard charge. </SIGNS>\n",
+      "25. BROKER.\n",
+      " <BROKER>Landlord and Tenant each represent and warrant that they have neither consulted nor negotiated with any broker or finder regarding the Premises, except the Landlord's Broker and Tenant's Broker. Tenant shall indemnify, defend, and hold Landlord harmless from and against any claims for commissions from any real estate broker other than Landlord's Broker and Tenant's Broker with whom Tenant has dealt in connection with this Lease. Landlord shall indemnify, defend, and hold Tenant harmless from and against payment of any leasing commission due Landlord's Broker and Tenant's Broker in connection with this Lease and any claims for commissions from any real estate broker other than Landlord's Broker and Tenant's Broker with whom Landlord has dealt in connection with this Lease. The terms of this article shall survive the expiration or earlier termination of this Lease. </BROKER>\n",
+      "26. END OF TERM.\n",
+      " <ENDOFTERM>Tenant shall surrender the Premises to Landlord at the expiration or sooner termination of this Lease or Tenant's right of possession in good order and condition, broom-clean, except for reasonable wear and tear. All Alterations made by Landlord or Tenant to the Premises shall become Landlord's property on the expiration or sooner termination of the Lease Term. On the expiration or sooner termination of the Lease Term, Tenant, at its expense, shall remove from the Premises all of Tenant's personal property, all computer and telecommunications wiring, and all Alterations that Landlord designates by notice to Tenant. Tenant shall also repair any damage to the Premises caused by the removal. Any items of Tenant's property that shall remain in the Premises after the expiration or sooner termination of the Lease Term, may, at the option of Landlord and without notice, be deemed to have been abandoned, and in that case, those items may be retained by Landlord as its property to be disposed of by Landlord, without accountability or notice to Tenant or any other party, in the manner Landlord shall determine, at Tenant's expense. </ENDOFTERM>\n",
+      "27. ATTORNEYS' FEES.\n",
+      " <ATTORNEYSFEES>Except as otherwise provided in this Lease, the prevailing party in any litigation or other dispute resolution proceeding, including arbitration, arising out of or in any manner based on or relating to this Lease, including tort actions and actions for injunctive, declaratory, and provisional relief, shall be entitled to recover from the losing party actual attorneys' fees and costs, including fees for litigating the entitlement to or amount of fees or costs owed under this provision, and fees in connection with bankruptcy, appellate, or collection proceedings. No person or entity other than Landlord or Tenant has any right to recover fees under this paragraph. In addition, if Landlord becomes a party to any suit or proceeding affecting the Premises or involving this Lease or Tenant's interest under this Lease, other than a suit between Landlord and Tenant, or if Landlord engages counsel to collect any of the amounts owed under this Lease, or to enforce performance of any of the agreements, conditions, covenants, provisions, or stipulations of this Lease, without commencing litigation, then the costs, expenses, and reasonable attorneys' fees and disbursements incurred by Landlord shall be paid to Landlord by Tenant. </ATTORNEYSFEES>\n",
+      "43090337ed2409e0da24ee07e2adbe94\n",
+      "<TenantsSoleCost> Tenant, at Tenant's sole cost and expense, shall be responsible for the removal and disposal of all of garbage, waste, and refuse from the Premises on a <Frequency>daily </Frequency>basis. Tenant shall cause all garbage, waste and refuse to be stored within the Premises until <Stored>thirty (30) minutes </Stored>before closing, except that Tenant shall be permitted, to the extent permitted by law, to place garbage outside the Premises after the time specified in the immediately preceding sentence for pick up prior to <PickUp>6:00 A.M. </PickUp>next following. Garbage shall be placed at the edge of the sidewalk in front of the Premises at the location furthest from he main entrance to the Building or such other location in front of the Building as may be specified by Landlord. </TenantsSoleCost>\n",
