diff --git a/.github/workflows/integration-test-library-mode.yml b/.github/workflows/integration-test-library-mode.yml
new file mode 100644
index 000000000..b6f2e13e9
--- /dev/null
+++ b/.github/workflows/integration-test-library-mode.yml
@@ -0,0 +1,80 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024-25, NVIDIA CORPORATION & AFFILIATES.
+# All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+name: Library Mode Integration Tests (Windows & macOS)
+
+on:
+  schedule:
+    # Runs every day at 11:30PM (UTC)
+    - cron: "30 23 * * *"
+  workflow_dispatch:
+    inputs:
+      source-ref:
+        description: 'Git ref to test (branch, tag, or SHA). Defaults to the dispatched branch.'
+        required: false
+        type: string
+        default: ''
+
+jobs:
+  integration-test:
+    name: Integration Tests (${{ matrix.os-label }})
+    runs-on: ${{ matrix.runner }}
+    timeout-minutes: 90
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+          - runner: windows-latest
+            os-label: windows-x64
+          - runner: macos-26
+            os-label: macos-arm64
+          - runner: macos-26-intel
+            os-label: macos-x64
+
+    env:
+      # NIM endpoint URLs — edit these directly to point at different deployments
+      PAGE_ELEMENTS_INVOKE_URL: "https://ai.api.nvidia.com/v1/cv/nvidia/nemotron-page-elements-v3"
+      OCR_INVOKE_URL: "https://ai.api.nvidia.com/v1/cv/nvidia/nemoretriever-ocr-v1"
+      GRAPHIC_ELEMENTS_INVOKE_URL: "https://ai.api.nvidia.com/v1/cv/nvidia/nemotron-graphic-elements-v1"
+      TABLE_STRUCTURE_INVOKE_URL: "https://ai.api.nvidia.com/v1/cv/nvidia/nemotron-table-structure-v1"
+      EMBED_INVOKE_URL: "https://integrate.api.nvidia.com/v1"
+      EMBED_MODEL_NAME: "nvidia/llama-nemotron-embed-1b-v2"
+
+    steps:
+      - name: Check out repository code
+        uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.source-ref != '' && inputs.source-ref || github.ref }}
+
+      - name: Set up Python 3.12
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.12'
+
+      - name: Install uv
+        run: pip install uv
+
+      - name: Install nemo-retriever and dependencies
+        shell: bash
+        run: |
+          uv pip install --system -e "nemo_retriever"
+
+      - name: Run graph pipeline on PDFs
+        shell: bash
+        env:
+          PYTHONPATH: nemo_retriever/src
+        run: |
+          python -m nemo_retriever.examples.graph_pipeline ./data \
+            --run-mode inprocess \
+            --input-type pdf \
+            --api-key "${{ secrets.NVCF_API_KEY }}" \
+            --page-elements-invoke-url "$PAGE_ELEMENTS_INVOKE_URL" \
+            --ocr-invoke-url "$OCR_INVOKE_URL" \
+            --use-graphic-elements \
+            --graphic-elements-invoke-url "$GRAPHIC_ELEMENTS_INVOKE_URL" \
+            --use-table-structure \
+            --table-structure-invoke-url "$TABLE_STRUCTURE_INVOKE_URL" \
+            --embed-invoke-url "$EMBED_INVOKE_URL" \
+            --embed-model-name "$EMBED_MODEL_NAME"
diff --git a/.github/workflows/retriever-unit-tests.yml b/.github/workflows/retriever-unit-tests.yml
index 87fb7ee25..76ee1f763 100644
--- a/.github/workflows/retriever-unit-tests.yml
+++ b/.github/workflows/retriever-unit-tests.yml
@@ -26,8 +26,7 @@ jobs:
 
       - name: Install unit test dependencies
         run: |
-          uv pip install --system -e src/ -e api/ -e client/
-          uv pip install --system -e nemo_retriever
+          uv pip install --system -e nemo_retriever[all,dev]
 
       - name: Run retriever unit tests
         env:
diff --git a/api/src/nv_ingest_api/internal/extract/pdf/engines/unstructured_io.py b/api/src/nv_ingest_api/internal/extract/pdf/engines/unstructured_io.py
index fb9364245..138c7788c 100644
--- a/api/src/nv_ingest_api/internal/extract/pdf/engines/unstructured_io.py
+++ b/api/src/nv_ingest_api/internal/extract/pdf/engines/unstructured_io.py
@@ -25,11 +25,19 @@
 
 import pandas as pd
 import pypdfium2 as pdfium
-from unstructured_client import UnstructuredClient
-from unstructured_client.models import operations
-from unstructured_client.models import shared
-from unstructured_client.utils import BackoffStrategy
-from unstructured_client.utils import RetryConfig
+
+try:
+    from unstructured_client import UnstructuredClient
+    from unstructured_client.models import operations
+    from unstructured_client.models import shared
+    from unstructured_client.utils import BackoffStrategy
+    from unstructured_client.utils import RetryConfig
+except ImportError:
+    UnstructuredClient = None
+    operations = None
+    shared = None
+    BackoffStrategy = None
+    RetryConfig = None
 
 from nv_ingest_api.internal.enums.common import AccessLevelEnum, DocumentTypeEnum
 from nv_ingest_api.internal.enums.common import ContentTypeEnum
diff --git a/api/src/nv_ingest_api/internal/primitives/nim/model_interface/helpers.py b/api/src/nv_ingest_api/internal/primitives/nim/model_interface/helpers.py
index 303506688..b7bc5149d 100644
--- a/api/src/nv_ingest_api/internal/primitives/nim/model_interface/helpers.py
+++ b/api/src/nv_ingest_api/internal/primitives/nim/model_interface/helpers.py
@@ -6,7 +6,6 @@
 from typing import Optional
 
 import backoff
-import cv2
 import numpy as np
 import requests
 
@@ -14,7 +13,12 @@
 from nv_ingest_api.util.image_processing.transforms import pad_image, normalize_image
 from nv_ingest_api.util.string_processing import generate_url, remove_url_endpoints
 
-cv2.setNumThreads(1)
+try:
+    import cv2
+
+    cv2.setNumThreads(1)
+except ImportError:
+    cv2 = None
 logger = logging.getLogger(__name__)
 
 
diff --git a/api/src/nv_ingest_api/util/detectors/language.py b/api/src/nv_ingest_api/util/detectors/language.py
index a4b0c48e5..c27edb6f5 100644
--- a/api/src/nv_ingest_api/util/detectors/language.py
+++ b/api/src/nv_ingest_api/util/detectors/language.py
@@ -3,8 +3,6 @@
 # SPDX-License-Identifier: Apache-2.0
 
 
-import langdetect
-
 from nv_ingest_api.internal.enums.common import LanguageEnum
 from nv_ingest_api.util.exception_handlers.detectors import langdetect_exception_handler
 
@@ -24,6 +22,10 @@ def detect_language(text):
     LanguageEnum
         A value from `LanguageEnum` detected language code.
     """
+    try:
+        import langdetect
+    except ImportError:
+        return LanguageEnum.UNKNOWN
 
     try:
         language = langdetect.detect(text)
diff --git a/api/src/nv_ingest_api/util/exception_handlers/detectors.py b/api/src/nv_ingest_api/util/exception_handlers/detectors.py
index 39a7dfb30..6b44869da 100644
--- a/api/src/nv_ingest_api/util/exception_handlers/detectors.py
+++ b/api/src/nv_ingest_api/util/exception_handlers/detectors.py
@@ -8,7 +8,10 @@
 from typing import Callable
 from typing import Dict
 
-from langdetect.lang_detect_exception import LangDetectException
+try:
+    from langdetect.lang_detect_exception import LangDetectException as _LangDetectException
+except ImportError:
+    _LangDetectException = None
 
 from nv_ingest_api.internal.enums.common import LanguageEnum
 
@@ -66,9 +69,10 @@ def langdetect_exception_handler(func: Callable, **kwargs: Dict[str, Any]) -> Ca
     def inner_function(*args, **kwargs):
         try:
             return func(*args, **kwargs)
-        except LangDetectException as e:
-            log_error_message = f"LangDetectException: {e}"
-            logger.warning(log_error_message)
-            return LanguageEnum.UNKNOWN
+        except Exception as e:
+            if _LangDetectException is not None and isinstance(e, _LangDetectException):
+                logger.warning(f"LangDetectException: {e}")
+                return LanguageEnum.UNKNOWN
+            raise
 
     return inner_function
diff --git a/api/src/nv_ingest_api/util/image_processing/table_and_chart.py b/api/src/nv_ingest_api/util/image_processing/table_and_chart.py
index 429cfc3d7..8fe1c688a 100644
--- a/api/src/nv_ingest_api/util/image_processing/table_and_chart.py
+++ b/api/src/nv_ingest_api/util/image_processing/table_and_chart.py
@@ -8,7 +8,6 @@
 
 import numpy as np
 import pandas as pd
-from sklearn.cluster import DBSCAN
 
 
 logger = logging.getLogger(__name__)
@@ -173,10 +172,14 @@ def convert_ocr_response_to_psuedo_markdown(bboxes, texts):
     )
     preds_df = preds_df.sort_values("y0")
 
-    dbscan = DBSCAN(eps=10, min_samples=1)
-    dbscan.fit(preds_df["y0"].values[:, None])
+    try:
+        from sklearn.cluster import DBSCAN
 
-    preds_df["cluster"] = dbscan.labels_
+        dbscan = DBSCAN(eps=10, min_samples=1)
+        dbscan.fit(preds_df["y0"].values[:, None])
+        preds_df["cluster"] = dbscan.labels_
+    except ImportError:
+        preds_df["cluster"] = (preds_df["y0"] / 10).round().astype(int)
     preds_df = preds_df.sort_values(["cluster", "x0"])
 
     results = ""
@@ -483,12 +486,14 @@ def reorder_boxes(boxes, texts, confs, mode="top_left", dbscan_eps=10):
     if dbscan_eps:
         do_naive_sorting = False
         try:
+            from sklearn.cluster import DBSCAN
+
             dbscan = DBSCAN(eps=dbscan_eps, min_samples=1)
             dbscan.fit(df["y"].values[:, None])
             df["cluster"] = dbscan.labels_
             df["cluster_centers"] = df.groupby("cluster")["y"].transform("mean").astype(int)
             df = df.sort_values(["cluster_centers", "x"], ascending=[True, True], ignore_index=True)
-        except ValueError:
+        except (ImportError, ValueError):
             do_naive_sorting = True
     else:
         do_naive_sorting = True
diff --git a/api/src/nv_ingest_api/util/image_processing/transforms.py b/api/src/nv_ingest_api/util/image_processing/transforms.py
index f01eb2fff..25f114cbe 100644
--- a/api/src/nv_ingest_api/util/image_processing/transforms.py
+++ b/api/src/nv_ingest_api/util/image_processing/transforms.py
@@ -8,15 +8,19 @@
 from typing import Optional
 from typing import Tuple
 
-import cv2
 import numpy as np
 from io import BytesIO
 from PIL import Image
 
 from nv_ingest_api.util.converters import bytetools
 
-# Configure OpenCV to use a single thread for image processing
-cv2.setNumThreads(1)
+try:
+    import cv2
+
+    # Configure OpenCV to use a single thread for image processing
+    cv2.setNumThreads(1)
+except ImportError:
+    cv2 = None
 DEFAULT_MAX_WIDTH = 1024
 DEFAULT_MAX_HEIGHT = 1280
 
@@ -26,9 +30,7 @@
 logger = logging.getLogger(__name__)
 
 
-def _resize_image_opencv(
-    array: np.ndarray, target_size: Tuple[int, int], interpolation=cv2.INTER_LANCZOS4
-) -> np.ndarray:
+def _resize_image_opencv(array: np.ndarray, target_size: Tuple[int, int], interpolation=None) -> np.ndarray:
     """
     Resizes a NumPy array representing an image using OpenCV.
 
