docs[patch]: google platform page update (#14475)

Added missed tools

---------

Co-authored-by: Erick Friis <erickfriis@gmail.com>

.github/CONTRIBUTING.md

@@ -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.

.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

@@ -1,21 +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/**"
-      - "libs/core/**"
-  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.
@@ -24,43 +27,39 @@ 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
     with:
-      working-directory: libs/langchain
+      working-directory: ${{ inputs.working-directory }}
     secrets: inherit
 
   test:
     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
     with:
-      working-directory: libs/langchain
+      working-directory: ${{ inputs.working-directory }}
     secrets: inherit
 
-  pydantic-compatibility:
-    uses: ./.github/workflows/_pydantic_compatibility.yml
+  dependencies:
+    uses: ./.github/workflows/_dependencies.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:
@@ -69,6 +68,9 @@ jobs:
           - "3.10"
           - "3.11"
     name: Python ${{ matrix.python-version }} extended tests
+    defaults:
+      run:
+        working-directory: ${{ inputs.working-directory }}
     steps:
       - uses: actions/checkout@v4
 
@@ -77,19 +79,14 @@ 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
         shell: bash
         run: |
           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
+          poetry install -E extended_testing --with test
 
       - name: Run extended tests
         run: make extended_tests

.github/workflows/_compile_integration_test.yml

@@ -38,7 +38,7 @@ jobs:
 
       - name: Install integration dependencies
         shell: bash
-        run: poetry install --with=test_integration
+        run: poetry install --with=test_integration,test
 
       - name: Check integration tests compile
         shell: bash

.github/workflows/_dependencies.yml

@@ -1,4 +1,4 @@
-name: pydantic v1/v2 compatibility
+name: dependencies
 
 on:
   workflow_call:
@@ -28,7 +28,7 @@ jobs:
           - "3.9"
           - "3.10"
           - "3.11"
-    name: Pydantic v1/v2 compatibility - Python ${{ matrix.python-version }}
+    name: dependencies - Python ${{ matrix.python-version }}
     steps:
       - uses: actions/checkout@v4
 
@@ -44,6 +44,14 @@ jobs:
         shell: bash
         run: poetry install
 
+      - name: Check imports with base dependencies
+        shell: bash
+        run: poetry run make check_imports
+
+      - name: Install test dependencies
+        shell: bash
+        run: poetry install --with test
+
       - name: Install langchain editable
         working-directory: ${{ inputs.working-directory }}
         if: ${{ inputs.langchain-location }}

.github/workflows/_lint.yml

@@ -90,4 +90,31 @@ jobs:
       - name: Analysing the code with our lint
         working-directory: ${{ inputs.working-directory }}
         run: |
-          make lint
+          make lint_package
+
+      - name: Install test dependencies
+        # Also installs dev/lint/test/typing dependencies, to ensure we have
+        # type hints for as many of our libraries as possible.
+        # This helps catch errors that require dependencies to be spotted, for example:
+        # https://github.com/langchain-ai/langchain/pull/10249/files#diff-935185cd488d015f026dcd9e19616ff62863e8cde8c0bee70318d3ccbca98341
+        #
+        # If you change this configuration, make sure to change the `cache-key`
+        # in the `poetry_setup` action above to stop using the old cache.
+        # It doesn't matter how you change it, any change will cause a cache-bust.
+        working-directory: ${{ inputs.working-directory }}
+        run: |
+          poetry install --with test
+
+      - name: Get .mypy_cache to speed up mypy
+        uses: actions/cache@v3
+        env:
+          SEGMENT_DOWNLOAD_TIMEOUT_MIN: "2"
+        with:
+          path: |
+            ${{ env.WORKDIR }}/.mypy_cache
+          key: mypy-test-${{ runner.os }}-${{ runner.arch }}-py${{ matrix.python-version }}-${{ inputs.working-directory }}-${{ hashFiles(format('{0}/poetry.lock', env.WORKDIR)) }}
+
+      - name: Analysing the code with our lint
+        working-directory: ${{ inputs.working-directory }}
+        run: |
+          make lint_tests

.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_community_release.yml

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

.github/workflows/langchain_core_ci.yml

@@ -1,52 +0,0 @@
----
-name: libs/langchain core 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_core_ci.yml'
-      - 'libs/core/**'
-  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/core"
-
-jobs:
-  lint:
-    uses:
-      ./.github/workflows/_lint.yml
-    with:
-      working-directory: libs/core
-    secrets: inherit
-
-  test:
-    uses:
-      ./.github/workflows/_test.yml
-    with:
-      working-directory: libs/core
-    secrets: inherit
-
-  pydantic-compatibility:
-    uses:
-      ./.github/workflows/_pydantic_compatibility.yml
-    with:
-      working-directory: libs/core
-    secrets: inherit

.github/workflows/langchain_experimental_ci.yml

@@ -1,136 +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/**"
-      - "libs/langchain/**"
-      - "libs/core/**"
-  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
-    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
-          poetry run pip install -e ../core
-
-      - 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'

.github/workflows/scheduled_test.yml

@@ -52,13 +52,7 @@ jobs:
         shell: bash
         run: |
           echo "Running scheduled tests, installing dependencies with poetry..."
-          poetry install --with=test_integration
-          poetry run pip install google-cloud-aiplatform
-          poetry run pip install "boto3>=1.28.57"
-          if [[ ${{ matrix.python-version }} != "3.8" ]]
-          then
-            poetry run pip install fireworks-ai
-          fi
+          poetry install --with=test_integration,test
 