+      "<ItsSoleCost> Tenant, at its sole cost and expense, agrees to use all reasonable diligence in accordance with the best prevailing methods for the prevention and extermination of vermin, rats, and mice, mold, fungus, allergens, <Bacterium>bacteria </Bacterium>and all other similar conditions in the Premises. Tenant, at Tenant's expense, shall cause the Premises to be exterminated <Exterminated>from time to time </Exterminated>to the reasonable satisfaction of Landlord and shall employ licensed exterminating companies. Landlord shall not be responsible for any cleaning, waste removal, janitorial, or similar services for the Premises, and Tenant sha ll not be entitled to seek any abatement, setoff or credit from the Landlord in the event any conditions described in this Article are found to exist in the Premises. </ItsSoleCost>\n",
+      "42B. Sidewalk Use and Maintenance\n",
+      "<TheSidewalk> Tenant shall, at its sole cost and expense, keep the sidewalk in front of the Premises 18 inches into the street from the curb clean free of garbage, waste, refuse, excess water, snow, and ice and Tenant shall pay, as additional rent, any fine, cost, or expense caused by Tenant's failure to do so. In the event Tenant operates a sidewalk café, Tenant shall, at its sole cost and expense, maintain, repair, and replace as necessary, the sidewalk in front of the Premises and the metal trapdoor leading to the basement of the Premises, if any. Tenant shall post warning signs and cones on all sides of any side door when in use and attach a safety bar across any such door at all times when open. </TheSidewalk>\n",
+      "<Display> In no event shall Tenant use, or permit to be used, the space adjacent to or any other space outside of the Premises, for display, sale or any other similar undertaking; except [1] in the event of a legal and licensed “street fair” type program or [<Number>2</Number>] if the local zoning, Community Board [if applicable] and other municipal laws, rules and regulations, allow for sidewalk café use and, if such I s the case, said operation shall be in strict accordance with all of the aforesaid requirements and conditions. . In no event shall Tenant use, or permit to be used, any advertising medium and/or loud speaker and/or sound amplifier and/or radio or television broadcast which may be heard outside of the Premises or which does not comply with the reasonable rules and regulations of Landlord which then will be in effect. </Display>\n",
+      "42C. Store Front Maintenance\n",
+      " <TheBulkheadAndSecurityGate> Tenant agrees to wash the storefront, including the bulkhead and security gate, from the top to the ground, monthly or more often as Landlord reasonably requests and make all repairs and replacements as and when deemed necessary by Landlord, to all windows and plate and ot her glass in or about the Premises and the security gate, if any. In case of any default by Tenant in maintaining the storefront as herein provided, Landlord may do so at its own expense and bill the cost thereof to Tenant as additional rent. </TheBulkheadAndSecurityGate>\n",
+      "42D. Music, Noise, and Vibration\n",
+      "4474c92ae7ccec9184ed2fef9f072734\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Query retriever, should return parents (using MMR since that was set as search_type above)\n",
+    "retrieved_parent_docs = retriever.get_relevant_documents(\n",
+    "    \"what signs does Birch Street allow on their property?\"\n",
+    ")\n",
+    "for chunk in retrieved_parent_docs:\n",
+    "    print(chunk.page_content)\n",
+    "    print(chunk.metadata[\"id\"])"
+   ]
   }
  ],
  "metadata": {