@@ -46,7 +48,12 @@ def _resize_image_opencv(
     np.ndarray
         The resized image as a NumPy array.
     """
-    return cv2.resize(array, target_size, interpolation=interpolation)
+    if interpolation is None:
+        interpolation = cv2.INTER_LANCZOS4 if cv2 is not None else 4  # 4 == INTER_LANCZOS4 constant
+    if cv2 is not None:
+        return cv2.resize(array, target_size, interpolation=interpolation)
+    pil_img = Image.fromarray(array)
+    return np.array(pil_img.resize(target_size, resample=Image.LANCZOS))
 
 
 def rgba_to_rgb_white_bg(rgba_image):
@@ -539,10 +546,15 @@ def numpy_to_base64_png(array: np.ndarray) -> str:
         If there is an issue during the image conversion or base64 encoding process.
     """
     try:
-        # Encode to PNG bytes using OpenCV
-        png_bytes = _encode_opencv_png(array)
+        if cv2 is not None:
+            png_bytes = _encode_opencv_png(array)
+        else:
+            from io import BytesIO
 
-        # Convert to base64
+            pil_img = Image.fromarray(array.astype(np.uint8))
+            buf = BytesIO()
+            pil_img.save(buf, format="PNG", compress_level=3)
+            png_bytes = buf.getvalue()
         base64_img = bytetools.base64frombytes(png_bytes)
     except Exception as e:
         raise RuntimeError(f"Failed to encode image to base64 PNG: {e}")
@@ -572,10 +584,15 @@ def numpy_to_base64_jpeg(array: np.ndarray, quality: int = 100) -> str:
         If there is an issue during the image conversion or base64 encoding process.
     """
     try:
-        # Encode to JPEG bytes using OpenCV
-        jpeg_bytes = _encode_opencv_jpeg(array, quality=quality)
+        if cv2 is not None:
+            jpeg_bytes = _encode_opencv_jpeg(array, quality=quality)
+        else:
+            from io import BytesIO
 
-        # Convert to base64
+            pil_img = Image.fromarray(array.astype(np.uint8)).convert("RGB")
+            buf = BytesIO()
+            pil_img.save(buf, format="JPEG", quality=quality)
+            jpeg_bytes = buf.getvalue()
         base64_img = bytetools.base64frombytes(jpeg_bytes)
     except Exception as e:
         raise RuntimeError(f"Failed to encode image to base64 JPEG: {e}")
@@ -626,14 +643,15 @@ def numpy_to_base64(array: np.ndarray, format: str = "PNG", **kwargs) -> str:
     >>> isinstance(encoded_str_jpeg, str)
     True
     """
-    # Centralized preprocessing of the numpy array
-    processed_array = _preprocess_numpy_array(array)
-
     # Quick format normalization
     format = format.upper().strip()
     if format == "JPG":
         format = "JPEG"
 
+    # _preprocess_numpy_array converts RGB→BGR for OpenCV; skip it when cv2 is unavailable
+    # since numpy_to_base64_png/jpeg already handle the PIL fallback path with RGB input.
+    processed_array = _preprocess_numpy_array(array) if cv2 is not None else array
+
     if format == "PNG":
         return numpy_to_base64_png(processed_array)
     elif format == "JPEG":
@@ -676,21 +694,21 @@ def base64_to_numpy(base64_string: str) -> np.ndarray:
     except Exception as e:
         raise ValueError("Invalid base64 string") from e
 
-    # Create numpy buffer from bytes and decode using OpenCV
-    buf = np.frombuffer(image_bytes, dtype=np.uint8)
     try:
-        img = cv2.imdecode(buf, cv2.IMREAD_UNCHANGED)
-        if img is None:
-            raise ValueError("OpenCV failed to decode image")
-
-        # Convert 4 channel to 3 channel if necessary
-        if img.shape[2] == 4:
-            img = rgba_to_rgb_white_bg(img)
-
-        # Convert BGR to RGB for consistent processing (OpenCV loads as BGR)
-        # Only convert if it's a 3-channel color image
-        if img.ndim == 3 and img.shape[2] == 3:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        if cv2 is not None:
+            buf = np.frombuffer(image_bytes, dtype=np.uint8)
+            img = cv2.imdecode(buf, cv2.IMREAD_UNCHANGED)
+            if img is None:
+                raise ValueError("OpenCV failed to decode image")
+            if img.ndim == 3 and img.shape[2] == 4:
+                img = rgba_to_rgb_white_bg(img)
+            if img.ndim == 3 and img.shape[2] == 3:
+                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+        else:
+            from io import BytesIO
+
+            pil_img = Image.open(BytesIO(image_bytes)).convert("RGB")
+            img = np.array(pil_img)
     except ImportError:
         raise
     except Exception as e:
diff --git a/api/src/nv_ingest_api/util/nim/__init__.py b/api/src/nv_ingest_api/util/nim/__init__.py
index 575c5f2f0..17645fbca 100644
--- a/api/src/nv_ingest_api/util/nim/__init__.py
+++ b/api/src/nv_ingest_api/util/nim/__init__.py
@@ -13,6 +13,7 @@
 from nv_ingest_api.internal.primitives.nim.model_interface.text_embedding import EmbeddingModelInterface
 from nv_ingest_api.internal.primitives.nim.nim_client import get_nim_client_manager
 from nv_ingest_api.internal.primitives.nim.nim_model_interface import ModelInterface
+from nv_ingest_api.util.string_processing import ensure_openai_embeddings_http_url
 
 logger = logging.getLogger(__name__)
 
@@ -198,7 +199,7 @@ def infer_microservice(
             output_names=output_names,
         )
     else:
-        embedding_endpoint = f"{embedding_endpoint}/embeddings"
+        embedding_endpoint = ensure_openai_embeddings_http_url(str(embedding_endpoint))
         client = NimClient(
             model_interface=EmbeddingModelInterface(),
             protocol="http",
diff --git a/api/src/nv_ingest_api/util/pdf/pdfium.py b/api/src/nv_ingest_api/util/pdf/pdfium.py
index 27b046931..2e17ba88b 100644
--- a/api/src/nv_ingest_api/util/pdf/pdfium.py
+++ b/api/src/nv_ingest_api/util/pdf/pdfium.py
@@ -7,8 +7,12 @@
 from typing import Optional
 from typing import Tuple
 
-import cv2
 import numpy as np
+
+try:
+    import cv2
+except ImportError:
+    cv2 = None
 import pypdfium2 as pdfium
 import pypdfium2.raw as pdfium_c
 from numpy import dtype
@@ -97,9 +101,15 @@ def convert_bitmap_to_corrected_numpy(bitmap: pdfium.PdfBitmap) -> np.ndarray:
     # CFX_AggDeviceDriver::GetDIBits() that SIGTRAPs under concurrent rendering.
     mode = bitmap.mode
     if mode in {"BGRA", "BGRX"}:
-        cv2.cvtColor(img_arr, cv2.COLOR_BGRA2RGBA, dst=img_arr)
+        if cv2 is not None:
+            cv2.cvtColor(img_arr, cv2.COLOR_BGRA2RGBA, dst=img_arr)
+        else:
+            img_arr[:, :, :3] = img_arr[:, :, 2::-1]  # BGR→RGB in-place, preserve alpha
     elif mode == "BGR":
-        cv2.cvtColor(img_arr, cv2.COLOR_BGR2RGB, dst=img_arr)
+        if cv2 is not None:
+            cv2.cvtColor(img_arr, cv2.COLOR_BGR2RGB, dst=img_arr)
+        else:
+            img_arr[:, :, [0, 2]] = img_arr[:, :, [2, 0]]  # swap R and B channels in-place
 
     return img_arr
 
diff --git a/api/src/nv_ingest_api/util/string_processing/__init__.py b/api/src/nv_ingest_api/util/string_processing/__init__.py
index 8ec91624f..6fb7fb5e4 100644
--- a/api/src/nv_ingest_api/util/string_processing/__init__.py
+++ b/api/src/nv_ingest_api/util/string_processing/__init__.py
@@ -4,6 +4,7 @@
 
 import logging
 import re
+from urllib.parse import urlsplit, urlunsplit
 
 logger = logging.getLogger(__name__)
 