       - name: Run tests
         shell: bash
@@ -68,7 +62,9 @@ jobs:
           AZURE_OPENAI_API_VERSION: ${{ secrets.AZURE_OPENAI_API_VERSION }}
           AZURE_OPENAI_API_BASE: ${{ secrets.AZURE_OPENAI_API_BASE }}
           AZURE_OPENAI_API_KEY: ${{ secrets.AZURE_OPENAI_API_KEY }}
-          AZURE_OPENAI_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_DEPLOYMENT_NAME }}
+          AZURE_OPENAI_CHAT_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_CHAT_DEPLOYMENT_NAME }}
+          AZURE_OPENAI_LLM_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_LLM_DEPLOYMENT_NAME }}
+          AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME: ${{ secrets.AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME }}
           FIREWORKS_API_KEY: ${{ secrets.FIREWORKS_API_KEY }}
         run: |
           make scheduled_tests

.github/workflows/templates_ci.yml

@@ -33,5 +33,4 @@ jobs:
       ./.github/workflows/_lint.yml
     with:
       working-directory: templates
-      langchain-location: ../libs/langchain
     secrets: inherit

.gitignore

@@ -167,8 +167,7 @@ docs/node_modules/
 docs/.docusaurus/
 docs/.cache-loader/
 docs/_dist
-docs/api_reference/api_reference.rst
-docs/api_reference/experimental_api_reference.rst
+docs/api_reference/*api_reference.rst
 docs/api_reference/_build
 docs/api_reference/*/
 !docs/api_reference/_static/

Makefile

@@ -41,7 +41,7 @@ spell_fix:
 # LINTING AND FORMATTING
 ######################
 
-lint:
+lint lint_package lint_tests:
 	poetry run ruff docs templates cookbook
 	poetry run ruff format docs templates cookbook --diff
 	poetry run ruff --select I docs templates cookbook

README.md

@@ -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/multi_modal_output_agent.ipynb

@@ -31,7 +31,7 @@
    "source": [
     "import re\n",
     "\n",
-    "from IPython.display import Image\n",
+    "from IPython.display import Image, display\n",
     "from steamship import Block, Steamship"
    ]
   },
@@ -180,7 +180,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

cookbook/qianfan_baidu_elasticesearch_RAG.ipynb

@@ -37,7 +37,8 @@
    "source": [
     "#!pip install qianfan\n",
     "#!pip install bce-python-sdk\n",
-    "#!pip install elasticsearch == 7.11.0"
+    "#!pip install elasticsearch == 7.11.0\n",
+    "#!pip install sentence-transformers"
    ]
   },
   {
@@ -54,8 +55,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import sentence_transformers\n",
     "from baidubce.auth.bce_credentials import BceCredentials\n",
     "from baidubce.bce_client_configuration import BceClientConfiguration\n",
+    "from langchain.chains.retrieval_qa import RetrievalQA\n",
     "from langchain.document_loaders.baiducloud_bos_directory import BaiduBOSDirectoryLoader\n",
     "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
     "from langchain.llms.baidu_qianfan_endpoint import QianfanLLMEndpoint\n",
@@ -161,15 +164,22 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "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",
-   "version": "3.9.17"
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
   },
-  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
@@ -177,5 +187,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

cookbook/retrieval_in_sql.ipynb

@@ -133,7 +133,7 @@
     "from tqdm import tqdm\n",
     "\n",
     "for i in tqdm(range(len(title_embeddings))):\n",
-    "    title = titles[i].replace(\"'\", \"''\")\n",
+    "    title = song_titles[i].replace(\"'\", \"''\")\n",
     "    embedding = title_embeddings[i]\n",
     "    sql_command = (\n",
     "        f'UPDATE \"Track\" SET \"embeddings\" = ARRAY{embedding} WHERE \"Name\" ='\n",
@@ -681,9 +681,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.18"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

cookbook/wikibase_agent.ipynb

@@ -187,7 +187,7 @@
     "    for key in path:\n",
     "        try:\n",
     "            current = current[key]\n",
-    "        except:\n",
+    "        except KeyError:\n",
     "            return None\n",
     "    return current\n",
     "\n",

docs/.local_build.sh

@@ -9,13 +9,15 @@ SCRIPT_DIR="$(cd "$(dirname "$0")"; pwd)"
 cd "${SCRIPT_DIR}"
 
 mkdir -p ../_dist
-rsync -ruv . ../_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 --pause 0
 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

@@ -196,11 +196,13 @@ def _load_package_modules(
     return modules_by_namespace
 
 
-def _construct_doc(pkg: str, members_by_namespace: Dict[str, ModuleMembers]) -> str:
+def _construct_doc(
+    package_namespace: str, members_by_namespace: Dict[str, ModuleMembers]
+) -> str:
     """Construct the contents of the reference.rst file for the given package.
 