docs/docs/integrations/document_loaders/dropbox.ipynb

@@ -15,7 +15,7 @@
     "1. Create a Dropbox app.\n",
     "2. Give the app these scope permissions: `files.metadata.read` and `files.content.read`.\n",
     "3. Generate access token: https://www.dropbox.com/developers/apps/create.\n",
-    "4. `pip install dropbox` (requires `pip install unstructured` for PDF filetype).\n",
+    "4. `pip install dropbox` (requires `pip install \"unstructured[pdf]\"` for PDF filetype).\n",
     "\n",
     "## Instructions\n",
     "\n",

docs/docs/integrations/document_loaders/embaas.ipynb

@@ -1,167 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "# Embaas\n",
-    "[embaas](https://embaas.io) is a fully managed NLP API service that offers features like embedding generation, document text extraction, document to embeddings and more. You can choose a [variety of pre-trained models](https://embaas.io/docs/models/embeddings).\n",
-    "\n",
-    "### Prerequisites\n",
-    "Create a free embaas account at [https://embaas.io/register](https://embaas.io/register) and generate an [API key](https://embaas.io/dashboard/api-keys)\n",
-    "\n",
-    "### Document Text Extraction API\n",
-    "The document text extraction API allows you to extract the text from a given document. The API supports a variety of document formats, including PDF, mp3, mp4 and more. For a full list of supported formats, check out the API docs (link below)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Set API key\n",
-    "embaas_api_key = \"YOUR_API_KEY\"\n",
-    "# or set environment variable\n",
-    "os.environ[\"EMBAAS_API_KEY\"] = \"YOUR_API_KEY\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "#### Using a blob (bytes)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders.blob_loaders import Blob\n",
-    "from langchain.document_loaders.embaas import EmbaasBlobLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "blob_loader = EmbaasBlobLoader()\n",
-    "blob = Blob.from_path(\"example.pdf\")\n",
-    "documents = blob_loader.load(blob)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-06-12T22:19:48.380467Z",
-     "start_time": "2023-06-12T22:19:48.366886Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# You can also directly create embeddings with your preferred embeddings model\n",
-    "blob_loader = EmbaasBlobLoader(params={\"model\": \"e5-large-v2\", \"should_embed\": True})\n",
-    "blob = Blob.from_path(\"example.pdf\")\n",
-    "documents = blob_loader.load(blob)\n",
-    "\n",
-    "print(documents[0][\"metadata\"][\"embedding\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "#### Using a file"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "from langchain.document_loaders.embaas import EmbaasLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "file_loader = EmbaasLoader(file_path=\"example.pdf\")\n",
-    "documents = file_loader.load()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2023-06-12T22:24:31.894665Z",
-     "start_time": "2023-06-12T22:24:31.880857Z"
-    },
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "# Disable automatic text splitting\n",
-    "file_loader = EmbaasLoader(file_path=\"example.mp3\", params={\"should_chunk\": False})\n",
-    "documents = file_loader.load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false
-   },
-   "source": [
-    "For more detailed information about the embaas document text extraction API, please refer to [the official embaas API documentation](https://embaas.io/api-reference)."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 2
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}

docs/docs/integrations/document_loaders/example_data/notebook.md

@@ -1,29 +0,0 @@
-# Notebook
-
-This notebook covers how to load data from an .ipynb notebook into a format suitable by LangChain.
-
-
-
-
-```python
-from langchain.document_loaders import NotebookLoader
-```
-
-
-```python
-loader = NotebookLoader("example_data/notebook.ipynb")
-```
-
-`NotebookLoader.load()` loads the `.ipynb` notebook file into a `Document` object.
-
-**Parameters**:
-
-* `include_outputs` (bool): whether to include cell outputs in the resulting document (default is False).
-* `max_output_length` (int): the maximum number of characters to include from each cell output (default is 10).
-* `remove_newline` (bool): whether to remove newline characters from the cell sources and outputs (default is False).
-* `traceback` (bool): whether to include full traceback (default is False).
-
-
-```python
-loader.load(include_outputs=True, max_output_length=20, remove_newline=True)
-```

