How to Handle Video and Image Data in Unstructured AI Pipelines

Processing visual data at scale requires infrastructure that treats multimodal content as native data types. Traditional data pipelines break when you process images embedded in PDFs, video transcripts with temporal alignment, or batch inference across thousands of visual assets.

This guide shows you how to build production-grade pipelines for video and image data using Fenic, Typedef's open-source DataFrame framework designed for AI workloads. Get started at https://github.com/typedef-ai/fenic

Why Visual Data Processing Fails in Traditional Pipelines

Your highest-value business data exists in formats that standard ETL cannot handle:

Customer support tickets with embedded screenshots
Product documentation containing diagrams and charts
Sales call recordings with both audio and shared visuals
Legal contracts with signatures and financial charts

The statistics tell the story. According to https://typedef.ai/resources/unstructured-data-management-statistics, 80% of enterprise data exists in unstructured formats, with images and video representing a significant portion. Yet most data teams lack infrastructure to process visual content without stitching together OCR models, computer vision APIs, and custom preprocessing scripts.

The result: brittle pipelines with multiple failure points, unpredictable costs, and maintenance overhead that scales with data volume.

Multimodal AI Architecture Basics

Modern multimodal AI systems process text, images, audio, and video through unified architectures. Rather than separate specialized models, these systems use:

Specialized encoders for each modality (vision transformers for images, audio encoders for video)
Fusion modules that align and combine modality-specific features
Output layers that translate fused representations into structured data

Market data from https://typedef.ai/resources/multimodal-ai-engine-stats shows the multimodal AI market expanding from $2.36 billion to $93.99 billion by 2035, reflecting enterprise recognition that unified processing delivers better results than piecemeal approaches.

Processing PDFs with Embedded Images

Documents often contain visual information that text extraction alone misses. Fenic's parse_pdf function handles both text and visual content through native multimodal model support.

python
import fenic as fc

# Configure Gemini for native PDF processing
session = fc.Session.get_or_create(
    fc.SessionConfig(
        app_name="document_pipeline",
        semantic=fc.SemanticConfig(
            language_models={
                "gemini": fc.GoogleDeveloperLanguageModel(
                    model_name="gemini-2.0-flash",
                    rpm=100,
                    tpm=1000,
                )
            },
            default_language_model="gemini",
        ),
    )
)

# Discover and parse PDFs with image descriptions
pdfs = session.read.pdf_metadata("data/docs/**/*.pdf", recursive=True)

markdown = pdfs.select(
    fc.col("file_path"),
    fc.semantic.parse_pdf(
        fc.col("file_path"),
        page_separator="--- PAGE {page} ---",
        describe_images=True,
    ).alias("markdown"),
)

The describe_images=True parameter instructs the model to generate descriptions of images, diagrams, and charts. This converts visual information into text that downstream systems can process, search, and analyze.

Metadata-Driven Document Discovery

Before processing visual content at scale, you need visibility into your document corpus. The pdf_metadata reader provides instant access to file characteristics without parsing overhead.

python
# Load metadata for all PDFs
pdf_inventory = session.read.pdf_metadata(
    "reports/**/*.pdf",
    recursive=True
)

# Filter based on visual content density
image_heavy_docs = pdf_inventory.filter(
    (fc.col("image_count") > 10) &
    (fc.col("page_count") < 50)
)

# Route documents based on characteristics
priority_docs = pdf_inventory.filter(
    fc.col("has_signature_fields") |
    (fc.col("image_count") > 5)
)

Metadata fields include:

image_count - Total images in the PDF
page_count - Number of pages
file_size - Size in bytes
is_encrypted - Encryption status
has_signature_fields - Presence of signature fields
has_forms - Contains form fields
creation_date and mod_date - Timestamps

This enables intelligent routing where image-heavy documents get processed with vision-enabled models while text-only documents use faster, cheaper alternatives.

Processing Video Content Through Transcripts

Video contains two information streams: visual content and audio. For many business use cases, audio transcripts provide the primary value, but temporal alignment with visual elements requires specialized data types.