     Args:
-        pkg: The package name
+        package_namespace: The package top level namespace
         members_by_namespace: The members of the package, dict organized by top level
                               module contains a list of classes and functions
                               inside of the top level namespace.
@@ -210,7 +212,7 @@ def _construct_doc(pkg: str, members_by_namespace: Dict[str, ModuleMembers]) ->
     """
     full_doc = f"""\
 =======================
-``{pkg}`` API Reference
+``{package_namespace}`` API Reference
 =======================
 
 """
@@ -222,13 +224,13 @@ def _construct_doc(pkg: str, members_by_namespace: Dict[str, ModuleMembers]) ->
         functions = _members["functions"]
         if not (classes or functions):
             continue
-        section = f":mod:`{pkg}.{module}`"
+        section = f":mod:`{package_namespace}.{module}`"
         underline = "=" * (len(section) + 1)
         full_doc += f"""\
 {section}
 {underline}
 
-.. automodule:: {pkg}.{module}
+.. automodule:: {package_namespace}.{module}
     :no-members:
     :no-inherited-members:
 
@@ -238,7 +240,7 @@ def _construct_doc(pkg: str, members_by_namespace: Dict[str, ModuleMembers]) ->
             full_doc += f"""\
 Classes
 --------------
-.. currentmodule:: {pkg}
+.. currentmodule:: {package_namespace}
 
 .. autosummary::
     :toctree: {module}
@@ -270,7 +272,7 @@ Classes
             full_doc += f"""\
 Functions
 --------------
-.. currentmodule:: {pkg}
+.. currentmodule:: {package_namespace}
 
 .. autosummary::
     :toctree: {module}
@@ -282,57 +284,57 @@ Functions
     return full_doc
 
 
-def _document_langchain_experimental() -> None:
-    """Document the langchain_experimental package."""
-    # Generate experimental_api_reference.rst
-    exp_members = _load_package_modules(EXP_DIR)
-    exp_doc = ".. _experimental_api_reference:\n\n" + _construct_doc(
-        "langchain_experimental", exp_members
-    )
-    with open(EXP_WRITE_FILE, "w") as f:
-        f.write(exp_doc)
+def _build_rst_file(package_name: str = "langchain") -> None:
+    """Create a rst file for building of documentation.
+
+    Args:
+        package_name: Can be either "langchain" or "core" or "experimental".
+    """
+    package_members = _load_package_modules(_package_dir(package_name))
+    with open(_out_file_path(package_name), "w") as f:
+        f.write(
+            _doc_first_line(package_name)
+            + _construct_doc(package_namespace[package_name], package_members)
+        )
 
 
-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)
+package_namespace = {
+    "langchain": "langchain",
+    "experimental": "langchain_experimental",
+    "core": "langchain_core",
+}
 
 
-def _document_langchain() -> None:
-    """Document the main langchain package."""
-    # load top level module members
-    lc_members = _load_package_modules(PKG_DIR)
+def _package_dir(package_name: str = "langchain") -> Path:
+    """Return the path to the directory containing the documentation."""
+    return ROOT_DIR / "libs" / package_name / package_namespace[package_name]
 
-    # Add additional packages
-    tools = _load_package_modules(PKG_DIR, "tools")
-    agents = _load_package_modules(PKG_DIR, "agents")
-    schema = _load_package_modules(PKG_DIR, "schema")
 
-    lc_members.update(
-        {
-            "agents.output_parsers": agents["output_parsers"],
-            "agents.format_scratchpad": agents["format_scratchpad"],
-            "tools.render": tools["render"],
-        }
-    )
+def _out_file_path(package_name: str = "langchain") -> Path:
+    """Return the path to the file containing the documentation."""
+    name_prefix = {
+        "langchain": "",
+        "experimental": "experimental_",
+        "core": "core_",
+    }
+    return HERE / f"{name_prefix[package_name]}api_reference.rst"
 
-    lc_doc = ".. _api_reference:\n\n" + _construct_doc("langchain", lc_members)
 
-    with open(WRITE_FILE, "w") as f:
-        f.write(lc_doc)
+def _doc_first_line(package_name: str = "langchain") -> str:
+    """Return the path to the file containing the documentation."""
+    prefix = {
+        "langchain": "",
+        "experimental": "experimental",
+        "core": "core",
+    }
+    return f".. {prefix[package_name]}_api_reference:\n\n"
 
 
 def main() -> None:
-    """Generate the reference.rst file for each package."""
-    _document_langchain()
-    _document_langchain_experimental()
-    _document_langchain_core()
+    """Generate the api_reference.rst file for each package."""
+    _build_rst_file(package_name="core")
+    _build_rst_file(package_name="langchain")
+    _build_rst_file(package_name="experimental")
 
 
 if __name__ == "__main__":

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": [],
@@ -226,39 +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, str]]) -> str:\n",
-    "    # chat history is of format:\n",
-    "    # [\n",
-    "    #   (human_message_str, ai_message_str),\n",
-    "    #   ...\n",
-    "    # ]\n",
-    "    # see below for an example of how it's invoked\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",
@@ -273,17 +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"
     }
@@ -299,17 +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"
     }
@@ -318,7 +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",
     ")"
    ]
@@ -335,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "id": "e31dd17c",
    "metadata": {},
    "outputs": [],
@@ -347,7 +327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "id": "d4bffe94",
    "metadata": {},
    "outputs": [],
@@ -359,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "id": "733be985",
    "metadata": {},
    "outputs": [],
@@ -373,7 +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",
@@ -400,18 +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"
     }
@@ -424,7 +404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "id": "977399fd",
    "metadata": {},
    "outputs": [],
@@ -437,18 +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"
     }
@@ -456,6 +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": {
@@ -474,7 +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": [
@@ -83,10 +88,10 @@
    ],
    "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",
+    "    except RateLimitError:\n",
     "        print(\"Hit error\")"
    ]
   },
@@ -106,10 +111,10 @@
    ],
    "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",
+    "    except RateLimitError:\n",
     "        print(\"Hit error\")"
    ]
   },
@@ -148,10 +153,10 @@
     "    ]\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",
+    "    except RateLimitError:\n",
     "        print(\"Hit error\")"
    ]
   },
@@ -185,10 +190,10 @@
     ")\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",
+    "    except RateLimitError:\n",
     "        print(\"Hit error\")"
    ]
   },
@@ -286,7 +291,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.10.12"
   }
  },
  "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 with 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