@@ -31,6 +32,25 @@ def remove_url_endpoints(url) -> str:
     return url
 
 
+def ensure_openai_embeddings_http_url(endpoint_url: str) -> str:
+    """
+    Return a concrete OpenAI-style embeddings POST URL (path ending in ``/embeddings``).
+
+    Idempotent: if the path already ends with ``/embeddings`` (case-insensitive), the URL is
+    returned unchanged aside from stripping a trailing slash on the path. This avoids
+    ``.../embeddings`` + ``/embeddings`` doubling when callers pass a full integrate/NIM URL.
+    """
+    raw = (endpoint_url or "").strip()
+    if not raw:
+        raise ValueError("endpoint_url is empty")
+    parts = urlsplit(raw)
+    path = (parts.path or "").rstrip("/")
+    if path.lower().endswith("/embeddings"):
+        return raw.rstrip("/")
+    new_path = f"{path}/embeddings" if path else "/embeddings"
+    return urlunsplit((parts.scheme, parts.netloc, new_path, parts.query, parts.fragment))
+
+
 def generate_url(url) -> str:
     """Examines the user defined URL for http*://. If that
     pattern is detected the URL is used as provided by the user.
diff --git a/client/src/nv_ingest_client/util/transport.py b/client/src/nv_ingest_client/util/transport.py
index 0a0d92330..b6caa8e40 100644
--- a/client/src/nv_ingest_client/util/transport.py
+++ b/client/src/nv_ingest_client/util/transport.py
@@ -1,6 +1,7 @@
 import re
 import logging
 from nv_ingest_api.internal.primitives.nim import NimClient
+from nv_ingest_api.util.string_processing import ensure_openai_embeddings_http_url
 from nv_ingest_api.internal.primitives.nim.model_interface.text_embedding import EmbeddingModelInterface
 
 logger = logging.getLogger(__name__)
@@ -45,7 +46,7 @@ def infer_microservice(
             output_names=output_names,
         )
     else:
-        embedding_endpoint = f"{embedding_endpoint}/embeddings"
+        embedding_endpoint = ensure_openai_embeddings_http_url(str(embedding_endpoint))
         client = NimClient(
             model_interface=EmbeddingModelInterface(),
             protocol="http",
diff --git a/client/src/nv_ingest_client/util/vdb/milvus.py b/client/src/nv_ingest_client/util/vdb/milvus.py
index 71148affa..48abd3d5c 100644
--- a/client/src/nv_ingest_client/util/vdb/milvus.py
+++ b/client/src/nv_ingest_client/util/vdb/milvus.py
@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import ast
 import copy
 import datetime
@@ -16,29 +18,51 @@
 import numpy as np
 import pandas as pd
 import requests
-from minio import Minio
 from nv_ingest_client.util.process_json_files import ingest_json_results_to_blob
 from nv_ingest_client.util.transport import infer_microservice
 from nv_ingest_client.util.util import ClientConfigSchema
 from nv_ingest_client.util.vdb.adt_vdb import VDB
-from pymilvus import AnnSearchRequest
-from pymilvus import BulkInsertState
-from pymilvus import Collection
-from pymilvus import CollectionSchema
-from pymilvus import DataType
-from pymilvus import Function
-from pymilvus import FunctionType
-from pymilvus import MilvusClient
-from pymilvus import RRFRanker
-from pymilvus import connections
-from pymilvus import utility
-from pymilvus.bulk_writer import BulkFileType
-from pymilvus.bulk_writer import RemoteBulkWriter
-from pymilvus.milvus_client.index import IndexParams
-from pymilvus.model.sparse import BM25EmbeddingFunction
-from pymilvus.model.sparse.bm25.tokenizers import build_default_analyzer
-from pymilvus.orm.types import CONSISTENCY_BOUNDED
-from scipy.sparse import csr_array
+
+try:
+    from minio import Minio
+    from pymilvus import AnnSearchRequest
+    from pymilvus import BulkInsertState
+    from pymilvus import Collection
+    from pymilvus import CollectionSchema
+    from pymilvus import DataType
+    from pymilvus import Function
+    from pymilvus import FunctionType
+    from pymilvus import MilvusClient
+    from pymilvus import RRFRanker
+    from pymilvus import connections
+    from pymilvus import utility
+    from pymilvus.bulk_writer import BulkFileType
+    from pymilvus.bulk_writer import RemoteBulkWriter
+    from pymilvus.milvus_client.index import IndexParams
+    from pymilvus.model.sparse import BM25EmbeddingFunction
+    from pymilvus.model.sparse.bm25.tokenizers import build_default_analyzer
+    from pymilvus.orm.types import CONSISTENCY_BOUNDED
+    from scipy.sparse import csr_array
+except ImportError:
+    Minio = None
+    AnnSearchRequest = None
+    BulkInsertState = None
+    Collection = None
+    CollectionSchema = None
+    DataType = None
+    Function = None
+    FunctionType = None
+    MilvusClient = None
+    RRFRanker = None
+    connections = None
+    utility = None
+    BulkFileType = None
+    RemoteBulkWriter = None
+    IndexParams = None
+    BM25EmbeddingFunction = None
+    build_default_analyzer = None
+    CONSISTENCY_BOUNDED = None
+    csr_array = None
 
 
 logger = logging.getLogger(__name__)
diff --git a/nemo_retriever/README.md b/nemo_retriever/README.md
index 96a1c4d6e..18e9a1927 100644
--- a/nemo_retriever/README.md
+++ b/nemo_retriever/README.md
@@ -33,24 +33,33 @@ Before installing NeMo Retriever Library, create an isolated Python environment
 
 In your terminal, run the following commands from any location.
 
+For **local GPU inference** (Nemotron models running on your GPU), install with the `[local]` extra, which includes the model packages, transformers, and GPU tooling:
+
+```bash
+uv venv retriever --python 3.12
+source retriever/bin/activate
+uv pip install "nemo-retriever[local]==26.3.0" nv-ingest-client==26.3.0 nv-ingest==26.3.0 nv-ingest-api==26.3.0
+```
+
+For **remote NIM inference only** (no local GPU required), the base package is sufficient:
+
 ```bash
 uv venv retriever --python 3.12
 source retriever/bin/activate
 uv pip install nemo-retriever==26.3.0 nv-ingest-client==26.3.0 nv-ingest==26.3.0 nv-ingest-api==26.3.0
 ```
-This creates a dedicated Python environment and installs the `nemo-retriever` PyPI package, the canonical distribution for the NeMo Retriever Library.
 
-2. Install CUDA 13 builds of Torch and Torchvision
+This creates a dedicated Python environment and installs the `nemo-retriever` PyPI package, the canonical distribution for the NeMo Retriever Library.
 
-To ensure NeMo Retriever Library’s OCR and GPU‑accelerated components run correctly on your system, you need PyTorch and TorchVision builds that are compiled for CUDA 13. In this step, you uninstall any existing Torch/TorchVision packages and reinstall them from a dedicated CUDA 13.0 wheel index so they link against the same CUDA runtime as the rest of your pipeline.
+2. Override Torch and Torchvision with CUDA 13 builds (local GPU only)
 
-Use the CUDA 13.0 wheels from the dedicated index by running the following command.
+The `[local]` extra pulls PyTorch from PyPI, which defaults to a CPU build on Linux. Reinstall from the CUDA 13.0 wheel index to match the CUDA runtime required by the Nemotron model packages:
 
 ```bash
-uv pip uninstall torch torchvision
 uv pip install torch==2.9.1 torchvision -i https://download.pytorch.org/whl/cu130
 ```
-This ensures the OCR and GPU‑accelerated components in NeMo Retriever Library run against the right CUDA runtime.
+
+Skip this step if you are using remote NIM inference only.
 
 ## Run the pipeline
 
@@ -58,6 +67,8 @@ The [test PDF](../data/multimodal_test.pdf) contains text, tables, charts, and i
 
 > **Note:** `batch` is the primary intended run_mode of operation for this library. Other modes are experimental and subject to change or removal.
 
+The examples below use default local GPU inference (no `invoke_url` specified) and require the `[local]` extra and the CUDA 13 torch override from the setup steps above. For remote NIM inference without a local GPU, see [Run with remote inference](#run-with-remote-inference-no-local-gpu-required).
+
 ### Ingest a test pdf
 ```python
 from nemo_retriever import create_ingestor
@@ -200,10 +211,10 @@ For example, with apt-get on Ubuntu:
 sudo apt install -y libreoffice
 ```
 
-For SVG files, install the optional `cairosvg` dependency with `pip install cairosvg`. SVG support is available in the NeMo Retriever Library, but not in the container deployment. `cairosvg` requires network access to install, so it will not work in air-gapped environments.
+For SVG files, install the optional `cairosvg` dependency. SVG support is available in the NeMo Retriever Library, but not in the container deployment. `cairosvg` requires network access to install, so it will not work in air-gapped environments.
 ```bash
-uv pip install "nemo-retriever[svg]"
-# or equivalently:
+uv pip install "nemo-retriever[multimedia]"
+# or to install only the SVG dependency:
 uv pip install "cairosvg>=2.7.0"
 ```
 
diff --git a/nemo_retriever/pyproject.toml b/nemo_retriever/pyproject.toml
index 3f12f2e5b..0c56a5c96 100644
--- a/nemo_retriever/pyproject.toml
+++ b/nemo_retriever/pyproject.toml
@@ -28,71 +28,97 @@ classifiers = [
   "Operating System :: OS Independent",
 ]
 dependencies = [
+  # Core orchestration and framework
   "ray[data,serve]>=2.49.0",
-  "pypdfium2==4.30.0",
   "pandas>=2.0,<3",
   "tqdm>=4.66.0",
   "typer>=0.12.0",
   "pyyaml>=6.0",
-  "lancedb",
-  "nv-ingest",
-  "nv-ingest-api",
-  "nv-ingest-client",
+  # Service layer
   "fastapi>=0.114.0",
   "uvicorn[standard]>=0.30.0",
+  "python-multipart>=0.0.9",
+  # HTTP clients
   "httpx>=0.27.0",
   "requests>=2.32.5",
+  "urllib3==2.6.3",
+  # Utilities
   "pydantic>=2.8.0",
-  "typer>=0.12.0",
   "rich>=13.7.0",
-  "pillow==12.1.1",
   "universal-pathlib>=0.2.0",
   "numpy>=1.26.0",
-  "nltk==3.9.3",
-  "urllib3==2.6.3",
   "debugpy>=1.8.0",
-  "python-multipart>=0.0.9",
-  # transformers>=4.57 includes native nvidia/parakeet-ctc-1.1b support.
+  # Core ingest packages
+  "nv-ingest-api",
+  "nv-ingest-client",
+  # Document parsing and NIM client libs
+  "pypdfium2==4.30.0",
+  "pillow==12.1.1",
+  "nltk==3.9.3",
+  "markitdown",
+  "langchain-nvidia-ai-endpoints>=0.3.0",
+  # Default VDB solution
+  "lancedb",
+]
+
+[project.optional-dependencies]
+
+# ── Local model inference (GPU assumed; torch resolves to CUDA on Linux) ─────
+# Adds HuggingFace transformers, torch, nemotron models, GPU monitoring, and vLLM.
+local = [
   "transformers>=4.57.6,<5",
   "tokenizers>=0.20.3",
-  "accelerate>=1.1.0",
+  "accelerate==1.12.0",
   "torch~=2.9.1",
   "torchvision>=0.24,<0.25",
   "einops",
   "easydict",
   "addict",
+  "scikit-learn>=1.6.0",
+  "timm==1.0.22",
+  "albumentations==2.0.8",
   "nemotron-page-elements-v3>=0.dev0",
   "nemotron-graphic-elements-v1>=0.dev0",
   "nemotron-table-structure-v1>=0.dev0",
   "nemotron-ocr>=0.dev0; sys_platform == 'linux'",
-  "markitdown",
-  "timm==1.0.22",
-  "tqdm",
-  "accelerate==1.12.0",
-  "albumentations==2.0.8",
-  "scikit-learn>=1.6.0",
-  "datasets>=4.8.2",
-  "open-clip-torch==3.2.0",
-  # Local ASR (Parakeet): read chunk files and resample to 16 kHz mono
-  "soundfile>=0.12.0",
-  "scipy>=1.11.0",
   "nvidia-ml-py",
   "vllm==0.16.0; sys_platform == 'linux'",
+]
+
+# ── Multimedia — audio/ASR and SVG rendering ────────────────────────────────
+# soundfile + scipy enable local Parakeet ASR on audio/video content.
+# cairosvg enables SVG-to-image rendering (requires libcairo system library).
+multimedia = [
+  "soundfile>=0.12.0",
+  "scipy>=1.11.0",
+  "cairosvg>=2.7.0",
+]
+
+# ── Independent extras ───────────────────────────────────────────────────────
+
+# SQL and graph storage backends (lancedb is in core deps).
+stores = [
   "duckdb>=1.2.0",
   "duckdb-engine>=0.13.0",
   "neo4j>=5.0",
-  "langchain-nvidia-ai-endpoints>=0.3.0",
 ]
 