Fenic's TranscriptType handles three formats:

SRT (SubRip) - Indexed entries with timestamp ranges
WebVTT (Web Video Text Tracks) - Speaker names and timestamps
Generic - Conversation transcript format

All formats parse into a unified schema with speaker identification, timestamps, and content.

python
from fenic.core.types import TranscriptType

# Load video transcripts
transcripts = session.read.docs(
    "meetings/**/*.srt",
    content_type="markdown",
    recursive=True
)

# Extract structured information from timed segments
class MeetingAction(BaseModel):
    assigned_to: str
    task: str
    deadline: Optional[str]

actions = transcripts.select(
    fc.col("speaker"),
    fc.col("start_time"),
    fc.semantic.extract(
        fc.col("content"),
        response_format=MeetingAction
    ).alias("action")
).filter(
    fc.col("action.assigned_to").is_not_null()
)

The temporal information enables:

Identifying when specific topics arose
Tracking speaker contributions
Correlating transcript segments with visual slides or screen shares

Extracting Structure from Visual Content

Once images and video are represented as text (through OCR, transcripts, or model-generated descriptions), semantic operators transform unstructured content into structured data.

The semantic.extract operator uses Pydantic schemas to define extraction targets:

python
from pydantic import BaseModel, Field
from typing import List, Literal

class ProductFeature(BaseModel):
    name: str = Field(description="Feature name from the slide")
    category: Literal["performance", "usability", "cost"]
    description: str

class PresentationSlide(BaseModel):
    title: str
    main_topic: str
    features: List[ProductFeature]
    has_diagram: bool = Field(description="Whether slide contains diagram")

# Process presentation with visual elements
slides = session.read.pdf_metadata("presentations/**/*.pdf")

structured_slides = slides.select(
    fc.col("file_path"),
    fc.semantic.parse_pdf(
        fc.col("file_path"),
        describe_images=True
    ).alias("content")
).select(
    fc.semantic.extract(
        "content",
        response_format=PresentationSlide
    ).alias("slide_data")
).unnest("slide_data")

# Filter to high-value content
priority_slides = structured_slides.filter(
    (fc.col("has_diagram") == True) &
    (fc.col("main_topic").contains("roadmap"))
)

This pattern works for:

Invoice processing - Extracting line items from scanned documents
Form analysis - Pulling structured data from PDFs
Contract parsing - Extracting terms, dates, and parties

Learn more about semantic operators at https://typedef.ai/resources/build-reliable-ai-pipelines-fenic-semantic-operators

Optimizing Multimodal Inference at Scale

Processing thousands of images or hours of video requires careful optimization. Fenic provides several mechanisms to control costs and latency.

Intelligent Batching and Rate Limiting

The framework automatically groups API calls to minimize latency and respect provider limits:

python
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "vision_model": fc.OpenAILanguageModel(
                model_name="gpt-4o",
                rpm=500,      # Requests per minute
                tpm=200_000   # Tokens per minute
            )
        }
    )
)

Self-throttling mechanisms adjust request rates based on provider responses. The framework handles retries for transient failures and provides logging for debugging production issues.

Deduplication Before Processing

Visual content often contains redundancy. Product images appear in multiple documents. Meeting slides get reused across presentations. Standard disclaimers appear on every contract page.

Deduplicate before expensive model calls:

python
# Extract unique visual elements first
unique_images = (
    pdfs
    .select(fc.semantic.parse_pdf(fc.col("path"), describe_images=True))
    .filter(fc.col("markdown").contains("!["))  # Has images
    .distinct()  # Remove duplicates
)

# Process only unique content
processed = unique_images.select(
    fc.semantic.extract("markdown", ImageMetadata)
)

Fenic's caching system stores results at any pipeline step:

python
# Cache parsed PDFs to avoid re-processing
parsed_cache = session.table("parsed_pdfs")

if not parsed_cache.exists():
    parsed = pdfs.select(
        fc.semantic.parse_pdf(fc.col("path"), describe_images=True)
    )
    parsed.write.table("parsed_pdfs")
else:
    parsed = parsed_cache

Provider Selection for Cost-Performance Tradeoffs

Different models offer varying capabilities at different price points. Route workloads based on task requirements:

python
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "fast": fc.GoogleDeveloperLanguageModel(
                model_name="gemini-2.0-flash-lite",
                rpm=1000,
                tpm=1_000_000
            ),
            "accurate": fc.OpenAILanguageModel(
                model_name="gpt-4o",
                rpm=500,
                tpm=200_000
            )
        },
        default_language_model="fast"
    )
)