@@ -251,7 +251,10 @@
    "id": "da3d1feb-b4bb-4624-961c-7db2e1180df7",
    "metadata": {},
    "source": [
-    ":::tip [Langsmith trace](https://smith.langchain.com/public/863a003b-7ca8-4b24-be9e-d63ec13c106e/r)\n",
+    ":::tip\n",
+    "\n",
+    "[Langsmith trace](https://smith.langchain.com/public/863a003b-7ca8-4b24-be9e-d63ec13c106e/r)\n",
+    "\n",
     ":::"
    ]
   },
@@ -290,9 +293,9 @@
    ],
    "source": [
     "from langchain.schema.messages import HumanMessage\n",
-    "from langchain.schema.runnable import RunnableMap\n",
+    "from langchain.schema.runnable import RunnableParallel\n",
     "\n",
-    "chain = RunnableMap({\"output_message\": ChatAnthropic(model=\"claude-2\")})\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",
@@ -334,7 +337,10 @@
    "id": "b898d1b1-11e6-4d30-a8dd-cc5e45533611",
    "metadata": {},
    "source": [
-    ":::tip [LangSmith trace](https://smith.langchain.com/public/f6c3e1d1-a49d-4955-a9fa-c6519df74fa7/r)\n",
+    ":::tip\n",
+    "\n",
+    "[LangSmith trace](https://smith.langchain.com/public/f6c3e1d1-a49d-4955-a9fa-c6519df74fa7/r)\n",
+    "\n",
     ":::"
    ]
   },

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/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/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/evaluation/string/json.ipynb

@@ -5,13 +5,13 @@
    "id": "465cfbef-5bba-4b3b-b02d-fe2eba39db17",
    "metadata": {},
    "source": [
-    "# Evaluating Structured Output: JSON Evaluators\n",
+    "# JSON Evaluators\n",
     "\n",
-    "Evaluating [extraction](https://python.langchain.com/docs/use_cases/extraction) and function calling applications often comes down to validation that the LLM's string output can be parsed correctly and how it compares to a reference object. The following JSON validators provide provide functionality to check your model's output in a consistent way.\n",
+    "Evaluating [extraction](https://python.langchain.com/docs/use_cases/extraction) and function calling applications often comes down to validation that the LLM's string output can be parsed correctly and how it compares to a reference object. The following `JSON` validators provide functionality to check your model's output consistently.\n",
     "\n",
     "## JsonValidityEvaluator\n",
     "\n",
-    "The `JsonValidityEvaluator` is designed to check the validity of a JSON string prediction.\n",
+    "The `JsonValidityEvaluator` is designed to check the validity of a `JSON` string prediction.\n",
     "\n",
     "### Overview:\n",
     "- **Requires Input?**: No\n",
@@ -377,7 +377,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/guides/evaluation/string/string_distance.ipynb

@@ -8,9 +8,12 @@
     "# String Distance\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/evaluation/string/string_distance.ipynb)\n",
     "\n",
-    "One of the simplest ways to compare an LLM or chain's string output against a reference label is by using string distance measurements such as Levenshtein or postfix distance.  This can be used alongside approximate/fuzzy matching criteria for very basic unit testing.\n",
+    ">In information theory, linguistics, and computer science, the [Levenshtein distance (Wikipedia)](https://en.wikipedia.org/wiki/Levenshtein_distance) is a string metric for measuring the difference between two sequences. Informally, the Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other. It is named after the Soviet mathematician Vladimir Levenshtein, who considered this distance in 1965.\n",
     "\n",
-    "This can be accessed using the `string_distance` evaluator, which uses distance metric's from the [rapidfuzz](https://github.com/maxbachmann/RapidFuzz) library.\n",
+    "\n",
+    "One of the simplest ways to compare an LLM or chain's string output against a reference label is by using string distance measurements such as `Levenshtein` or `postfix` distance.  This can be used alongside approximate/fuzzy matching criteria for very basic unit testing.\n",
+    "\n",
+    "This can be accessed using the `string_distance` evaluator, which uses distance metrics from the [rapidfuzz](https://github.com/maxbachmann/RapidFuzz) library.\n",
     "\n",
     "**Note:** The returned scores are _distances_, meaning lower is typically \"better\".\n",
     "\n",
@@ -213,9 +216,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.2"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}

