Enable the embedding task for all content types including image capti…

…ons (#336)

client/src/nv_ingest_client/nv_ingest_cli.py

@@ -120,7 +120,7 @@
   --task 'extract:{"document_type":"pdf", "extract_method":"unstructured_io"}'
   --task 'extract:{"document_type":"docx", "extract_text":true, "extract_images":true}'
   --task 'store:{"content_type":"image", "store_method":"minio", "endpoint":"minio:9000"}'
-  --task 'embed:{"text":true, "tables":true}'
+  --task 'embed'
   --task 'vdb_upload'
   --task 'caption:{}'
 
@@ -143,8 +143,6 @@
 - embed: Computes embeddings on multimodal extractions.
     Options:
     - filter_errors (bool): Flag to filter embedding errors. Optional.
-    - tables (bool): Flag to create embeddings for table extractions. Optional.
-    - text (bool): Flag to create embeddings for text extractions. Optional.
 \b
 - extract: Extracts content from documents, customizable per document type.
     Can be specified multiple times for different 'document_type' values.

client/src/nv_ingest_client/primitives/tasks/embed.py

@@ -9,18 +9,30 @@
 import logging
 from typing import Dict
 
-from pydantic import BaseModel
+from pydantic import BaseModel, root_validator
 
 from .task_base import Task
 
 logger = logging.getLogger(__name__)
 
 
 class EmbedTaskSchema(BaseModel):
-    text: bool = True
-    tables: bool = True
     filter_errors: bool = False
 
+    @root_validator(pre=True)
+    def handle_deprecated_fields(cls, values):
+        if "text" in values:
+            logger.warning(
+                "'text' parameter is deprecated and will be ignored. Future versions will remove this argument."
+            )
+            values.pop("text")
+        if "tables" in values:
+            logger.warning(
+                "'tables' parameter is deprecated and will be ignored. Future versions will remove this argument."
+            )
+            values.pop("tables")
+        return values
+
     class Config:
         extra = "forbid"
 
@@ -30,13 +42,22 @@ class EmbedTask(Task):
     Object for document embedding task
     """
 
-    def __init__(self, text: bool = True, tables: bool = True, filter_errors: bool = False) -> None:
+    def __init__(self, text: bool = None, tables: bool = None, filter_errors: bool = False) -> None:
         """
         Setup Embed Task Config
         """
         super().__init__()
-        self._text = text
-        self._tables = tables
+
+        if text is not None:
+            logger.warning(
+                "'text' parameter is deprecated and will be ignored. Future versions will remove this argument."
+            )
+
+        if tables is not None:
+            logger.warning(
+                "'tables' parameter is deprecated and will be ignored. Future versions will remove this argument."
+            )
+
         self._filter_errors = filter_errors
 
     def __str__(self) -> str:
@@ -45,8 +66,6 @@ def __str__(self) -> str:
         """
         info = ""
         info += "Embed Task:\n"
-        info += f"  text: {self._text}\n"
-        info += f"  tables: {self._tables}\n"
         info += f"  filter_errors: {self._filter_errors}\n"
         return info
 
@@ -56,8 +75,6 @@ def to_dict(self) -> Dict:
         """
 
         task_properties = {
-            "text": self._text,
-            "tables": self._tables,
             "filter_errors": False,
         }
 

src/nv_ingest/modules/transforms/embed_extractions.py

@@ -281,22 +281,54 @@ def _add_embeddings(row, embeddings, info_msgs):
     return row
 
 
-def _get_text_content(row):
+def _get_pandas_text_content(row):
     """
     A pandas UDF used to select extracted text content to be used to create embeddings.
     """
 
     return row["content"]
 
 
-def _get_table_content(row):
+def _get_pandas_table_content(row):
     """
     A pandas UDF used to select extracted table/chart content to be used to create embeddings.
     """
 
     return row["table_metadata"]["table_content"]
 
 
+def _get_pandas_image_content(row):
+    """
+    A pandas UDF used to select extracted image captions to be used to create embeddings.
+    """
+
+    return row["image_metadata"]["caption"]
+
+
+def _get_cudf_text_content(df: cudf.DataFrame):
+    """
+    A cuDF UDF used to select extracted text content to be used to create embeddings.
+    """
+
+    return df.struct.field("content")
+
+
+def _get_cudf_table_content(df: cudf.DataFrame):
+    """
+    A cuDF UDF used to select extracted table/chart content to be used to create embeddings.
+    """
+
+    return df.struct.field("table_metadata").struct.field("table_content")
+
+
+def _get_cudf_image_content(df: cudf.DataFrame):
+    """
+    A cuDF UDF used to select extracted image captions to be used to create embeddings.
+    """
+
+    return df.struct.field("image_metadata").struct.field("caption")
+
+
 def _batch_generator(iterable: Iterable, batch_size=10):
     """
     A generator to yield batches of size `batch_size` from an interable.
@@ -349,7 +381,6 @@ def _generate_batches(prompts: List[str], batch_size: int = 100):
 