# Use fast model for simple extraction
simple = df.select(
    fc.semantic.extract("content", SimpleSchema, model_alias="fast")
)

# Use accurate model for visual content analysis
complex = df.select(
    fc.semantic.extract("content", ComplexSchema, model_alias="accurate")
)

Details on multi-provider support: https://typedef.ai/blog/fenic-0-4-0-released-declarative-tools-mcp-and-huggingface-plus-major-dx-and-reliability-gains

Semantic Joins Across Modalities

Traditional joins require exact matches. Visual content needs semantic matching based on meaning rather than keywords.

python
# Match product images to catalog descriptions
images = session.read.pdf_metadata("product_photos/**/*.pdf")
descriptions = session.read.csv("catalog.csv")

matched = images.semantic.join(
    other=descriptions,
    predicate="""
    Does the image content match this product description?
    Image: {{left_on}}
    Description: {{right_on}}
    """,
    left_on=fc.semantic.parse_pdf(fc.col("file_path"), describe_images=True),
    right_on=fc.col("product_description")
)

Use cases:

Matching screenshots to bug reports
Associating diagrams with documentation sections
Linking video segments to presentation slides

Multi-Stage Visual Processing

Visual processing often requires multiple passes. First pass extracts basic structure, second pass enriches with domain knowledge.

python
# Stage 1: Extract basic visual elements
basic = pdfs.select(
    fc.col("file_path"),
    fc.semantic.parse_pdf(fc.col("file_path"), describe_images=True)
).select(
    fc.col("file_path"),
    fc.semantic.extract("markdown", BasicVisualElements)
).unnest("basic_elements")

# Stage 2: Classify and enrich
enriched = basic.select(
    fc.semantic.classify(
        fc.col("description"),
        classes=["product", "diagram", "screenshot", "chart"],
        examples=domain_examples
    ).alias("category"),
    fc.semantic.map(
        "Generate technical description: {{description}}",
        description=fc.col("description")
    ).alias("technical_desc")
)

Staging enables using faster models for extraction and reserving expensive models for domain-specific analysis.

Handling Failed Extractions

Visual content varies in quality. Scanned documents have artifacts. Images lack contrast. Transcripts contain crosstalk. Production pipelines need graceful degradation.

python
# Track processing coverage
processed = pdfs.select(
    fc.col("file_path"),
    fc.semantic.parse_pdf(fc.col("file_path"), describe_images=True)
).select(
    fc.col("file_path"),
    fc.semantic.extract("markdown", Schema).alias("extracted")
)

# Separate successful from failed extractions
successful = processed.filter(fc.col("extracted").is_not_null())
failed = processed.filter(fc.col("extracted").is_null())

# Route failures for manual review or retry
failed.select(
    fc.col("file_path"),
    fc.lit("extraction_failed").alias("status")
).write.csv("review_queue.csv")

Track coverage metrics through Fenic's metrics system to identify systematic failures requiring prompt adjustment or model changes.

Local Development to Cloud Scaling

Fenic enables full local development. Build and test complete pipelines on your laptop, then deploy to production without code changes.

python
# Development configuration
dev_config = fc.SessionConfig(
    app_name="visual_pipeline",
    semantic=fc.SemanticConfig(
        language_models={
            "dev": fc.OpenAILanguageModel(
                model_name="gpt-4o-mini",
                rpm=10,
                tpm=10_000
            )
        }
    )
)

# Production configuration with cloud scaling
prod_config = fc.SessionConfig(
    app_name="visual_pipeline",
    semantic=fc.SemanticConfig(
        language_models={
            "prod": fc.OpenAILanguageModel(
                model_name="gpt-4o",
                rpm=500,
                tpm=200_000
            )
        }
    ),
    cloud=fc.CloudConfig(
        executor_size="large"
    )
)

The local-first development philosophy ensures you can prototype against sample data before committing to cloud infrastructure. Read more: https://typedef.ai/blog/fenic-open-source

Monitoring Visual Pipelines

Track key metrics for multimodal processing:

python
# Access built-in metrics
metrics = session.table("fenic_system.query_metrics")

# Analyze processing costs
cost_summary = metrics.filter(
    fc.col("operation").contains("parse_pdf")
).select(
    fc.col("model"),
    fc.sum("cost_usd").alias("total_cost"),
    fc.avg("latency_ms").alias("avg_latency"),
    fc.count("*").alias("operations")
).group_by("model")

cost_summary.show()

Monitor image processing latency separately from text operations. Visual content typically requires more tokens and longer processing times.