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": [
@@ -85,10 +90,10 @@
    ],
    "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",
+    "    except RateLimitError:\n",
     "        print(\"Hit error\")"
    ]
   },
@@ -108,10 +113,10 @@
    ],
    "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",
+    "    except RateLimitError:\n",
     "        print(\"Hit error\")"
    ]
   },
@@ -150,10 +155,10 @@
     "    ]\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",
+    "    except RateLimitError:\n",
     "        print(\"Hit error\")"
    ]
   },
@@ -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",
@@ -284,6 +284,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain.callbacks.manager import CallbackManager\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
     "from langchain.llms import LlamaCpp\n",
     "\n",
     "llm = LlamaCpp(\n",

docs/docs/guides/privacy/presidio_data_anonymization/index.ipynb

@@ -8,6 +8,8 @@
     "\n",
     "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/langchain-ai/langchain/blob/master/docs/docs/guides/privacy/presidio_data_anonymization/index.ipynb)\n",
     "\n",
+    ">[Presidio](https://microsoft.github.io/presidio/) (Origin from Latin praesidium ‘protection, garrison’) helps to ensure sensitive data is properly managed and governed. It provides fast identification and anonymization modules for private entities in text and images such as credit card numbers, names, locations, social security numbers, bitcoin wallets, US phone numbers, financial data and more.\n",
+    "\n",
     "## Use case\n",
     "\n",
     "Data anonymization is crucial before passing information to a language model like GPT-4 because it helps protect privacy and maintain confidentiality. If data is not anonymized, sensitive information such as names, addresses, contact numbers, or other identifiers linked to specific individuals could potentially be learned and misused. Hence, by obscuring or removing this personally identifiable information (PII), data can be used freely without compromising individuals' privacy rights or breaching data protection laws and regulations.\n",
@@ -530,7 +532,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.4"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

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/integrations/adapters/openai-old.ipynb

@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "700a516b",
+   "metadata": {},
+   "source": [
+    "# 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",
+    "At the moment this only deals with output and does not return other information (token counts, stop reasons, etc)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "6017f26a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import openai\n",
+    "from langchain.adapters import openai as lc_openai"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b522ceda",
+   "metadata": {},
+   "source": [
+    "## ChatCompletion.create"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "1d22eb61",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "messages = [{\"role\": \"user\", \"content\": \"hi\"}]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d550d3ad",
+   "metadata": {},
+   "source": [
+    "Original OpenAI call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "012d81ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result = openai.ChatCompletion.create(\n",
+    "    messages=messages, model=\"gpt-3.5-turbo\", temperature=0\n",
+    ")\n",
+    "result[\"choices\"][0][\"message\"].to_dict_recursive()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db5b5500",
+   "metadata": {},
+   "source": [
+    "LangChain OpenAI wrapper call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c67a5ac8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lc_result = lc_openai.ChatCompletion.create(\n",
+    "    messages=messages, model=\"gpt-3.5-turbo\", temperature=0\n",
+    ")\n",
+    "lc_result[\"choices\"][0][\"message\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "034ba845",
+   "metadata": {},
+   "source": [
+    "Swapping out model providers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "f7c94827",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'role': 'assistant', 'content': ' Hello!'}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lc_result = lc_openai.ChatCompletion.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": [
+    "## ChatCompletion.stream"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7b8cd18",
+   "metadata": {},
+   "source": [
+    "Original OpenAI call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "fd8cb1ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'role': 'assistant', 'content': ''}\n",
+      "{'content': 'Hello'}\n",
+      "{'content': '!'}\n",
+      "{'content': ' How'}\n",
+      "{'content': ' can'}\n",
+      "{'content': ' I'}\n",
+      "{'content': ' assist'}\n",
+      "{'content': ' you'}\n",
+      "{'content': ' today'}\n",
+      "{'content': '?'}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in openai.ChatCompletion.create(\n",
+    "    messages=messages, model=\"gpt-3.5-turbo\", temperature=0, stream=True\n",
+    "):\n",
+    "    print(c[\"choices\"][0][\"delta\"].to_dict_recursive())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b2a076b",
+   "metadata": {},
+   "source": [
+    "LangChain OpenAI wrapper call"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "9521218c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'role': 'assistant', 'content': ''}\n",
+      "{'content': 'Hello'}\n",
+      "{'content': '!'}\n",
+      "{'content': ' How'}\n",
+      "{'content': ' can'}\n",
+      "{'content': ' I'}\n",
+      "{'content': ' assist'}\n",
+      "{'content': ' you'}\n",
+      "{'content': ' today'}\n",
+      "{'content': '?'}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in lc_openai.ChatCompletion.create(\n",
+    "    messages=messages, 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": 31,
+   "id": "68f0214e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'role': 'assistant', 'content': ' Hello'}\n",
+      "{'content': '!'}\n",
+      "{}\n"
+     ]
+    }
+   ],
+   "source": [
+    "for c in lc_openai.ChatCompletion.create(\n",
+    "    messages=messages,\n",
+    "    model=\"claude-2\",\n",
+    "    temperature=0,\n",
+    "    stream=True,\n",
+    "    provider=\"ChatAnthropic\",\n",
+    "):\n",
+    "    print(c[\"choices\"][0][\"delta\"])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/docs/integrations/adapters/openai.ipynb