docs/docs/integrations/document_loaders/google_drive.ipynb

@@ -217,7 +217,7 @@
     "It's compatible with the ̀`langchain.document_loaders.GoogleDriveLoader` and can be used\n",
     "in its place.\n",
     "\n",
-    "To be compatible with containers, the authentication uses an environment variable ̀GOOGLE_ACCOUNT_FILE` to credential file (for user or service)."
+    "To be compatible with containers, the authentication uses an environment variable `̀GOOGLE_ACCOUNT_FILE` to credential file (for user or service)."
    ]
   },
   {
@@ -331,6 +331,7 @@
     "Some pre-formated request are proposed (use `{query}`, `{folder_id}` and/or `{mime_type}`):\n",
     "\n",
     "You can customize the criteria to select the files. A set of predefined filter are proposed:\n",
+    "\n",
     "| template                               | description                                                           |\n",
     "| -------------------------------------- | --------------------------------------------------------------------- |\n",
     "| gdrive-all-in-folder                   | Return all compatible files from a `folder_id`                        |\n",
@@ -401,6 +402,14 @@
    "id": "375bb465-8f69-407b-94bd-ffa3718ef500",
    "metadata": {},
    "source": [
+    "The conversion can manage in Markdown format:\n",
+    "- bullet\n",
+    "- link\n",
+    "- table\n",
+    "- titles\n",
+    "\n",
+    "Set the attribut `return_link` to `True` to export links.\n",
+    "\n",
     "#### Modes for GSlide and GSheet\n",
     "The parameter mode accepts different values:\n",
     "\n",
@@ -408,12 +417,6 @@
     "- \"snippets\": return the description of each file (set in metadata of Google Drive files).\n",
     "\n",
     "\n",
-    "The conversion can manage in Markdown format:\n",
-    "- bullet\n",
-    "- link\n",
-    "- table\n",
-    "- titles\n",
-    "\n",
     "The parameter `gslide_mode` accepts different values:\n",
     "\n",
     "- \"single\" : one document with <PAGE BREAK>\n",
@@ -503,14 +506,6 @@
     "    print(\"---\")\n",
     "    print(doc.page_content.strip()[:60] + \"...\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "51efa73a-4e2d-4f9c-abaf-6c9bde2ff69d",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