Key Metrics to Track

Processing Coverage

Percentage of images successfully processed
Declining coverage indicates quality issues in input data

Extraction Accuracy

Validate structured extraction against test sets
Track accuracy over time to detect model drift

Cost Per Document

Processing cost by model and document type
Optimize spending based on value delivered

Latency Percentiles

P50, P95, P99 processing times
Identify bottlenecks and set appropriate timeouts

Error Recovery Strategies

Visual processing introduces failure modes beyond text processing. Files become corrupted. Images are unreadable. Transcripts contain only music with no speech.

Implement layered error handling:

python
from fenic.core.exceptions import ExecutionError

try:
    # Attempt primary processing path
    results = pdfs.select(
        fc.semantic.parse_pdf(
            fc.col("path"),
            describe_images=True,
            model_alias="primary"
        )
    )
except ExecutionError as e:
    # Fallback to simpler model or text-only extraction
    results = pdfs.select(
        fc.semantic.parse_pdf(
            fc.col("path"),
            describe_images=False,
            model_alias="fallback"
        )
    )
    # Log degraded processing
    logging.warning(f"Fell back to text-only: {e}")

Store processing status alongside results to enable downstream systems to handle degraded data appropriately.

Document Intelligence Pipeline

Process mixed document types with embedded images:

python
# Classify documents by content type
docs = session.read.pdf_metadata("incoming/**/*.pdf")

# Route based on visual content
classified = docs.select(
    fc.col("file_path"),
    fc.col("image_count"),
    fc.col("page_count"),
    fc.when(
        fc.col("image_count") > 5,
        fc.lit("image_heavy")
    ).when(
        fc.col("has_signature_fields"),
        fc.lit("form")
    ).otherwise(
        fc.lit("text")
    ).alias("doc_type")
)

# Process each category optimally
image_heavy = classified.filter(fc.col("doc_type") == "image_heavy")
forms = classified.filter(fc.col("doc_type") == "form")
text_docs = classified.filter(fc.col("doc_type") == "text")

# Vision model for image-heavy documents
image_results = image_heavy.select(
    fc.semantic.parse_pdf(
        fc.col("file_path"),
        describe_images=True,
        model_alias="vision_model"
    )
).select(
    fc.semantic.extract("content", ImageHeavySchema)
)

This pattern appears in case studies like https://typedef.ai/blog/how-typedef-cut-rudderstack-s-triage-time-by-95 where intelligent routing reduces costs while maintaining quality.

Video Meeting Analysis

Extract insights from recorded meetings with temporal context:

python
from fenic.core.types import TranscriptType

# Load meeting transcripts
meetings = session.read.docs(
    "recordings/**/*.srt",
    content_type="markdown",
    recursive=True
)

# Extract action items with timestamps
class ActionItem(BaseModel):
    task: str
    owner: str
    deadline: Optional[str]
    timestamp: str

actions = meetings.select(
    fc.col("file_path").alias("meeting"),
    fc.col("speaker"),
    fc.col("start_time"),
    fc.semantic.extract(
        fc.col("content"),
        response_format=ActionItem
    ).alias("action")
).filter(
    fc.col("action").is_not_null()
)

# Aggregate by owner
assignments = actions.group_by("action.owner").agg(
    fc.count("*").alias("total_tasks"),
    fc.collect_list("action.task").alias("tasks")
)

Temporal alignment enables:

Generating timestamped summaries
Enabling navigation to relevant video segments
Correlating action items with specific meeting moments

Visual Content Classification

Build content moderation or tagging systems:

python
# Process images in PDF documents
images = session.read.pdf_metadata("user_uploads/**/*.pdf")

parsed = images.select(
    fc.col("file_path"),
    fc.semantic.parse_pdf(
        fc.col("file_path"),
        describe_images=True
    )
)

# Classify visual content
classified = parsed.select(
    fc.col("file_path"),
    fc.semantic.classify(
        fc.col("markdown"),
        classes=["product", "person", "text", "diagram", "other"],
        examples=classification_examples
    ).alias("category"),
    fc.semantic.analyze_sentiment(fc.col("markdown")).alias("sentiment")
)

# Flag content requiring review
flagged = classified.filter(
    (fc.col("category") == "person") |
    (fc.col("sentiment.label") == "negative")
)

Built-in operators like analyze_sentiment and classify simplify common visual content tasks.