@@ -7,6 +7,8 @@
    "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)."
@@ -28,12 +30,12 @@
    "id": "b522ceda",
    "metadata": {},
    "source": [
-    "## ChatCompletion.create"
+    "## chat.completions.create"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 2,
    "id": "1d22eb61",
    "metadata": {},
    "outputs": [],
@@ -51,26 +53,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "id": "012d81ae",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
+       "{'content': 'Hello! How can I assist you today?',\n",
+       " 'role': 'assistant',\n",
+       " 'function_call': None,\n",
+       " 'tool_calls': None}"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "result = openai.ChatCompletion.create(\n",
+    "result = openai.chat.completions.create(\n",
     "    messages=messages, model=\"gpt-3.5-turbo\", temperature=0\n",
     ")\n",
-    "result[\"choices\"][0][\"message\"].to_dict_recursive()"
+    "result.choices[0].message.model_dump()"
    ]
   },
   {
@@ -83,26 +88,48 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 4,
    "id": "c67a5ac8",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
+       "{'role': 'assistant', 'content': 'Hello! How can I help you today?'}"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "lc_result = lc_openai.ChatCompletion.create(\n",
+    "lc_result = lc_openai.chat.completions.create(\n",
     "    messages=messages, model=\"gpt-3.5-turbo\", temperature=0\n",
     ")\n",
-    "lc_result[\"choices\"][0][\"message\"]"
+    "\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"
    ]
   },
   {
@@ -115,26 +142,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 6,
    "id": "f7c94827",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'role': 'assistant', 'content': ' Hello!'}"
+       "{'role': 'assistant', 'content': 'Hello! How can I assist you today?'}"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "lc_result = lc_openai.ChatCompletion.create(\n",
+    "lc_result = lc_openai.chat.completions.create(\n",
     "    messages=messages, model=\"claude-2\", temperature=0, provider=\"ChatAnthropic\"\n",
     ")\n",
-    "lc_result[\"choices\"][0][\"message\"]"
+    "lc_result.choices[0].message"
    ]
   },
   {
@@ -142,7 +169,7 @@
    "id": "cb3f181d",
    "metadata": {},
    "source": [
-    "## ChatCompletion.stream"
+    "## chat.completions.stream"
    ]
   },
   {
@@ -155,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 7,
    "id": "fd8cb1ea",
    "metadata": {},
    "outputs": [
@@ -163,25 +190,25 @@
      "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"
+      "{'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.ChatCompletion.create(\n",
+    "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\"].to_dict_recursive())"
+    "    print(c.choices[0].delta.model_dump())"
    ]
   },
   {
@@ -194,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 8,
    "id": "9521218c",
    "metadata": {},
    "outputs": [
@@ -217,10 +244,10 @@
     }
    ],
    "source": [
-    "for c in lc_openai.ChatCompletion.create(\n",
+    "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\"])"
+    "    print(c.choices[0].delta)"
    ]
   },
   {
@@ -233,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 9,
    "id": "68f0214e",
    "metadata": {},
    "outputs": [
@@ -241,14 +268,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'role': 'assistant', 'content': ' Hello'}\n",
+      "{'role': 'assistant', 'content': ''}\n",
+      "{'content': 'Hello'}\n",
       "{'content': '!'}\n",
+      "{'content': ' How'}\n",
+      "{'content': ' can'}\n",
+      "{'content': ' I'}\n",
+      "{'content': ' assist'}\n",
+      "{'content': ' you'}\n",
+      "{'content': ' today'}\n",
+      "{'content': '?'}\n",
       "{}\n"
      ]
     }
    ],
    "source": [
-    "for c in lc_openai.ChatCompletion.create(\n",
+    "for c in lc_openai.chat.completions.create(\n",
     "    messages=messages,\n",
     "    model=\"claude-2\",\n",
     "    temperature=0,\n",
@@ -275,7 +310,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

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

@@ -7,13 +7,13 @@
    "source": [
     "# PromptLayer\n",
     "\n",
-    "![PromptLayer](https://promptlayer.com/text_logo.png)\n",
+    ">[PromptLayer](https://docs.promptlayer.com/introduction) is a platform for prompt engineering. It also helps with the LLM observability to visualize requests, version prompts, and track usage.\n",
+    ">\n",
+    ">While `PromptLayer` does have LLMs that integrate directly with LangChain (e.g. [`PromptLayerOpenAI`](https://python.langchain.com/docs/integrations/llms/promptlayer_openai)), using a callback is the recommended way to integrate `PromptLayer` with LangChain.\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",
+    "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",
-    "\n",
-    "See [our docs](https://docs.promptlayer.com/languages/langchain) for more information."
+    "See [PromptLayer docs](https://docs.promptlayer.com/languages/langchain) for more information."
    ]
   },
   {
@@ -51,7 +51,7 @@
    "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 +63,7 @@
    "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 +99,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### GPT4All Example"
+    "## GPT4All Example"
    ]
   },
   {
@@ -125,9 +125,9 @@
    "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 +182,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -196,7 +196,7 @@
    "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

@@ -1,11 +1,21 @@
 {
  "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",
     ">[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",

docs/docs/integrations/chat/azureml_chat_endpoint.ipynb

@@ -1,10 +1,19 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Azure ML Endpoint\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Azure ML Endpoint\n",
+    "# AzureMLChatOnlineEndpoint\n",
     "\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",