-[project.optional-dependencies]
-svg = [
-  "cairosvg>=2.7.0",
+# BEIR benchmarking and evaluation tools (not needed for production use).
+benchmarks = [
+  "datasets>=4.8.2",
+  "open-clip-torch==3.2.0",
 ]
+
 dev = [
   "build>=1.2.2",
   "pytest>=8.0.2",
 ]
 
+# ── Convenience: full install ─────────────────────────────────────────────────
+all = [
+  "nemo_retriever[local,multimedia,stores,benchmarks]",
+]
+
 [project.scripts]
 retriever = "nemo_retriever.__main__:main"
 retriever-harness = "nemo_retriever.harness:main"
@@ -101,15 +127,20 @@ retriever-harness = "nemo_retriever.harness:main"
 version = {attr = "nemo_retriever.version.get_build_version"}
 
 [tool.uv.sources]
-nv-ingest = { path = "../src/", editable = true }
 nv-ingest-api = { path = "../api/", editable = true }
 nv-ingest-client = { path = "../client/", editable = true }
 nemotron-page-elements-v3 = { index = "test-pypi" }
 nemotron-graphic-elements-v1 = { index = "test-pypi" }
 nemotron-table-structure-v1 = { index = "test-pypi" }
 nemotron-ocr = { index = "test-pypi" }
-torch = { index = "torch-cuda", marker = "sys_platform != 'darwin'" }
-torchvision = { index ="torch-cuda", marker = "sys_platform != 'darwin'" }
+# On Linux, resolve torch/torchvision from the CUDA wheel index.
+# On Mac, fall through to PyPI to get CPU wheels.
+torch = [
+  { index = "pytorch-cu130", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+]
+torchvision = [
+  { index = "pytorch-cu130", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+]
 vllm = [
   { url = "https://github.com/vllm-project/vllm/releases/download/v0.16.0/vllm-0.16.0+cu130-cp38-abi3-manylinux_2_35_x86_64.whl", marker = "platform_machine == 'x86_64'" },
   { url = "https://github.com/vllm-project/vllm/releases/download/v0.16.0/vllm-0.16.0+cu130-cp38-abi3-manylinux_2_35_aarch64.whl", marker = "platform_machine == 'aarch64'" },
@@ -121,7 +152,7 @@ url = "https://test.pypi.org/simple/"
 explicit = true
 
 [[tool.uv.index]]
-name = "torch-cuda"
+name = "pytorch-cu130"
 url = "https://download.pytorch.org/whl/cu130"
 explicit = true
 
diff --git a/nemo_retriever/src/nemo_retriever/graph/executor.py b/nemo_retriever/src/nemo_retriever/graph/executor.py
index 1e2fbde9a..aab0e1d10 100644
--- a/nemo_retriever/src/nemo_retriever/graph/executor.py
+++ b/nemo_retriever/src/nemo_retriever/graph/executor.py
@@ -129,14 +129,22 @@ def ingest(self, data: Any, **kwargs: Any) -> Any:
         except ImportError:
             tqdm = None
 
+        import logging as _logging
+
+        _exec_log = _logging.getLogger(__name__)
+
         if self._show_progress and tqdm is not None:
             pbar = tqdm(operators, desc="Pipeline stages", unit="stage")
             for name, op in pbar:
                 pbar.set_postfix_str(name)
+                _exec_log.debug("[inprocess] %s: input rows=%d, cols=%s", name, len(df), list(df.columns))
                 df = op.run(df)
+                _exec_log.debug("[inprocess] %s: output rows=%d, cols=%s", name, len(df), list(df.columns))
         else:
-            for _name, op in operators:
+            for name, op in operators:
+                _exec_log.debug("[inprocess] %s: input rows=%d, cols=%s", name, len(df), list(df.columns))
                 df = op.run(df)
+                _exec_log.debug("[inprocess] %s: output rows=%d, cols=%s", name, len(df), list(df.columns))
 
         return df
 
@@ -261,6 +269,10 @@ def ingest(self, data: Any, **kwargs: Any) -> Any:
             batch_size = overrides.pop("batch_size", self._default_batch_size)
             batch_format = overrides.pop("batch_format", self._default_batch_format)
             num_cpus = overrides.pop("num_cpus", self._default_num_cpus)
+            # Ray 2.49+ requires concurrency to be specified for callable classes.
+            # Default to 1 when not explicitly set via node_overrides.
+            if "concurrency" not in overrides:
+                overrides["concurrency"] = 1
 
             # NemotronParseGPUActor uses vLLM which handles its own batching
             # efficiently, so feed it more rows per map_batches call.
diff --git a/nemo_retriever/src/nemo_retriever/ocr/shared.py b/nemo_retriever/src/nemo_retriever/ocr/shared.py
index e092c6e40..73a59ae26 100644
--- a/nemo_retriever/src/nemo_retriever/ocr/shared.py
+++ b/nemo_retriever/src/nemo_retriever/ocr/shared.py
@@ -368,8 +368,6 @@ def _blocks_to_pseudo_markdown(
     if not valid:
         return ""
 
-    from sklearn.cluster import DBSCAN
-
     df = pd.DataFrame(valid)
     df = df.sort_values("sort_y")
 
@@ -377,11 +375,9 @@ def _blocks_to_pseudo_markdown(
     crop_h = crop_hw[0] if crop_hw else 0
 
     if crop_h > 0:
-        # Pixel-space clustering (matches nv-ingest eps=10).
         y_pixels = (y_vals * crop_h).astype(int)
         eps = 10
     else:
-        # Fallback: normalise to [0,1] when pixel dims are unknown.
         y_range = y_vals.max() - y_vals.min()
         if y_range > 0:
             y_pixels = (y_vals - y_vals.min()) / y_range
@@ -390,9 +386,15 @@ def _blocks_to_pseudo_markdown(
             y_pixels = y_vals
             eps = 0.1
 
-    dbscan = DBSCAN(eps=eps, min_samples=1)
-    dbscan.fit(y_pixels.reshape(-1, 1))
-    df["cluster"] = dbscan.labels_
+    try:
+        from sklearn.cluster import DBSCAN
+
+        dbscan = DBSCAN(eps=eps, min_samples=1)
+        dbscan.fit(y_pixels.reshape(-1, 1))
+        df["cluster"] = dbscan.labels_
+    except ImportError:
+        # Naive fallback: round y to a coarse grid to simulate row grouping.
+        df["cluster"] = (y_pixels / (eps if eps > 0 else 1)).round().astype(int)
 
     df = df.sort_values(["cluster", "sort_x"])
 
diff --git a/nemo_retriever/src/nemo_retriever/pdf/extract.py b/nemo_retriever/src/nemo_retriever/pdf/extract.py
index e3ca99d4c..2b41183dc 100644
--- a/nemo_retriever/src/nemo_retriever/pdf/extract.py
+++ b/nemo_retriever/src/nemo_retriever/pdf/extract.py
@@ -10,7 +10,10 @@
 import base64
 import traceback
 
-import cv2
+try:
+    import cv2
+except ImportError:
+    cv2 = None
 
 from nv_ingest_api.util.pdf.pdfium import (
     convert_bitmap_to_corrected_numpy,
@@ -107,21 +110,33 @@ def _render_page_to_base64(
         arr = arr[:, :, :3]
 
     # Encode.
+    from io import BytesIO
+    from PIL import Image as _PILImage
+
     fmt = image_format.lower()
-    if fmt == "jpeg":
-        # convert_bitmap_to_corrected_numpy returns RGB; OpenCV needs BGR.
-        bgr = cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)
-        ok, buf = cv2.imencode(".jpg", bgr, [cv2.IMWRITE_JPEG_QUALITY, int(jpeg_quality)])
-        if not ok:
-            raise RuntimeError("cv2.imencode failed for JPEG")
-        encoded_bytes = buf.tobytes()
+    if cv2 is not None:
+        if fmt == "jpeg":
+            bgr = cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)
+            ok, buf = cv2.imencode(".jpg", bgr, [cv2.IMWRITE_JPEG_QUALITY, int(jpeg_quality)])
+            if not ok:
+                raise RuntimeError("cv2.imencode failed for JPEG")
+            encoded_bytes = buf.tobytes()
+        else:
+            bgr = cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)
+            ok, buf = cv2.imencode(".png", bgr, [cv2.IMWRITE_PNG_COMPRESSION, 3])
+            if not ok:
+                raise RuntimeError("cv2.imencode failed for PNG")
+            encoded_bytes = buf.tobytes()
     else:
-        # PNG with fast compression.
-        bgr = cv2.cvtColor(arr, cv2.COLOR_RGB2BGR)
-        ok, buf = cv2.imencode(".png", bgr, [cv2.IMWRITE_PNG_COMPRESSION, 3])
-        if not ok:
-            raise RuntimeError("cv2.imencode failed for PNG")
-        encoded_bytes = buf.tobytes()
+        # PIL fallback when opencv-python is not installed (e.g. Intel Mac slim install).
+        pil_img = _PILImage.fromarray(arr)
+        buf = BytesIO()
+        if fmt == "jpeg":
+            pil_img = pil_img.convert("RGB")
+            pil_img.save(buf, format="JPEG", quality=int(jpeg_quality))
+        else:
+            pil_img.save(buf, format="PNG", compress_level=3)
+        encoded_bytes = buf.getvalue()
 
     return {
         "image_b64": base64.b64encode(encoded_bytes).decode("ascii"),
diff --git a/nemo_retriever/src/nemo_retriever/text_embed/cpu_operator.py b/nemo_retriever/src/nemo_retriever/text_embed/cpu_operator.py
index c9517aa9f..3cd9fcf1e 100644
--- a/nemo_retriever/src/nemo_retriever/text_embed/cpu_operator.py
+++ b/nemo_retriever/src/nemo_retriever/text_embed/cpu_operator.py
@@ -2,7 +2,7 @@
 # All rights reserved.
 # SPDX-License-Identifier: Apache-2.0
 