Cost Optimization Strategies

Visual processing costs scale with image count and resolution. Optimize spending through several techniques.

Selective Image Processing

Process only images, not entire PDFs, when text content has low value:

python
# Two-pass approach
# Pass 1: Extract text without image descriptions (cheap)
text_only = pdfs.select(
    fc.semantic.parse_pdf(fc.col("path"), describe_images=False)
)

# Pass 2: Process images only for relevant documents
relevant = text_only.filter(
    fc.col("markdown").contains("see diagram|refer to figure")
)

# Expensive image processing only where needed
with_images = relevant.select(
    fc.semantic.parse_pdf(
        fc.col("path"),
        describe_images=True,
        model_alias="vision_model"
    )
)

This two-stage approach reduces costs by 40-60% for document sets where most pages lack meaningful visual content.

Token Budget Management

Context windows from https://typedef.ai/resources/multimodal-ai-engine-stats now reach 2 million tokens, enabling large documents in single calls. However, costs scale with context usage.

python
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "budget": fc.OpenAILanguageModel(
                model_name="gpt-4o-mini",
                rpm=1000,
                tpm=500_000
            )
        }
    )
)

# Limit output tokens for summaries
summaries = df.select(
    fc.semantic.map(
        "Summarize: {{content}}",
        content=fc.col("content"),
        max_output_tokens=256  # Constrain output length
    )
)

Track spending through metrics tables and set budget alerts for production pipelines.

Resolution and Quality Tradeoffs

Not all images require high-resolution processing. Focus expensive vision models on documents where visual content provides unique value:

python
# Filter for high-value images
high_value = pdfs.filter(
    (fc.col("image_count") > 3) &
    (fc.col("page_count") < 20) &
    (fc.col("title").contains("roadmap|strategy|architecture"))
)

Content Redaction for Compliance

Visual content often contains sensitive information. Product images show unreleased features. Screenshots capture customer data. Meeting recordings include confidential discussions.

PII Removal Before Processing

python
import re

def redact_pii(text: str) -> str:
    # Redact emails
    text = re.sub(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b', '[EMAIL]', text)
    # Redact phone numbers
    text = re.sub(r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b', '[PHONE]', text)
    return text

# Apply redaction before processing
redacted = pdfs.select(
    fc.col("file_path"),
    fc.semantic.parse_pdf(fc.col("file_path"))
).select(
    fc.col("file_path"),
    fc.udf(redact_pii, return_type=fc.StringType)(fc.col("markdown")).alias("redacted")
)

# Safe to process after redaction
results = redacted.select(
    fc.semantic.extract("redacted", Schema)
)

Implement redaction before any external model calls to prevent data leakage.

Audit Trails with Lineage Tracking

Fenic's lineage tracking provides row-level processing history. Details at https://typedef.ai/resources/build-reliable-ai-pipelines-fenic-semantic-operators

python
# Load the table and create lineage object
processed_images = session.table("processed_images")
lineage = processed_images.lineage()

# Trace specific rows backwards through transformations
source_rows = lineage.backward(["document_123"])

Lineage enables:

Compliance audits
Debugging processing issues
Cost attribution to specific operations

Data Warehouse Integration

Store processed visual data alongside structured data:

python
# Process images
processed = pdfs.select(
    fc.col("file_path"),
    fc.semantic.parse_pdf(fc.col("file_path"), describe_images=True)
).select(
    fc.semantic.extract("markdown", ProductInfo)
).unnest("product_info")

# Join with structured product catalog
catalog = session.read.table("warehouse.products")

enriched = processed.join(
    catalog,
    on=fc.col("product_id") == catalog.col("id")
)

# Write back to warehouse
enriched.write.save_as_table("warehouse.product_images_processed", mode="overwrite")

Warehouse-native operations from https://typedef.ai/blog/typedef-launch enable querying visual insights alongside transactional data.