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",
@@ -25,18 +34,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "tags": []
    },
    "outputs": [],
    "source": [
-    "#!pip install langchain google-cloud-aiplatform"
+    "!pip install -U google-cloud-aiplatform"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,43 +55,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "chat = ChatVertexAI()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = \"You are a helpful assistant who translate English to French\"\n",
-    "human = \"Translate this sentence from English to French. I love programming.\"\n",
-    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
-    "messages = prompt.format_messages()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False)"
+       "AIMessage(content=\" J'aime la programmation.\")"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "chat(messages)"
+    "system = \"You are a helpful assistant who translate English to French\"\n",
+    "human = \"Translate this sentence from English to French. I love programming.\"\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
+    "\n",
+    "chat = ChatVertexAI()\n",
+    "\n",
+    "chain = prompt | chat\n",
+    "chain.invoke({})"
    ]
   },
   {
@@ -94,35 +89,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "system = (\n",
-    "    \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
-    ")\n",
-    "human = \"{text}\"\n",
-    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=' 私はプログラミングが大好きです。', additional_kwargs={}, example=False)"
+       "AIMessage(content=' プログラミングが大好きです')"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "system = (\n",
+    "    \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
+    ")\n",
+    "human = \"{text}\"\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
+    "\n",
     "chain = prompt | chat\n",
+    "\n",
     "chain.invoke(\n",
     "    {\n",
     "        \"input_language\": \"English\",\n",
@@ -153,20 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "chat = ChatVertexAI(\n",
-    "    model_name=\"codechat-bison\", max_output_tokens=1000, temperature=0.5\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 4,
    "metadata": {
     "tags": []
    },
@@ -176,20 +152,39 @@
      "output_type": "stream",
      "text": [
       " ```python\n",
-      "def is_prime(x): \n",
-      "    if (x <= 1): \n",
+      "def is_prime(n):\n",
+      "    if n <= 1:\n",
       "        return False\n",
-      "    for i in range(2, x): \n",
-      "        if (x % i == 0): \n",
+      "    for i in range(2, n):\n",
+      "        if n % i == 0:\n",
       "            return False\n",
       "    return True\n",
+      "\n",
+      "def find_prime_numbers(n):\n",
+      "    prime_numbers = []\n",
+      "    for i in range(2, n + 1):\n",
+      "        if is_prime(i):\n",
+      "            prime_numbers.append(i)\n",
+      "    return prime_numbers\n",
+      "\n",
+      "print(find_prime_numbers(100))\n",
+      "```\n",
+      "\n",
+      "Output:\n",
+      "\n",
+      "```\n",
+      "[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83, 89, 97]\n",
       "```\n"
      ]
     }
    ],
    "source": [
-    "# For simple string in string out usage, we can use the `predict` method:\n",
-    "print(chat.predict(\"Write a Python function to identify all prime numbers\"))"
+    "chat = ChatVertexAI(\n",
+    "    model_name=\"codechat-bison\", max_output_tokens=1000, temperature=0.5\n",
+    ")\n",
+    "\n",
+    "message = chat.invoke(\"Write a Python function to identify all prime numbers\")\n",
+    "print(message.content)"
    ]
   },
   {
@@ -198,66 +193,47 @@
    "source": [
     "## Asynchronous calls\n",
     "\n",
-    "We can make asynchronous calls via the `agenerate` and `ainvoke` methods."
+    "We can make asynchronous calls via the Runnables [Async Interface](/docs/expression_language/interface)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# for running these examples in the notebook:\n",
     "import asyncio\n",
     "\n",
-    "# import nest_asyncio\n",
-    "# nest_asyncio.apply()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "LLMResult(generations=[[ChatGeneration(text=\" J'aime la programmation.\", generation_info=None, message=AIMessage(content=\" J'aime la programmation.\", additional_kwargs={}, example=False))]], llm_output={}, run=[RunInfo(run_id=UUID('223599ef-38f8-4c79-ac6d-a5013060eb9d'))])"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "chat = ChatVertexAI(\n",
-    "    model_name=\"chat-bison\",\n",
-    "    max_output_tokens=1000,\n",
-    "    temperature=0.7,\n",
-    "    top_p=0.95,\n",
-    "    top_k=40,\n",
-    ")\n",
+    "import nest_asyncio\n",
     "\n",
-    "asyncio.run(chat.agenerate([messages]))"
+    "nest_asyncio.apply()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "AIMessage(content=' अहं प्रोग्रामिंग प्रेमामि', additional_kwargs={}, example=False)"
+       "AIMessage(content=' Why do you love programming?')"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "system = (\n",
+    "    \"You are a helpful assistant that translates {input_language} to {output_language}.\"\n",
+    ")\n",
+    "human = \"{text}\"\n",
+    "prompt = ChatPromptTemplate.from_messages([(\"system\", system), (\"human\", human)])\n",
+    "chain = prompt | chat\n",
+    "\n",
     "asyncio.run(\n",
     "    chain.ainvoke(\n",
     "        {\n",
@@ -280,56 +256,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      " 1. China (1,444,216,107)\n",
-      "2. India (1,393,409,038)\n",
-      "3. United States (332,403,650)\n",
-      "4. Indonesia (273,523,615)\n",
-      "5. Pakistan (220,892,340)\n",
-      "6. Brazil (212,559,409)\n",
-      "7. Nigeria (206,139,589)\n",
-      "8. Bangladesh (164,689,383)\n",
-      "9. Russia (145,934,462)\n",
-      "10. Mexico (128,932,488)\n",
-      "11. Japan (126,476,461)\n",
-      "12. Ethiopia (115,063,982)\n",
-      "13. Philippines (109,581,078)\n",
-      "14. Egypt (102,334,404)\n",
-      "15. Vietnam (97,338,589)"
+      " The five most populous countries in the world are:\n",
+      "1. China (1.4 billion)\n",
+      "2. India (1.3 billion)\n",
+      "3. United States (331 million)\n",
+      "4. Indonesia (273 million)\n",
+      "5. Pakistan (220 million)"
      ]
     }
    ],
    "source": [
+    "import sys\n",
+    "\n",
     "prompt = ChatPromptTemplate.from_messages(\n",
-    "    [(\"human\", \"List out the 15 most populous countries in the world\")]\n",
+    "    [(\"human\", \"List out the 5 most populous countries in the world\")]\n",
     ")\n",
-    "messages = prompt.format_messages()\n",
-    "for chunk in chat.stream(messages):\n",
+    "\n",
+    "chat = ChatVertexAI()\n",
+    "\n",
+    "chain = prompt | chat\n",
+    "\n",
+    "for chunk in chain.stream({}):\n",
     "    sys.stdout.write(chunk.content)\n",
     "    sys.stdout.flush()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "poetry-venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "poetry-venv"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -341,7 +312,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.1"
+   "version": "3.11.4"
   },
   "vscode": {
    "interpreter": {

