feat: add nvidia ingest component

.github/workflows/py_autofix.yml

@@ -14,8 +14,8 @@ jobs:
       - uses: actions/checkout@v4
       - name: "Setup Environment"
         uses: ./.github/actions/setup-uv
-      - run: uv run ruff check --fix-only .
-      - run: uv run ruff format .
+      - run: uv run ruff check --fix-only . --ignore A005
+      - run: uv run ruff format . --config pyproject.toml
       - uses: autofix-ci/action@551dded8c6cc8a1054039c8bc0b8b48c51dfc6ef
       - name: Minimize uv cache
         run: uv cache prune --ci

.github/workflows/style-check-py.yml

@@ -24,6 +24,6 @@ jobs:
       - name: Register problem matcher
         run: echo "::add-matcher::.github/workflows/matchers/ruff.json"
       - name: Run Ruff Check
-        run: uv run --only-dev ruff check --output-format=github .
+        run: uv run --only-dev ruff check --output-format=github . --ignore A005
       - name: Minimize uv cache
         run: uv cache prune --ci

Makefile

@@ -65,7 +65,7 @@ reinstall_backend: ## forces reinstall all dependencies (no caching)
 
 install_backend: ## install the backend dependencies
 	@echo 'Installing backend dependencies'
-	@uv sync --frozen
+	@uv sync --frozen $(EXTRA_ARGS)
 
 install_frontend: ## install the frontend dependencies
 	@echo 'Installing frontend dependencies'
@@ -198,7 +198,7 @@ fix_codespell: ## run codespell to fix spelling errors
 	poetry run codespell --toml pyproject.toml --write
 
 format_backend: ## backend code formatters
-	@uv run ruff check . --fix --ignore EXE002
+	@uv run ruff check . --fix --ignore EXE002 --ignore A005
 	@uv run ruff format . --config pyproject.toml
 
 format_frontend: ## frontend code formatters

docker/build_and_push_ep.Dockerfile

@@ -40,7 +40,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,source=src/backend/base/README.md,target=src/backend/base/README.md \
     --mount=type=bind,source=src/backend/base/uv.lock,target=src/backend/base/uv.lock \
     --mount=type=bind,source=src/backend/base/pyproject.toml,target=src/backend/base/pyproject.toml \
-    uv sync --frozen --no-install-project --no-editable
+    uv sync --frozen --no-install-project --no-editable --extra nv-ingest
 
 COPY ./src /app/src
 
@@ -58,7 +58,7 @@ COPY ./uv.lock /app/uv.lock
 COPY ./README.md /app/README.md
 
 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv sync --frozen --no-editable
+    uv sync --frozen --no-editable --extra nv-ingest
 
 ################################
 # RUNTIME

pyproject.toml

@@ -149,6 +149,14 @@ local = [
 clickhouse-connect = [
     "clickhouse-connect==0.7.19"
 ]
+nv-ingest = [
+    # nv-ingest-client 2025.2.7.dev0 does not correctly install its
+    # dependencies, so we need to install some manually.
+    "nv-ingest-client==2025.2.7.dev0",
+    "python-pptx==0.6.23",
+    "pymilvus[bulk_writer,model]==2.5.0",
+    "llama-index-embeddings-nvidia==0.1.5",
+]
 
 [project.scripts]
 langflow = "langflow.__main__:main"
@@ -276,6 +284,9 @@ external = ["RUF027"]
     "SLF001",
 ]
 
+[tool.ruff.lint.flake8-builtins]
+builtins-allowed-modules = [ "io", "logging", "socket"]
+
 [tool.mypy]
 plugins = ["pydantic.mypy"]
 follow_imports = "skip"

src/backend/base/langflow/components/nvidia/__init__.py

@@ -1,3 +1,4 @@
+from .nvidia_ingest import NvidiaIngestComponent
 from .nvidia_rerank import NvidiaRerankComponent
 