docs/docs/integrations/document_loaders/onenote.ipynb

@@ -0,0 +1,118 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6125a85e",
+   "metadata": {},
+   "source": [
+    "# Microsoft OneNote\n",
+    "\n",
+    "This notebook covers how to load documents from `OneNote`.\n",
+    "\n",
+    "## Prerequisites\n",
+    "1. Register an application with the [Microsoft identity platform](https://learn.microsoft.com/en-us/azure/active-directory/develop/quickstart-register-app) instructions.\n",
+    "2. When registration finishes, the Azure portal displays the app registration's Overview pane. You see the Application (client) ID. Also called the `client ID`, this value uniquely identifies your application in the Microsoft identity platform.\n",
+    "3. During the steps you will be following at **item 1**, you can set the redirect URI as `http://localhost:8000/callback`\n",
+    "4. During the steps you will be following at **item 1**, generate a new password (`client_secret`) under Application Secrets section.\n",
+    "5. Follow the instructions at this [document](https://learn.microsoft.com/en-us/azure/active-directory/develop/quickstart-configure-app-expose-web-apis#add-a-scope) to add the following `SCOPES` (`Notes.Read`) to your application.\n",
+    "6. You need to install the msal and bs4 packages using the commands `pip install msal` and `pip install beautifulsoup4`.\n",
+    "7. At the end of the steps you must have the following values: \n",
+    "- `CLIENT_ID`\n",
+    "- `CLIENT_SECRET`\n",
+    "\n",
+    "## 🧑 Instructions for ingesting your documents from OneNote\n",
+    "\n",
+    "### 🔑 Authentication\n",
+    "\n",
+    "By default, the `OneNoteLoader` expects that the values of `CLIENT_ID` and `CLIENT_SECRET` must be stored as environment variables named `MS_GRAPH_CLIENT_ID` and `MS_GRAPH_CLIENT_SECRET` respectively. You could pass those environment variables through a `.env` file at the root of your application or using the following command in your script.\n",
+    "\n",
+    "```python\n",
+    "os.environ['MS_GRAPH_CLIENT_ID'] = \"YOUR CLIENT ID\"\n",
+    "os.environ['MS_GRAPH_CLIENT_SECRET'] = \"YOUR CLIENT SECRET\"\n",
+    "```\n",
+    "\n",
+    "This loader uses an authentication called [*on behalf of a user*](https://learn.microsoft.com/en-us/graph/auth-v2-user?context=graph%2Fapi%2F1.0&view=graph-rest-1.0). It is a 2 step authentication with user consent. When you instantiate the loader, it will call will print a url that the user must visit to give consent to the app on the required permissions. The user must then visit this url and give consent to the application. Then the user must copy the resulting page url and paste it back on the console. The method will then return True if the login attempt was successful.\n",
+    "\n",
+    "\n",
+    "```python\n",
+    "from langchain.document_loaders.onenote import OneNoteLoader\n",
+    "\n",
+    "loader = OneNoteLoader(notebook_name=\"NOTEBOOK NAME\", section_name=\"SECTION NAME\", page_title=\"PAGE TITLE\")\n",
+    "```\n",
+    "\n",
+    "Once the authentication has been done, the loader will store a token (`onenote_graph_token.txt`) at `~/.credentials/` folder. This token could be used later to authenticate without the copy/paste steps explained earlier. To use this token for authentication, you need to change the `auth_with_token` parameter to True in the instantiation of the loader.\n",
+    "\n",
+    "```python\n",
+    "from langchain.document_loaders.onenote import OneNoteLoader\n",
+    "\n",
+    "loader = OneNoteLoader(notebook_name=\"NOTEBOOK NAME\", section_name=\"SECTION NAME\", page_title=\"PAGE TITLE\", auth_with_token=True)\n",
+    "```\n",
+    "\n",
+    "Alternatively, you can also pass the token directly to the loader. This is useful when you want to authenticate with a token that was generated by another application. For instance, you can use the [Microsoft Graph Explorer](https://developer.microsoft.com/en-us/graph/graph-explorer) to generate a token and then pass it to the loader.\n",
+    "\n",
+    "```python\n",
+    "from langchain.document_loaders.onenote import OneNoteLoader\n",
+    "\n",
+    "loader = OneNoteLoader(notebook_name=\"NOTEBOOK NAME\", section_name=\"SECTION NAME\", page_title=\"PAGE TITLE\", access_token=\"TOKEN\")\n",
+    "```\n",
+    "\n",
+    "### 🗂️ Documents loader\n",
+    "\n",
+    "#### 📑 Loading pages from a OneNote Notebook\n",
+    "\n",
+    "`OneNoteLoader` can load pages from OneNote notebooks stored in OneDrive. You can specify any combination of `notebook_name`, `section_name`, `page_title` to filter for pages under a specific notebook, under a specific section, or with a specific title respectively. For instance, you want to load all pages that are stored under a section called `Recipes` within any of your notebooks OneDrive.\n",
+    "\n",
+    "\n",
+    "```python\n",
+    "from langchain.document_loaders.onenote import OneNoteLoader\n",
+    "\n",
+    "loader = OneNoteLoader(section_name=\"Recipes\", auth_with_token=True)\n",
+    "documents = loader.load()\n",
+    "```\n",
+    "\n",
+    "#### 📑 Loading pages from a list of Page IDs\n",
+    "\n",
+    "Another possibility is to provide a list of `object_ids` for each page you want to load. For that, you will need to query the [Microsoft Graph API](https://developer.microsoft.com/en-us/graph/graph-explorer) to find all the documents ID that you are interested in. This [link](https://learn.microsoft.com/en-us/graph/onenote-get-content#page-collection) provides a list of endpoints that will be helpful to retrieve the documents ID.\n",
+    "\n",
+    "For instance, to retrieve information about all pages that are stored in your notebooks, you need make a request to: `https://graph.microsoft.com/v1.0/me/onenote/pages`. Once you have the list of IDs that you are interested in, then you can instantiate the loader with the following parameters.\n",
+    "\n",
+    "\n",
+    "```python\n",
+    "from langchain.document_loaders.onenote import OneNoteLoader\n",
+    "\n",
+    "loader = OneNoteLoader(object_ids=[\"ID_1\", \"ID_2\"], auth_with_token=True)\n",
+    "documents = loader.load()\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb36fe41",
+   "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": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}