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)"
    ]
@@ -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

@@ -1,11 +1,21 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "7320f16b",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Llama 2 Chat\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "90a1faf2",
    "metadata": {},
    "source": [
-    "# Llama-2 Chat\n",
+    "# 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",
@@ -721,7 +731,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.11.4"
   }
  },
  "nbformat": 4,

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",

docs/docs/integrations/chat/ollama_functions.ipynb

@@ -1,10 +1,19 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Ollama Functions\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Ollama Functions\n",
+    "# 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",

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."
    ]
@@ -119,12 +129,6 @@
     "**The above request should now appear on your [PromptLayer dashboard](https://www.promptlayer.com).**"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "05e9e2fe",
-   "metadata": {},
-   "source": []
-  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -142,6 +146,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import promptlayer\n",
+    "\n",
     "chat = PromptLayerChatOpenAI(return_pl_id=True)\n",
     "chat_results = chat.generate([[HumanMessage(content=\"I am a cat and I want\")]])\n",
     "\n",
@@ -162,7 +168,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -176,7 +182,7 @@
    "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/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

@@ -1,5 +1,15 @@
 {
  "cells": [
+  {
+   "cell_type": "raw",
+   "id": "66107bdd",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "sidebar_label: Volc Enging Maas\n",
+    "---"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "404758628c7b20f6",
@@ -7,7 +17,7 @@
     "collapsed": false
    },
    "source": [
-    "# Volc Engine Maas\n",
+    "# VolcEngineMaasChat\n",
     "\n",
     "This notebook provides you with a guide on how to get started with volc engine maas chat models."
    ]
@@ -86,7 +96,9 @@
    "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！')"
+      "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": {},
@@ -141,7 +153,9 @@
    "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”。')"
+      "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": {},

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/imessage.ipynb

@@ -153,7 +153,7 @@
    "source": [
     "# Now all of the Tortoise's messages will take the AI message class\n",
     "# which maps to the 'assistant' role in OpenAI's training format\n",
-    "alternating_sessions[0][\"messages\"][:3]"
+    "chat_sessions[0][\"messages\"][:3]"
    ]
   },
   {
@@ -191,7 +191,7 @@
     }
    ],
    "source": [
-    "training_data = convert_messages_for_finetuning(alternating_sessions)\n",
+    "training_data = convert_messages_for_finetuning(chat_sessions)\n",
     "print(f\"Prepared {len(training_data)} dialogues for training\")"
    ]
   },
@@ -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": "3.10.12"
   }
  },
  "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/arcgis.ipynb

@@ -23,8 +23,18 @@
    "source": [
     "from langchain.document_loaders import ArcGISLoader\n",
     "\n",
-    "url = \"https://maps1.vcgov.org/arcgis/rest/services/Beaches/MapServer/7\"\n",
-    "loader = ArcGISLoader(url)"
+    "URL = \"https://maps1.vcgov.org/arcgis/rest/services/Beaches/MapServer/7\"\n",
+    "loader = ArcGISLoader(URL)\n",
+    "\n",
+    "docs = loader.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e174ebd-bbbd-4a66-a644-51e0df12982d",
+   "metadata": {},
+   "source": [
+    "Let's measure loader latency."
    ]
   },
   {
@@ -261,7 +271,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "loader_geom = ArcGISLoader(url, return_geometry=True)"
+    "loader_geom = ArcGISLoader(URL, return_geometry=True)"
    ]
   },
   {

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/datadog_logs.ipynb

@@ -30,6 +30,16 @@
     "#!pip install datadog-api-client"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DD_API_KEY = \"...\"\n",
+    "DD_APP_KEY = \"...\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -73,7 +83,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -87,10 +97,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.11"
-  },
-  "orig_nbformat": 4
+   "version": "3.10.12"
+  }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

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/etherscan.ipynb

@@ -65,6 +65,16 @@
     "%pip install langchain -q"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ab73cc1-d8e0-4b6d-bb03-9522b112fce5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "etherscanAPIKey = \"...\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,

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/larksuite.ipynb

@@ -74,7 +74,9 @@
    "source": [
     "# see https://python.langchain.com/docs/use_cases/summarization for more details\n",
     "from langchain.chains.summarize import load_summarize_chain\n",
+    "from langchain.llms.fake import FakeListLLM\n",
     "\n",
+    "llm = FakeListLLM()\n",
     "chain = load_summarize_chain(llm, chain_type=\"map_reduce\")\n",
     "chain.run(docs)"
    ]
@@ -96,7 +98,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.3"
+   "version": "3.10.12"
   }
  },
  "nbformat": 4,

docs/docs/integrations/document_loaders/tensorflow_datasets.ipynb

@@ -166,6 +166,9 @@
     }
    ],
    "source": [
+    "from langchain_core.documents import Document\n",
+    "\n",
+    "\n",
     "def decode_to_str(item: tf.Tensor) -> str:\n",
     "    return item.numpy().decode(\"utf-8\")\n",
     "\n",