-__all__ = ["NvidiaRerankComponent"]
+__all__ = ["NvidiaIngestComponent", "NvidiaRerankComponent"]

src/backend/base/langflow/components/nvidia/nvidia_ingest.py

@@ -0,0 +1,235 @@
+from pathlib import Path
+from urllib.parse import urlparse
+
+from loguru import logger
+
+from langflow.custom import Component
+from langflow.io import BoolInput, DropdownInput, FileInput, IntInput, MessageTextInput, Output
+from langflow.schema import Data
+
+
+class NvidiaIngestComponent(Component):
+    display_name = "NVIDIA Ingest"
+    description = "Process, transform, and store data."
+    documentation: str = "https://github.com/NVIDIA/nv-ingest/tree/main/docs"
+    icon = "NVIDIA"
+    name = "NVIDIAIngest"
+    beta = True
+
+    try:
+        from nv_ingest_client.util.file_processing.extract import EXTENSION_TO_DOCUMENT_TYPE
+
+        file_types = list(EXTENSION_TO_DOCUMENT_TYPE.keys())
+        supported_file_types_info = f"Supported file types: {', '.join(file_types)}"
+    except ImportError:
+        msg = (
+            "NVIDIA Ingest dependencies missing. "
+            "Please install them using your package manager. (e.g. uv sync --extra nv-ingest)"
+        )
+        logger.warning(msg)
+        file_types = [msg]
+        supported_file_types_info = msg
+
+    inputs = [
+        MessageTextInput(
+            name="base_url",
+            display_name="NVIDIA Ingestion URL",
+            info="The URL of the NVIDIA Ingestion API.",
+        ),
+        FileInput(
+            name="path",
+            display_name="Path",
+            file_types=file_types,
+            info=supported_file_types_info,
+            required=True,
+        ),
+        BoolInput(
+            name="extract_text",
+            display_name="Extract Text",
+            info="Extract text from documents",
+            value=True,
+        ),
+        BoolInput(
+            name="extract_charts",
+            display_name="Extract Charts",
+            info="Extract text from charts",
+            value=False,
+        ),
+        BoolInput(
+            name="extract_tables",
+            display_name="Extract Tables",
+            info="Extract text from tables",
+            value=True,
+        ),
+        DropdownInput(
+            name="text_depth",
+            display_name="Text Depth",
+            info=(
+                "Level at which text is extracted (applies before splitting). "
+                "Support for 'block', 'line', 'span' varies by document type."
+            ),
+            options=["document", "page", "block", "line", "span"],
+            value="document",  # Default value
+            advanced=True,
+        ),
+        BoolInput(
+            name="split_text",
+            display_name="Split Text",
+            info="Split text into smaller chunks",
+            value=True,
+        ),
+        DropdownInput(
+            name="split_by",
+            display_name="Split By",
+            info="How to split into chunks ('size' splits by number of characters)",
+            options=["page", "sentence", "word", "size"],
+            value="word",  # Default value
+            advanced=True,
+        ),
+        IntInput(
+            name="split_length",
+            display_name="Split Length",
+            info="The size of each chunk based on the 'split_by' method",
+            value=200,
+            advanced=True,
+        ),
+        IntInput(
+            name="split_overlap",
+            display_name="Split Overlap",
+            info="Number of segments (as determined by the 'split_by' method) to overlap from previous chunk",
+            value=20,
+            advanced=True,
+        ),
+        IntInput(
+            name="max_character_length",
+            display_name="Max Character Length",
+            info="Maximum number of characters in each chunk",
+            value=1000,
+            advanced=True,
+        ),
+        IntInput(
+            name="sentence_window_size",
+            display_name="Sentence Window Size",
+            info="Number of sentences to include from previous and following chunk (when split_by='sentence')",
+            value=0,
+            advanced=True,
+        ),
+    ]
+
+    outputs = [
+        Output(display_name="Data", name="data", method="load_file"),
+    ]
+
+    def load_file(self) -> list[Data]:
+        try:
+            from nv_ingest_client.client import Ingestor
+        except ImportError as e:
+            msg = (
+                "NVIDIA Ingest dependencies missing. "
+                "Please install them using your package manager. (e.g. uv sync --extra nv-ingest)"
+            )