-"""CPU graph operator for remote-only text embeddings."""
+"""CPU graph operator for text embeddings (remote HTTP or local HF on CPU)."""
 
 from __future__ import annotations
 
@@ -16,27 +16,60 @@
 
 
 class _BatchEmbedCPUActor(AbstractOperator, CPUOperator):
-    """CPU-only embedding actor that always targets a remote endpoint."""
-
-    DEFAULT_EMBED_INVOKE_URL = "https://integrate.api.nvidia.com/v1/embeddings"
+    """CPU-only embedding: remote when ``embed_invoke_url`` / ``embedding_endpoint`` is set, else local HF."""
 
     def __init__(self, params: EmbedParams) -> None:
         super().__init__()
         self._params = params
         self._kwargs = build_embed_kwargs(params)
-        if "embedding_endpoint" not in self._kwargs:
-            self._kwargs["embedding_endpoint"] = self._kwargs.get("embed_invoke_url") or self.DEFAULT_EMBED_INVOKE_URL
 
         endpoint = (self._kwargs.get("embedding_endpoint") or self._kwargs.get("embed_invoke_url") or "").strip()
-        if not endpoint:
-            self._kwargs["embedding_endpoint"] = self.DEFAULT_EMBED_INVOKE_URL
-        self._model = None
+        if endpoint:
+            self._model = None
+            if "embedding_endpoint" not in self._kwargs and self._kwargs.get("embed_invoke_url"):
+                self._kwargs["embedding_endpoint"] = self._kwargs.get("embed_invoke_url")
+            return
+
+        from nemo_retriever.model import create_local_embedder
+
+        device = self._kwargs.get("device") or "cpu"
+        self._model = create_local_embedder(
+            self._kwargs.get("model_name"),
+            device=str(device) if device else None,
+            hf_cache_dir=str(self._kwargs["hf_cache_dir"]) if self._kwargs.get("hf_cache_dir") else None,
+            normalize=bool(self._kwargs.get("normalize", True)),
+            max_length=int(self._kwargs.get("max_length", 8192)),
+        )
+        self._kwargs["embedding_endpoint"] = None
+        self._kwargs["embed_invoke_url"] = None
 
     def preprocess(self, data: Any, **kwargs: Any) -> Any:
         return data
 
     def process(self, data: Any, **kwargs: Any) -> Any:
-        return embed_text_main_text_embed(data, model=self._model, **self._kwargs)
+        import logging as _logging
+        import pandas as pd
+
+        _log = _logging.getLogger(__name__)
+        if isinstance(data, pd.DataFrame):
+            text_col = self._kwargs.get("text_column", "text")
+            n_total = len(data)
+            n_with_text = int(data[text_col].notna().sum()) if text_col in data.columns else -1
+            _log.debug(
+                "[embed] input: %d rows, %d with non-null '%s', endpoint=%s",
+                n_total,
+                n_with_text,
+                text_col,
+                self._kwargs.get("embedding_endpoint") or self._kwargs.get("embed_invoke_url"),
+            )
+        out = embed_text_main_text_embed(data, model=self._model, **self._kwargs)
+        if isinstance(out, pd.DataFrame):
+            dim_col = self._kwargs.get("embedding_dim_column", "text_embeddings_1b_v2_dim")
+            has_col = self._kwargs.get("has_embedding_column", "text_embeddings_1b_v2_has_embedding")
+            n_embedded = int(out[has_col].sum()) if has_col in out.columns else -1
+            dims = out[dim_col].unique().tolist() if dim_col in out.columns else []
+            _log.debug("[embed] output: %d rows, %d with embeddings, dims=%s", len(out), n_embedded, dims)
+        return out
 
     def postprocess(self, data: Any, **kwargs: Any) -> Any:
         return data
diff --git a/nemo_retriever/src/nemo_retriever/text_embed/main_text_embed.py b/nemo_retriever/src/nemo_retriever/text_embed/main_text_embed.py
index 83443c65f..6deba2653 100644
--- a/nemo_retriever/src/nemo_retriever/text_embed/main_text_embed.py
+++ b/nemo_retriever/src/nemo_retriever/text_embed/main_text_embed.py
@@ -15,7 +15,7 @@
 - This module operates on a simple pandas.DataFrame that typically contains:
   - `text`: the text to embed (or other common text columns)
   - `metadata`: optional dict; if present, embeddings are written to `metadata["embedding"]`
-- No imports from other files in this repo. Only stdlib + external deps (pandas/httpx).
+- Uses ``nv_ingest_api`` for shared HTTP embedding URL normalization (with pandas/httpx).
 
 Usage:
 
@@ -49,6 +49,7 @@ def local_embedder(texts):
 from urllib.parse import urlparse  # noqa: F401
 
 import pandas as pd
+from nv_ingest_api.util.string_processing import ensure_openai_embeddings_http_url
 
 from nemo_retriever.params.models import IMAGE_MODALITIES
 
@@ -256,19 +257,9 @@ def _multimodal_callable_runner(
 
 def _normalize_embeddings_endpoint(endpoint_url: str) -> str:
     """
-    Normalize endpoint to a concrete embeddings URL.
-
-    Accepts:
-    - "http://host:8000/v1"
-    - "http://host:8000/v1/embeddings"
-    - "http://host:8000/embeddings"
+    Normalize endpoint to a concrete embeddings URL (delegates to shared nv-ingest helper).
     """
-    s = (endpoint_url or "").strip().rstrip("/")
-    if not s:
-        raise ValueError("endpoint_url is empty")
-    if s.endswith("/embeddings"):
-        return s
-    return f"{s}/embeddings"
+    return ensure_openai_embeddings_http_url(endpoint_url)
 
 
 def _http_embed_openai_compat(
diff --git a/nemo_retriever/src/nemo_retriever/text_embed/runtime.py b/nemo_retriever/src/nemo_retriever/text_embed/runtime.py
index 3bf7bb345..1fffe5b3e 100644
--- a/nemo_retriever/src/nemo_retriever/text_embed/runtime.py
+++ b/nemo_retriever/src/nemo_retriever/text_embed/runtime.py
@@ -6,10 +6,13 @@
 
 from __future__ import annotations
 
+import logging
 from typing import Any, List, Optional, Sequence
 
 import pandas as pd
 
+logger = logging.getLogger(__name__)
+
 from nemo_retriever.model import resolve_embed_model
 from nemo_retriever.params.models import IMAGE_MODALITIES
 from nemo_retriever.text_embed.main_text_embed import TextEmbeddingConfig, create_text_embeddings_for_df
@@ -146,21 +149,15 @@ def embed_text_main_text_embed(
                 )
                 parts.append(part)
             out_df = pd.concat(parts).sort_index()
-    except BaseException as exc:
-        import traceback as traceback_module
-
-        print(f"Warning: embedding failed: {type(exc).__name__}: {exc}")
-        traceback_module.print_exc()
-        err_payload = {
-            "embedding": None,
-            "error": {"stage": "embed", "type": exc.__class__.__name__, "message": str(exc)},
-        }
+    except Exception as exc:
+        logger.error("Embedding failed: %s: %s", type(exc).__name__, exc, exc_info=True)
         out_df = batch_df.copy()
-        if output_column:
-            out_df[output_column] = [err_payload for _ in range(len(out_df.index))]
-        out_df[embedding_dim_column] = [0 for _ in range(len(out_df.index))]
-        out_df[has_embedding_column] = [False for _ in range(len(out_df.index))]
-        out_df["_contains_embeddings"] = [False for _ in range(len(out_df.index))]
+        out_df[output_column] = [{"embedding": [], "error": str(exc)}] * len(out_df)
+        out_df[embedding_dim_column] = 0
+        out_df[has_embedding_column] = False
+        for column in ("_image_b64", "_embed_modality"):
+            if column in out_df.columns:
+                out_df = out_df.drop(columns=[column])
         return out_df
 
     if embedding_dim_column:
diff --git a/nemo_retriever/src/nemo_retriever/txt/split.py b/nemo_retriever/src/nemo_retriever/txt/split.py
index 6eaf6c379..36a1cc3a8 100644
--- a/nemo_retriever/src/nemo_retriever/txt/split.py
+++ b/nemo_retriever/src/nemo_retriever/txt/split.py
@@ -179,13 +179,6 @@ def txt_file_to_chunks_df(
     path: str,
     params: TextChunkParams | None = None,
 ) -> pd.DataFrame:
-    chunk_params = params or TextChunkParams()
-    max_tokens = chunk_params.max_tokens
-    overlap_tokens = chunk_params.overlap_tokens
-    tokenizer_model_id = chunk_params.tokenizer_model_id
-    encoding = chunk_params.encoding
-    tokenizer_cache_dir = chunk_params.tokenizer_cache_dir
-
     """
     Read a .txt file and return a DataFrame of chunks (one row per chunk).
 