 def _generate_embeddings(
     ctrl_msg: ControlMessage,
-    content_type: ContentTypeEnum,
     event_loop: asyncio.SelectorEventLoop,
     batch_size: int,
     api_key: str,
@@ -361,8 +392,10 @@ def _generate_embeddings(
     filter_errors: bool,
 ):
     """
-    A function to generate embeddings for the supplied `ContentTypeEnum`. The `ContentTypeEnum` will
-    drive filtering criteria used to select rows of data to enrich with embeddings.
+    A function to generate text embeddings for supported content types (TEXT, STRUCTURED, IMAGE).
+
+    This function dynamically selects the appropriate metadata field based on content type and
+    calculates embeddings using the NIM embedding service. AUDIO and VIDEO types are stubbed and skipped.
 
     Parameters
     ----------
@@ -403,53 +436,71 @@ def _generate_embeddings(
         content_mask : cudf.Series
             A boolean mask representing rows filtered to calculate embeddings.
     """
+    cudf_content_extractor = {
+        ContentTypeEnum.TEXT: _get_cudf_text_content,
+        ContentTypeEnum.STRUCTURED: _get_cudf_table_content,
+        ContentTypeEnum.IMAGE: _get_cudf_image_content,
+        ContentTypeEnum.AUDIO: lambda _: None,  # Not supported yet.
+        ContentTypeEnum.VIDEO: lambda _: None,  # Not supported yet.
+    }
+    pandas_content_extractor = {
+        ContentTypeEnum.TEXT: _get_pandas_text_content,
+        ContentTypeEnum.STRUCTURED: _get_pandas_table_content,
+        ContentTypeEnum.IMAGE: _get_pandas_image_content,
+        ContentTypeEnum.AUDIO: lambda _: None,  # Not supported yet.
+        ContentTypeEnum.VIDEO: lambda _: None,  # Not supported yet.
+    }
+
+    logger.debug("Generating text embeddings for supported content types: TEXT, STRUCTURED, IMAGE.")
+
+    embedding_dataframes = []
+    content_masks = []
 
     with ctrl_msg.payload().mutable_dataframe() as mdf:
         if mdf.empty:
-            return None, None
-
-        # generate table text mask
-        if content_type == ContentTypeEnum.TEXT:
-            content_mask = (mdf["document_type"] == content_type.value) & (
-                mdf["metadata"].struct.field("content") != ""
-            ).fillna(False)
-            content_getter = _get_text_content
-        elif content_type == ContentTypeEnum.STRUCTURED:
-            table_mask = mdf["document_type"] == content_type.value
-            if not table_mask.any():
-                return None, None
-            content_mask = table_mask & (
-                mdf["metadata"].struct.field("table_metadata").struct.field("table_content") != ""
-            ).fillna(False)
-            content_getter = _get_table_content
-
-        # exit if matches found
-        if not content_mask.any():
-            return None, None
-
-        df_text = mdf.loc[content_mask].to_pandas().reset_index(drop=True)
-        # get text list
-        filtered_text = df_text["metadata"].apply(content_getter)
-        # calculate embeddings
-        filtered_text_batches = _generate_batches(filtered_text.tolist(), batch_size)
-        text_embeddings = _async_runner(
-            filtered_text_batches,
-            api_key,
-            embedding_nim_endpoint,
-            embedding_model,
-            encoding_format,
-            input_type,
-            truncate,
-            event_loop,
-            filter_errors,
-        )
-        # update embeddings in metadata
-        df_text[["metadata", "document_type", "_contains_embeddings"]] = df_text.apply(
-            _add_embeddings, **text_embeddings, axis=1
-        )[["metadata", "document_type", "_contains_embeddings"]]
-        df_text["_content"] = filtered_text
+            return ctrl_msg
+
+        for content_type, content_getter in pandas_content_extractor.items():
+            if not content_getter:
+                logger.debug(f"Skipping unsupported content type: {content_type}")
+                continue
+
+            content_mask = mdf["document_type"] == content_type.value
+            if not content_mask.any():
+                continue
+
+            cudf_content_getter = cudf_content_extractor[content_type]
+            content_mask = (content_mask & (cudf_content_getter(mdf["metadata"]) != "")).fillna(False)
+            if not content_mask.any():
+                continue
+
+            df_content = mdf.loc[content_mask].to_pandas().reset_index(drop=True)
+            filtered_content = df_content["metadata"].apply(content_getter)
+            # calculate embeddings
+            filtered_content_batches = _generate_batches(filtered_content.tolist(), batch_size)
+            content_embeddings = _async_runner(
+                filtered_content_batches,
+                api_key,
+                embedding_nim_endpoint,
+                embedding_model,
+                encoding_format,
+                input_type,
+                truncate,
+                event_loop,
+                filter_errors,
+            )
+            # update embeddings in metadata
+            df_content[["metadata", "document_type", "_contains_embeddings"]] = df_content.apply(
+                _add_embeddings, **content_embeddings, axis=1
+            )[["metadata", "document_type", "_contains_embeddings"]]
+            df_content["_content"] = filtered_content
+
+            embedding_dataframes.append(df_content)
+            content_masks.append(content_mask)
+
+    message = _concatenate_extractions(ctrl_msg, embedding_dataframes, content_masks)
 