+            raise ImportError(msg) from e
+
+        self.base_url: str | None = self.base_url.strip() if self.base_url else None
+
+        if not self.path:
+            err_msg = "Upload a file to use this component."
+            self.log(err_msg, name="NVIDIAIngestComponent")
+            raise ValueError(err_msg)
+
+        resolved_path = self.resolve_path(self.path)
+        extension = Path(resolved_path).suffix[1:].lower()
+        if extension not in self.file_types:
+            err_msg = f"Unsupported file type: {extension}"
+            self.log(err_msg, name="NVIDIAIngestComponent")
+            raise ValueError(err_msg)
+
+        try:
+            parsed_url = urlparse(self.base_url)
+            if not parsed_url.hostname or not parsed_url.port:
+                err_msg = "Invalid URL: Missing hostname or port."
+                self.log(err_msg, name="NVIDIAIngestComponent")
+                raise ValueError(err_msg)
+        except Exception as e:
+            self.log(f"Error parsing URL: {e}", name="NVIDIAIngestComponent")
+            raise
+
+        self.log(
+            f"Creating Ingestor for host: {parsed_url.hostname!r}, port: {parsed_url.port!r}",
+            name="NVIDIAIngestComponent",
+        )
+        try:
+            from nv_ingest_client.client import Ingestor
+
+            ingestor = (
+                Ingestor(message_client_hostname=parsed_url.hostname, message_client_port=parsed_url.port)
+                .files(resolved_path)
+                .extract(
+                    extract_text=self.extract_text,
+                    extract_tables=self.extract_tables,
+                    extract_charts=self.extract_charts,
+                    extract_images=False,  # Currently not supported
+                    text_depth=self.text_depth,
+                )
+            )
+        except Exception as e:
+            self.log(f"Error creating Ingestor: {e}", name="NVIDIAIngestComponent")
+            raise
+
+        if self.split_text:
+            ingestor = ingestor.split(
+                split_by=self.split_by,
+                split_length=self.split_length,
+                split_overlap=self.split_overlap,
+                max_character_length=self.max_character_length,
+                sentence_window_size=self.sentence_window_size,
+            )
+
+        try:
+            result = ingestor.ingest()
+        except Exception as e:
+            self.log(f"Error during ingestion: {e}", name="NVIDIAIngestComponent")
+            raise
+
+        self.log(f"Results: {result}", name="NVIDIAIngestComponent")
+
+        data = []
+        document_type_text = "text"
+        document_type_structured = "structured"
+
+        # Result is a list of segments as determined by the text_depth option (if "document" then only one segment)
+        # each segment is a list of elements (text, structured, image)
+        for segment in result:
+            for element in segment:
+                document_type = element.get("document_type")
+                metadata = element.get("metadata", {})
+                source_metadata = metadata.get("source_metadata", {})
+                content_metadata = metadata.get("content_metadata", {})
+
+                if document_type == document_type_text:
+                    data.append(
+                        Data(
+                            text=metadata.get("content", ""),
+                            file_path=source_metadata.get("source_name", ""),
+                            document_type=document_type,
+                            description=content_metadata.get("description", ""),
+                        )
+                    )
+                # Both charts and tables are returned as "structured" document type,
+                # with extracted text in "table_content"
+                elif document_type == document_type_structured:
+                    table_metadata = metadata.get("table_metadata", {})
+                    data.append(
+                        Data(
+                            text=table_metadata.get("table_content", ""),
+                            file_path=source_metadata.get("source_name", ""),
+                            document_type=document_type,
+                            description=content_metadata.get("description", ""),
+                        )
+                    )
+                else:
+                    # image is not yet supported; skip if encountered
+                    self.log(f"Unsupported document type: {document_type}", name="NVIDIAIngestComponent")
+
+        self.status = data if data else "No data"
+        return data