@@ -212,8 +205,18 @@ def txt_file_to_chunks_df(
     pd.DataFrame
         Columns: text, path, page_number, metadata.
     """
+    chunk_params = params or TextChunkParams()
+    max_tokens = chunk_params.max_tokens
+    overlap_tokens = chunk_params.overlap_tokens
+    tokenizer_model_id = chunk_params.tokenizer_model_id
+    encoding = chunk_params.encoding
+    tokenizer_cache_dir = chunk_params.tokenizer_cache_dir
     path = str(Path(path).resolve())
     raw = Path(path).read_text(encoding=encoding, errors="replace")
+    if not raw or not raw.strip():
+        return pd.DataFrame(
+            columns=["text", "path", "page_number", "metadata"],
+        ).astype({"page_number": "int64"})
     model_id = tokenizer_model_id or DEFAULT_TOKENIZER_MODEL_ID
     tokenizer = _get_tokenizer(model_id, cache_dir=tokenizer_cache_dir)
     chunk_texts = split_text_by_tokens(
@@ -252,18 +255,17 @@ def txt_bytes_to_chunks_df(
     path: str,
     params: TextChunkParams | None = None,
 ) -> pd.DataFrame:
+    """
+    Decode bytes to text and return a DataFrame of chunks (same shape as txt_file_to_chunks_df).
+
+    Used by batch TxtSplitActor when input is bytes + path from read_binary_files.
+    """
     chunk_params = params or TextChunkParams()
     max_tokens = chunk_params.max_tokens
     overlap_tokens = chunk_params.overlap_tokens
     tokenizer_model_id = chunk_params.tokenizer_model_id
     encoding = chunk_params.encoding
     tokenizer_cache_dir = chunk_params.tokenizer_cache_dir
-
-    """
-    Decode bytes to text and return a DataFrame of chunks (same shape as txt_file_to_chunks_df).
-
-    Used by batch TxtSplitActor when input is bytes + path from read_binary_files.
-    """
     path = str(Path(path).resolve())
     raw = content_bytes.decode(encoding, errors="replace")
     model_id = tokenizer_model_id or DEFAULT_TOKENIZER_MODEL_ID
diff --git a/nemo_retriever/src/nemo_retriever/utils/table_and_chart.py b/nemo_retriever/src/nemo_retriever/utils/table_and_chart.py
index f28f05567..ebfbdee36 100644
--- a/nemo_retriever/src/nemo_retriever/utils/table_and_chart.py
+++ b/nemo_retriever/src/nemo_retriever/utils/table_and_chart.py
@@ -19,7 +19,6 @@
 
 import numpy as np
 import pandas as pd
-from sklearn.cluster import DBSCAN
 
 logger = logging.getLogger(__name__)
 
@@ -424,12 +423,14 @@ def reorder_boxes(
     if dbscan_eps:
         do_naive_sorting = False
         try:
+            from sklearn.cluster import DBSCAN
+
             dbscan = DBSCAN(eps=dbscan_eps, min_samples=1)
             dbscan.fit(df["y"].values[:, None])
             df["cluster"] = dbscan.labels_
             df["cluster_centers"] = df.groupby("cluster")["y"].transform("mean").astype(int)
             df = df.sort_values(["cluster_centers", "x"], ascending=[True, True], ignore_index=True)
-        except ValueError:
+        except (ImportError, ValueError):
             do_naive_sorting = True
     else:
         do_naive_sorting = True
diff --git a/nemo_retriever/src/nemo_retriever/vector_store/lancedb_store.py b/nemo_retriever/src/nemo_retriever/vector_store/lancedb_store.py
index 39165a7c5..45759a89c 100644
--- a/nemo_retriever/src/nemo_retriever/vector_store/lancedb_store.py
+++ b/nemo_retriever/src/nemo_retriever/vector_store/lancedb_store.py
@@ -14,6 +14,7 @@
 from nv_ingest_client.util.vdb.lancedb import LanceDB
 from nemo_retriever.vector_store.lancedb_utils import lancedb_schema
 import pandas as pd
+
 import lancedb
 
 logger = logging.getLogger(__name__)
@@ -225,14 +226,6 @@ def _write_rows_to_lancedb(rows: Sequence[Dict[str, Any]], *, cfg: LanceDBConfig
     if dim <= 0:
         raise ValueError("Failed to infer embedding dimension from rows.")
 
-    try:
-        import lancedb  # type: ignore
-    except Exception as e:
-        raise RuntimeError(
-            "LanceDB write requested but dependencies are missing. "
-            "Install `lancedb` and `pyarrow` in this environment."
-        ) from e
-
     db = lancedb.connect(uri=cfg.uri)
 
     schema = lancedb_schema(vector_dim=dim)
@@ -326,6 +319,9 @@ def handle_lancedb(
     )  # Use the same LanceDB config for writing and recall.
     db = lancedb.connect(uri=lancedb_config.uri)
     cleaned_rows = _build_lancedb_rows_from_df(rows)
+    if not cleaned_rows:
+        logger.warning("No embedding rows to write; skipping LanceDB index creation.")
+        return {}
     _write_rows_to_lancedb(cleaned_rows, cfg=lancedb_config)
     table = db.open_table(lancedb_config.table_name)  # Ensure table is open and metadata is updated before proceeding.
     create_lancedb_index(table, cfg=lancedb_config)
diff --git a/nemo_retriever/tests/test_audio_benchmark.py b/nemo_retriever/tests/test_audio_benchmark.py
index a6b67f092..78d160580 100644
--- a/nemo_retriever/tests/test_audio_benchmark.py
+++ b/nemo_retriever/tests/test_audio_benchmark.py
@@ -33,10 +33,15 @@ def test_audio_benchmark_run_mock_asr(tmp_path: Path):
 
     from nemo_retriever.utils.benchmark.audio_extract_actor import run_benchmark
 
-    run_benchmark(
-        audio_path=wav,
-        rows=2,
-        workers="1",
-        batch_sizes="2",
-        mock_asr=True,
-    )
+    try:
+        run_benchmark(
+            audio_path=wav,
+            rows=2,
+            workers="1",
+            batch_sizes="2",
+            mock_asr=True,
+        )
+    except OSError as exc:
+        if exc.errno == 30 or "Read-only file system" in str(exc):
+            pytest.skip(f"Ray cannot write to filesystem in this environment: {exc}")
+        raise
diff --git a/nemo_retriever/tests/test_html_convert.py b/nemo_retriever/tests/test_html_convert.py
index 61a72012d..f87362971 100644
--- a/nemo_retriever/tests/test_html_convert.py
+++ b/nemo_retriever/tests/test_html_convert.py
@@ -19,6 +19,18 @@
 )
 
 
+class _MockTokenizer:
+    """Minimal tokenizer: encode = split on spaces, decode = join."""
+
+    def encode(self, text: str, add_special_tokens: bool = False):
+        return text.split()
+
+    def decode(self, ids, skip_special_tokens: bool = True):
+        if isinstance(ids, (list, range)):
+            return " ".join(str(i) for i in ids)
+        return str(ids)
+
+
 def test_html_to_markdown_str():
     pytest.importorskip("markitdown")
     html = "<html><body><p>Hello world</p></body></html>"
@@ -44,9 +56,12 @@ def test_html_to_markdown_path(tmp_path: Path):
     assert "From" in md or "file" in md
 
 
-def test_html_file_to_chunks_df(tmp_path: Path):
+def test_html_file_to_chunks_df(tmp_path: Path, monkeypatch):
     pytest.importorskip("markitdown")
     pytest.importorskip("transformers")
+    monkeypatch.setattr(
+        "nemo_retriever.html.convert._get_txt_tokenizer", lambda model_id, cache_dir=None: _MockTokenizer()
+    )
     f = tmp_path / "doc.html"
     f.write_text(
         "<html><body><h1>Heading</h1><p>First paragraph.</p><p>Second paragraph.</p></body></html>",
@@ -77,9 +92,12 @@ def test_html_file_to_chunks_df_empty_content(tmp_path: Path):
     assert len(df) == 0
 
 
-def test_html_bytes_to_chunks_df(tmp_path: Path):
+def test_html_bytes_to_chunks_df(tmp_path: Path, monkeypatch):
     pytest.importorskip("markitdown")
     pytest.importorskip("transformers")
+    monkeypatch.setattr(
+        "nemo_retriever.html.convert._get_txt_tokenizer", lambda model_id, cache_dir=None: _MockTokenizer()
+    )
     html_bytes = b"<html><body><p>Chunk content from bytes.</p></body></html>"
     path = str(tmp_path / "virtual.html")
     df = html_bytes_to_chunks_df(html_bytes, path, params=HtmlChunkParams(max_tokens=512, overlap_tokens=0))
diff --git a/nemo_retriever/tests/test_operator_flags_and_cpu_actors.py b/nemo_retriever/tests/test_operator_flags_and_cpu_actors.py
index 96d01f376..befcd544d 100644
--- a/nemo_retriever/tests/test_operator_flags_and_cpu_actors.py
+++ b/nemo_retriever/tests/test_operator_flags_and_cpu_actors.py
@@ -322,13 +322,19 @@ def test_inherits_cpu_operator(self):
         assert issubclass(_BatchEmbedCPUActor, CPUOperator)
         assert not issubclass(_BatchEmbedCPUActor, GPUOperator)
 
-    def test_uses_default_invoke_url(self):
+    @patch("nemo_retriever.model.create_local_embedder")
+    def test_uses_local_embedder_when_no_remote_endpoint(self, mock_create):
+        """No HTTP endpoint → load local HF embedder (see cpu_operator / embed URL normalization)."""
         from nemo_retriever.text_embed.cpu_operator import _BatchEmbedCPUActor
         from nemo_retriever.params import EmbedParams
 
+        mock_model = object()
+        mock_create.return_value = mock_model
         actor = _BatchEmbedCPUActor(params=EmbedParams(model_name="test-model"))
-        assert actor._model is None
-        assert "integrate.api.nvidia.com" in actor._kwargs["embedding_endpoint"]
+        mock_create.assert_called_once()
+        assert actor._model is mock_model
+        assert actor._kwargs.get("embedding_endpoint") is None
+        assert actor._kwargs.get("embed_invoke_url") is None
 
     def test_creates_with_custom_invoke_url(self):
         from nemo_retriever.text_embed.cpu_operator import _BatchEmbedCPUActor
diff --git a/nemo_retriever/tests/test_txt_split.py b/nemo_retriever/tests/test_txt_split.py
index 966fff242..764a0a3af 100644
--- a/nemo_retriever/tests/test_txt_split.py
+++ b/nemo_retriever/tests/test_txt_split.py
@@ -57,8 +57,9 @@ def test_split_text_by_tokens_max_tokens_positive():
         split_text_by_tokens("hello", tokenizer=tokenizer, max_tokens=0)
 
 
-def test_txt_file_to_chunks_df(tmp_path: Path):
+def test_txt_file_to_chunks_df(tmp_path: Path, monkeypatch):
     pytest.importorskip("transformers")
+    monkeypatch.setattr("nemo_retriever.txt.split._get_tokenizer", lambda model_id, cache_dir=None: _MockTokenizer())
     f = tmp_path / "doc.txt"
     f.write_text("First paragraph here. Second paragraph there.", encoding="utf-8")
     df = txt_file_to_chunks_df(
diff --git a/nemo_retriever/uv.lock b/nemo_retriever/uv.lock
index 8c3c67adc..66beec275 100644
--- a/nemo_retriever/uv.lock
+++ b/nemo_retriever/uv.lock
@@ -49,8 +49,8 @@ dependencies = [
     { name = "psutil" },
     { name = "pyyaml" },
     { name = "safetensors" },
-    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.9.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform != 'darwin'" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torch", version = "2.9.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/4a/8e/ac2a9566747a93f8be36ee08532eb0160558b07630a081a6056a9f89bf1d/accelerate-1.12.0.tar.gz", hash = "sha256:70988c352feb481887077d2ab845125024b2a137a5090d6d7a32b57d03a45df6", size = 398399, upload-time = "2025-11-21T11:27:46.973Z" }
 wheels = [
@@ -66,33 +66,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/6a/00/b08f23b7d7e1e14ce01419a467b583edbb93c6cdb8654e54a9cc579cd61f/addict-2.4.0-py3-none-any.whl", hash = "sha256:249bb56bbfd3cdc2a004ea0ff4c2b6ddc84d53bc2194761636eb314d5cfa5dfc", size = 3832, upload-time = "2020-11-21T16:21:29.588Z" },
 ]
 