-    return df_text, content_mask
+    return message
 
 
 def _concatenate_extractions(ctrl_msg: ControlMessage, dataframes: List[pd.DataFrame], masks: List[cudf.Series]):
@@ -493,8 +544,8 @@ def _concatenate_extractions(ctrl_msg: ControlMessage, dataframes: List[pd.DataF
 @register_module(MODULE_NAME, MODULE_NAMESPACE)
 def _embed_extractions(builder: mrc.Builder):
     """
-    A pipeline module that receives incoming messages in ControlMessage format and calculates embeddings for
-    supported document types.
+    A pipeline module that receives incoming messages in ControlMessage format
+    and calculates text embeddings for all supported content types.
 
     Parameters
     ----------
@@ -519,56 +570,20 @@ def embed_extractions_fn(message: ControlMessage):
         try:
             task_props = message.remove_task("embed")
             model_dump = task_props.model_dump()
-            embed_text = model_dump.get("text")
-            embed_tables = model_dump.get("tables")
             filter_errors = model_dump.get("filter_errors", False)
 
-            logger.debug(f"Generating embeddings: text={embed_text}, tables={embed_tables}")
-            embedding_dataframes = []
-            content_masks = []
-
-            if embed_text:
-                df_text, content_mask = _generate_embeddings(
-                    message,
-                    ContentTypeEnum.TEXT,
-                    event_loop,
-                    validated_config.batch_size,
-                    validated_config.api_key,
-                    validated_config.embedding_nim_endpoint,
-                    validated_config.embedding_model,
-                    validated_config.encoding_format,
-                    validated_config.input_type,
-                    validated_config.truncate,
-                    filter_errors,
-                )
-                if df_text is not None:
-                    embedding_dataframes.append(df_text)
-                    content_masks.append(content_mask)
-
-            if embed_tables:
-                df_tables, table_mask = _generate_embeddings(
-                    message,
-                    ContentTypeEnum.STRUCTURED,
-                    event_loop,
-                    validated_config.batch_size,
-                    validated_config.api_key,
-                    validated_config.embedding_nim_endpoint,
-                    validated_config.embedding_model,
-                    validated_config.encoding_format,
-                    validated_config.input_type,
-                    validated_config.truncate,
-                    filter_errors,
-                )
-                if df_tables is not None:
-                    embedding_dataframes.append(df_tables)
-                    content_masks.append(table_mask)
-
-            if len(content_masks) == 0:
-                return message
-
-            message = _concatenate_extractions(message, embedding_dataframes, content_masks)
-
-            return message
+            return _generate_embeddings(
+                message,
+                event_loop,
+                validated_config.batch_size,
+                validated_config.api_key,
+                validated_config.embedding_nim_endpoint,
+                validated_config.embedding_model,
+                validated_config.encoding_format,
+                validated_config.input_type,
+                validated_config.truncate,
+                filter_errors,
+            )
 
         except Exception as e:
             traceback.print_exc()

src/nv_ingest/schemas/ingest_job_schema.py

@@ -130,8 +130,6 @@ class IngestTaskDedupSchema(BaseModelNoExt):
 
 
 class IngestTaskEmbedSchema(BaseModelNoExt):
-    text: bool = True
-    tables: bool = True
     filter_errors: bool = False
 
 

src/nv_ingest/schemas/metadata_schema.py

@@ -36,12 +36,14 @@ class AccessLevelEnum(int, Enum):
 
 
 class ContentTypeEnum(str, Enum):
-    TEXT = "text"
+    AUDIO = "audio"
+    EMBEDDING = "embedding"
     IMAGE = "image"
+    INFO_MSG = "info_message"
     STRUCTURED = "structured"
+    TEXT = "text"
     UNSTRUCTURED = "unstructured"
-    INFO_MSG = "info_message"
-    EMBEDDING = "embedding"
+    VIDEO = "video"
 
 
 class StdContentDescEnum(str, Enum):