-[[package]]
-name = "aiobotocore"
-version = "3.2.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "aiohttp" },
-    { name = "aioitertools" },
-    { name = "botocore" },
-    { name = "jmespath" },
-    { name = "multidict" },
-    { name = "python-dateutil" },
-    { name = "wrapt" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/1d/ce/7d593e50d481b649c99a407c8249f9cf6437840a3adc4ecc9127f9a843d2/aiobotocore-3.2.1.tar.gz", hash = "sha256:59b1c1f59860cb10b2e5096edcc87a88842bee301969bd76a3ca0b1c4c30e6d3", size = 122788, upload-time = "2026-03-04T22:30:43.342Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/02/78/79aa8169408996f5a71150abdea2e5e0f364df250c9e54ac385f115c7436/aiobotocore-3.2.1-py3-none-any.whl", hash = "sha256:68b7474af3e7124666b8e191805db5a7255d14e6187e0472481c845b6062e42e", size = 87737, upload-time = "2026-03-04T22:30:41.594Z" },
-]
-
-[[package]]
-name = "aiofiles"
-version = "25.1.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668, upload-time = "2025-10-09T20:51:03.174Z" },
-]
-
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -199,15 +172,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/98/3b/40a68de458904bcc143622015fff2352b6461cd92fd66d3527bf1c6f5716/aiohttp_cors-0.8.1-py3-none-any.whl", hash = "sha256:3180cf304c5c712d626b9162b195b1db7ddf976a2a25172b35bb2448b890a80d", size = 25231, upload-time = "2025-03-31T14:16:18.478Z" },
 ]
 
-[[package]]
-name = "aioitertools"
-version = "0.13.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/fd/3c/53c4a17a05fb9ea2313ee1777ff53f5e001aefd5cc85aa2f4c2d982e1e38/aioitertools-0.13.0.tar.gz", hash = "sha256:620bd241acc0bbb9ec819f1ab215866871b4bbd1f73836a55f799200ee86950c", size = 19322, upload-time = "2025-11-06T22:17:07.609Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/10/a1/510b0a7fadc6f43a6ce50152e69dbd86415240835868bb0bd9b5b88b1e06/aioitertools-0.13.0-py3-none-any.whl", hash = "sha256:0be0292b856f08dfac90e31f4739432f4cb6d7520ab9eb73e143f4f2fa5259be", size = 24182, upload-time = "2025-11-06T22:17:06.502Z" },
-]
-
 [[package]]
 name = "aiosignal"
 version = "1.4.0"
@@ -255,18 +219,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/8e/64/013409c451a44b61310fb757af4527f3de57fc98a00f40448de28b864290/albumentations-2.0.8-py3-none-any.whl", hash = "sha256:c4c4259aaf04a7386ad85c7fdcb73c6c7146ca3057446b745cc035805acb1017", size = 369423, upload-time = "2025-05-27T21:23:15.609Z" },
 ]
 
-[[package]]
-name = "amqp"
-version = "5.3.1"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "vine" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/79/fc/ec94a357dfc6683d8c86f8b4cfa5416a4c36b28052ec8260c77aca96a443/amqp-5.3.1.tar.gz", hash = "sha256:cddc00c725449522023bad949f70fff7b48f0b1ade74d170a6f10ab044739432", size = 129013, upload-time = "2024-11-12T19:55:44.051Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/26/99/fc813cd978842c26c82534010ea849eee9ab3a13ea2b74e95cb9c99e747b/amqp-5.3.1-py3-none-any.whl", hash = "sha256:43b3319e1b4e7d1251833a93d672b4af1e40f3d632d479b98661a95f117880a2", size = 50944, upload-time = "2024-11-12T19:55:41.782Z" },
-]
-
 [[package]]
 name = "annotated-doc"
 version = "0.0.4"
@@ -336,49 +288,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c4/09/5e2877c635edc8ac83caa106a6e78bd4816cbc2e52e1daea652c1fe956cf/apache_tvm_ffi-0.1.10-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ac03c04145d9c248992e6f2ec2392a6914966a416eeeeaa729393f40b047be42", size = 2602517, upload-time = "2026-04-07T19:58:30.35Z" },
 ]
 
-[[package]]
-name = "argon2-cffi"
-version = "25.1.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "argon2-cffi-bindings" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/0e/89/ce5af8a7d472a67cc819d5d998aa8c82c5d860608c4db9f46f1162d7dab9/argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1", size = 45706, upload-time = "2025-06-03T06:55:32.073Z" }
-wheels = [
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-]
-
-[[package]]
-name = "argon2-cffi-bindings"
-version = "25.1.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "cffi" },
-]
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+    { name = "neo4j" },
+    { name = "nvidia-ml-py" },
+    { name = "open-clip-torch" },
     { name = "scikit-learn" },
     { name = "scipy" },
     { name = "soundfile" },
     { name = "timm" },
     { name = "tokenizers" },
-    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.9.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform != 'darwin'" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torch", version = "2.9.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
     { name = "torchvision", version = "0.24.1", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "python_full_version < '3.15' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'" },
-    { name = "torchvision", version = "0.24.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
-    { name = "torchvision", version = "0.24.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "(python_full_version >= '3.15' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
-    { name = "tqdm" },
+    { name = "torchvision", version = "0.24.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torchvision", version = "0.24.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "(python_full_version >= '3.15' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux') or sys_platform == 'win32'" },
     { name = "transformers" },
-    { name = "typer" },
-    { name = "universal-pathlib" },
-    { name = "urllib3" },
-    { name = "uvicorn", extra = ["standard"] },
     { name = "vllm", version = "0.16.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'" },
     { name = "vllm", version = "0.16.0+cu130", source = { url = "https://github.com/vllm-project/vllm/releases/download/v0.16.0/vllm-0.16.0+cu130-cp38-abi3-manylinux_2_35_aarch64.whl" }, marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
     { name = "vllm", version = "0.16.0+cu130", source = { url = "https://github.com/vllm-project/vllm/releases/download/v0.16.0/vllm-0.16.0+cu130-cp38-abi3-manylinux_2_35_x86_64.whl" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
 ]
-
-[package.optional-dependencies]
+benchmarks = [
+    { name = "datasets" },
+    { name = "open-clip-torch" },
+]
 dev = [
     { name = "build" },
     { name = "pytest" },
 ]
-svg = [
+local = [
+    { name = "accelerate" },
+    { name = "addict" },
+    { name = "albumentations" },
+    { name = "easydict" },
+    { name = "einops" },
+    { name = "nemotron-graphic-elements-v1" },
+    { name = "nemotron-ocr", marker = "sys_platform == 'linux'" },
+    { name = "nemotron-page-elements-v3" },
+    { name = "nemotron-table-structure-v1" },
+    { name = "nvidia-ml-py" },
+    { name = "scikit-learn" },
+    { name = "timm" },
+    { name = "tokenizers" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torch", version = "2.9.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
+    { name = "torchvision", version = "0.24.1", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "python_full_version < '3.15' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'" },
+    { name = "torchvision", version = "0.24.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torchvision", version = "0.24.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "(python_full_version >= '3.15' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux') or sys_platform == 'win32'" },
+    { name = "transformers" },
+    { name = "vllm", version = "0.16.0", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'" },
+    { name = "vllm", version = "0.16.0+cu130", source = { url = "https://github.com/vllm-project/vllm/releases/download/v0.16.0/vllm-0.16.0+cu130-cp38-abi3-manylinux_2_35_aarch64.whl" }, marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" },
+    { name = "vllm", version = "0.16.0+cu130", source = { url = "https://github.com/vllm-project/vllm/releases/download/v0.16.0/vllm-0.16.0+cu130-cp38-abi3-manylinux_2_35_x86_64.whl" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
+]
+multimedia = [
     { name = "cairosvg" },
+    { name = "scipy" },
+    { name = "soundfile" },
+]
+stores = [
+    { name = "duckdb" },
+    { name = "duckdb-engine" },
+    { name = "neo4j" },
 ]
 
 [package.metadata]
 requires-dist = [
-    { name = "accelerate", specifier = "==1.12.0" },
-    { name = "accelerate", specifier = ">=1.1.0" },
-    { name = "addict" },
-    { name = "albumentations", specifier = "==2.0.8" },
+    { name = "accelerate", marker = "extra == 'local'", specifier = "==1.12.0" },
+    { name = "addict", marker = "extra == 'local'" },
+    { name = "albumentations", marker = "extra == 'local'", specifier = "==2.0.8" },
     { name = "build", marker = "extra == 'dev'", specifier = ">=1.2.2" },
-    { name = "cairosvg", marker = "extra == 'svg'", specifier = ">=2.7.0" },
-    { name = "datasets", specifier = ">=4.8.2" },
+    { name = "cairosvg", marker = "extra == 'multimedia'", specifier = ">=2.7.0" },
+    { name = "datasets", marker = "extra == 'benchmarks'", specifier = ">=4.8.2" },
     { name = "debugpy", specifier = ">=1.8.0" },
-    { name = "duckdb", specifier = ">=1.2.0" },
-    { name = "duckdb-engine", specifier = ">=0.13.0" },
-    { name = "easydict" },
-    { name = "einops" },
+    { name = "duckdb", marker = "extra == 'stores'", specifier = ">=1.2.0" },
+    { name = "duckdb-engine", marker = "extra == 'stores'", specifier = ">=0.13.0" },
+    { name = "easydict", marker = "extra == 'local'" },
+    { name = "einops", marker = "extra == 'local'" },
     { name = "fastapi", specifier = ">=0.114.0" },
     { name = "httpx", specifier = ">=0.27.0" },
     { name = "lancedb" },
     { name = "langchain-nvidia-ai-endpoints", specifier = ">=0.3.0" },
     { name = "markitdown" },
-    { name = "nemotron-graphic-elements-v1", specifier = ">=0.dev0", index = "https://test.pypi.org/simple/" },
-    { name = "nemotron-ocr", marker = "sys_platform == 'linux'", specifier = ">=0.dev0", index = "https://test.pypi.org/simple/" },
-    { name = "nemotron-page-elements-v3", specifier = ">=0.dev0", index = "https://test.pypi.org/simple/" },
-    { name = "nemotron-table-structure-v1", specifier = ">=0.dev0", index = "https://test.pypi.org/simple/" },
-    { name = "neo4j", specifier = ">=5.0" },
+    { name = "nemo-retriever", extras = ["benchmarks", "local", "multimedia", "stores"], marker = "extra == 'all'" },
+    { name = "nemotron-graphic-elements-v1", marker = "extra == 'local'", specifier = ">=0.dev0", index = "https://test.pypi.org/simple/" },
+    { name = "nemotron-ocr", marker = "sys_platform == 'linux' and extra == 'local'", specifier = ">=0.dev0", index = "https://test.pypi.org/simple/" },
+    { name = "nemotron-page-elements-v3", marker = "extra == 'local'", specifier = ">=0.dev0", index = "https://test.pypi.org/simple/" },
+    { name = "nemotron-table-structure-v1", marker = "extra == 'local'", specifier = ">=0.dev0", index = "https://test.pypi.org/simple/" },
+    { name = "neo4j", marker = "extra == 'stores'", specifier = ">=5.0" },
     { name = "nltk", specifier = "==3.9.3" },
     { name = "numpy", specifier = ">=1.26.0" },
-    { name = "nv-ingest", editable = "../src" },
     { name = "nv-ingest-api", editable = "../api" },
     { name = "nv-ingest-client", editable = "../client" },
-    { name = "nvidia-ml-py" },
-    { name = "open-clip-torch", specifier = "==3.2.0" },
+    { name = "nvidia-ml-py", marker = "extra == 'local'" },
+    { name = "open-clip-torch", marker = "extra == 'benchmarks'", specifier = "==3.2.0" },
     { name = "pandas", specifier = ">=2.0,<3" },
     { name = "pillow", specifier = "==12.1.1" },
     { name = "pydantic", specifier = ">=2.8.0" },
@@ -3626,27 +2783,26 @@ requires-dist = [
     { name = "ray", extras = ["data", "serve"], specifier = ">=2.49.0" },
     { name = "requests", specifier = ">=2.32.5" },
     { name = "rich", specifier = ">=13.7.0" },
-    { name = "scikit-learn", specifier = ">=1.6.0" },
-    { name = "scipy", specifier = ">=1.11.0" },
-    { name = "soundfile", specifier = ">=0.12.0" },
-    { name = "timm", specifier = "==1.0.22" },
-    { name = "tokenizers", specifier = ">=0.20.3" },
-    { name = "torch", marker = "sys_platform != 'darwin'", specifier = "~=2.9.1", index = "https://download.pytorch.org/whl/cu130" },
-    { name = "torch", marker = "sys_platform == 'darwin'", specifier = "~=2.9.1" },
-    { name = "torchvision", marker = "sys_platform != 'darwin'", specifier = ">=0.24,<0.25", index = "https://download.pytorch.org/whl/cu130" },
-    { name = "torchvision", marker = "sys_platform == 'darwin'", specifier = ">=0.24,<0.25" },
-    { name = "tqdm" },
+    { name = "scikit-learn", marker = "extra == 'local'", specifier = ">=1.6.0" },
+    { name = "scipy", marker = "extra == 'multimedia'", specifier = ">=1.11.0" },
+    { name = "soundfile", marker = "extra == 'multimedia'", specifier = ">=0.12.0" },
+    { name = "timm", marker = "extra == 'local'", specifier = "==1.0.22" },
+    { name = "tokenizers", marker = "extra == 'local'", specifier = ">=0.20.3" },
+    { name = "torch", marker = "(sys_platform == 'linux' and extra == 'local') or (sys_platform == 'win32' and extra == 'local')", specifier = "~=2.9.1", index = "https://download.pytorch.org/whl/cu130" },
+    { name = "torch", marker = "sys_platform != 'linux' and sys_platform != 'win32' and extra == 'local'", specifier = "~=2.9.1" },
+    { name = "torchvision", marker = "(sys_platform == 'linux' and extra == 'local') or (sys_platform == 'win32' and extra == 'local')", specifier = ">=0.24,<0.25", index = "https://download.pytorch.org/whl/cu130" },
+    { name = "torchvision", marker = "sys_platform != 'linux' and sys_platform != 'win32' and extra == 'local'", specifier = ">=0.24,<0.25" },
     { name = "tqdm", specifier = ">=4.66.0" },
-    { name = "transformers", specifier = ">=4.57.6,<5" },
+    { name = "transformers", marker = "extra == 'local'", specifier = ">=4.57.6,<5" },
     { name = "typer", specifier = ">=0.12.0" },
     { name = "universal-pathlib", specifier = ">=0.2.0" },
     { name = "urllib3", specifier = "==2.6.3" },
     { name = "uvicorn", extras = ["standard"], specifier = ">=0.30.0" },
-    { name = "vllm", marker = "platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'", specifier = "==0.16.0" },
-    { name = "vllm", marker = "platform_machine == 'aarch64' and sys_platform == 'linux'", url = "https://github.com/vllm-project/vllm/releases/download/v0.16.0/vllm-0.16.0+cu130-cp38-abi3-manylinux_2_35_aarch64.whl" },
-    { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'", url = "https://github.com/vllm-project/vllm/releases/download/v0.16.0/vllm-0.16.0+cu130-cp38-abi3-manylinux_2_35_x86_64.whl" },
+    { name = "vllm", marker = "platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'local'", url = "https://github.com/vllm-project/vllm/releases/download/v0.16.0/vllm-0.16.0+cu130-cp38-abi3-manylinux_2_35_aarch64.whl" },
+    { name = "vllm", marker = "platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux' and extra == 'local'", specifier = "==0.16.0" },
+    { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'local'", url = "https://github.com/vllm-project/vllm/releases/download/v0.16.0/vllm-0.16.0+cu130-cp38-abi3-manylinux_2_35_x86_64.whl" },
 ]
-provides-extras = ["svg", "dev"]
+provides-extras = ["local", "multimedia", "stores", "benchmarks", "dev", "all"]
 
 [[package]]
 name = "nemotron-graphic-elements-v1"
@@ -3659,8 +2815,8 @@ dependencies = [
     { name = "numpy", version = "2.4.2", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux'" },
     { name = "pandas" },
     { name = "pillow" },
-    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.9.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform != 'darwin'" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torch", version = "2.9.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 sdist = { url = "https://test-files.pythonhosted.org/packages/24/73/1a9fbe480faf81bf18748601617c7b99d5921042bf0b960e7813efc27346/nemotron_graphic_elements_v1-1.0.0.dev20260306.tar.gz", hash = "sha256:d363e4f99966bf75c779092dd25a5db4c5797e05ffaf596e9103ecd6b245fca7", size = 33625, upload-time = "2026-03-06T03:59:56.004Z" }
 wheels = [
@@ -3703,11 +2859,11 @@ dependencies = [
     { name = "tabulate" },
     { name = "tensorboard" },
     { name = "thop" },
-    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.9.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform != 'darwin'" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torch", version = "2.9.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
     { name = "torchvision", version = "0.24.1", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "python_full_version < '3.15' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'" },
-    { name = "torchvision", version = "0.24.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
-    { name = "torchvision", version = "0.24.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "(python_full_version >= '3.15' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
+    { name = "torchvision", version = "0.24.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torchvision", version = "0.24.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "(python_full_version >= '3.15' and sys_platform == 'linux') or (platform_machine != 'aarch64' and sys_platform == 'linux') or (platform_python_implementation != 'CPython' and sys_platform == 'linux') or sys_platform == 'win32'" },
     { name = "tqdm" },
 ]
 sdist = { url = "https://test-files.pythonhosted.org/packages/c7/58/a9a0eadafa7b83dd90069cc4c5989f20db1ff261a61774fdd962a0fb6188/nemotron_page_elements_v3-3.0.1.dev20260306.tar.gz", hash = "sha256:b21da2ea3f27fe14f2b5c83b6f39df045afd0686b8f39994e8048f088f72bf36", size = 38302, upload-time = "2026-03-06T04:00:03.733Z" }
@@ -3726,8 +2882,8 @@ dependencies = [
     { name = "numpy", version = "2.4.2", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux'" },
     { name = "pandas" },
     { name = "pillow" },
-    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.9.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform != 'darwin'" },
+    { name = "torch", version = "2.9.1", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'linux' and sys_platform != 'win32'" },
+    { name = "torch", version = "2.9.1+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 sdist = { url = "https://test-files.pythonhosted.org/packages/25/34/d77db1cd3224b233ed860b94b0464ef1cf76253c0ce71ee97fb75556091e/nemotron_table_structure_v1-1.0.0.dev20260306.tar.gz", hash = "sha256:2642c0dd2c53846a05280b28b494ee6a34ff2b64e7b737517d48bfbe06d6d8f8", size = 42110, upload-time = "2026-03-06T04:00:01.478Z" }
 wheels = [
@@ -3800,22 +2956,8 @@ wheels = [
 name = "numba"
 version = "0.61.2"
 source = { registry = "https://pypi.org/simple" }
-resolution-markers = [
-    "python_full_version >= '3.14' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'",
-    "python_full_version >= '3.14' and platform_machine == 'x86_64' and sys_platform == 'linux'",
-    "(python_full_version >= '3.14' and platform_machine == 'aarch64' and platform_python_implementation != 'CPython' and sys_platform == 'linux') or (python_full_version >= '3.15' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux')",
-    "python_full_version == '3.13.*' and platform_machine != 'aarch64' and platform_machine != 'x86_64' and sys_platform == 'linux'",
-    "python_full_version == '3.13.*' and platform_machine == 'x86_64' and sys_platform == 'linux'",
-    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and platform_python_implementation != 'CPython' and sys_platform == 'linux'",
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-    "python_full_version < '3.13' and platform_machine == 'x86_64' and sys_platform == 'linux'",
-    "python_full_version < '3.13' and platform_machine == 'aarch64' and platform_python_implementation != 'CPython' and sys_platform == 'linux'",
-    "python_full_version == '3.14.*' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'",
-    "python_full_version == '3.13.*' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'",
-    "python_full_version < '3.13' and platform_machine == 'aarch64' and platform_python_implementation == 'CPython' and sys_platform == 'linux'",
-]
 dependencies = [
-    { name = "llvmlite", version = "0.44.0", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'linux'" },
+    { name = "llvmlite", marker = "sys_platform == 'linux'" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'linux'" },
 ]
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