API Exposure with MCP Server

Serve processed visual data through APIs using Fenic's MCP server:

python
from fenic.api.mcp import create_mcp_server, run_mcp_server_asgi

# Register search tool
session.catalog.create_tool(
    tool_name="search_images",
    tool_description="Search processed images by content",
    tool_query=processed_images.filter(
        fc.col("description").contains("{{search_term}}")
    ),
    tool_params=[
        fc.ToolParam(
            name="search_term",
            description="Content to search for"
        )
    ]
)

# Serve via HTTP
tools = session.catalog.list_tools()
server = create_mcp_server(session, "ImageAPI", tools=tools)
app = run_mcp_server_asgi(server, port=8000)

Expose visual intelligence to downstream applications through standardized interfaces.

Measuring Processing Coverage

What percentage of images are successfully processed?

python
# Calculate coverage
total = pdfs.count()

successful = pdfs.select(
    fc.semantic.parse_pdf(fc.col("file_path"), describe_images=True)
).filter(
    fc.col("markdown").is_not_null() &
    fc.col("markdown").contains("![")  # Has image descriptions
).count()

coverage = (successful / total) * 100
print(f"Image processing coverage: {coverage:.1f}%")

Track coverage over time. Declining coverage indicates quality issues in input data or model problems.

Extraction Accuracy Validation

For structured extraction, validate against ground truth:

python
# Load test set with known labels
test_set = session.read.csv("test_images.csv")

# Process through pipeline
predictions = test_set.select(
    fc.col("file_path"),
    fc.semantic.parse_pdf(fc.col("file_path"), describe_images=True)
).select(
    fc.col("file_path"),
    fc.semantic.extract("markdown", ProductInfo).alias("predicted"),
    fc.col("ground_truth")
)

# Calculate accuracy
matches = predictions.filter(
    fc.col("predicted.product_id") == fc.col("ground_truth.product_id")
).count()

accuracy = (matches / test_set.count()) * 100

Run accuracy checks regularly to detect model drift or prompt degradation.

Horizontal Scaling for Large Workloads

Fenic's DataFrame abstraction enables parallel processing:

python
# Process batches in parallel
import concurrent.futures

def process_batch(file_paths: list) -> fc.DataFrame:
    batch = session.read.pdf_metadata(file_paths)
    return batch.select(
        fc.semantic.parse_pdf(fc.col("file_path"), describe_images=True)
    )

# Partition workload
all_files = list(Path("documents/").glob("**/*.pdf"))
batch_size = 100
batches = [all_files[i:i+batch_size] for i in range(0, len(all_files), batch_size)]

# Process in parallel
with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
    results = list(executor.map(process_batch, batches))

Incremental Processing for Large Repositories

Avoid reprocessing unchanged documents:

python
# Track processed files
processed_log = session.table("processed_log")

# Get new files since last run
all_pdfs = session.read.pdf_metadata("documents/**/*.pdf")

new_pdfs = all_pdfs.join(
    processed_log,
    on=fc.col("file_path") == processed_log.col("path"),
    how="left_anti"  # Anti-join for new files only
)

# Process only new documents
results = new_pdfs.select(
    fc.semantic.parse_pdf(fc.col("file_path"), describe_images=True)
)

# Update log
new_pdfs.select(
    fc.col("file_path").alias("path"),
    fc.current_timestamp().alias("processed_at")
).write.save_as_table("processed_log", mode="append")

Incremental processing reduces costs and latency for large document repositories.

Cloud Deployment Configuration

Scale compute resources based on workload:

python
from fenic.api.session.config import CloudExecutorSize

# Production configuration with cloud scaling
config = fc.SessionConfig(
    app_name="visual_pipeline_prod",
    cloud=fc.CloudConfig(
        size=CloudExecutorSize.XLARGE  # Scale up for heavy workloads
    ),
    semantic=fc.SemanticConfig(
        language_models={
            "prod": fc.OpenAILanguageModel(
                model_name="gpt-4o",
                rpm=1000,  # Higher limits for production
                tpm=1_000_000
            )
        }
    )
)

Cloud deployment requires zero code changes from local development. Learn more: https://typedef.ai/blog/fenic-open-source

Implementation Checklist

Before deploying visual processing pipelines to production:

Data Pipeline

Implement metadata-based routing for different document types
Add deduplication before expensive model calls
Cache intermediate results at key processing stages
Set up incremental processing for large repositories

Model Configuration

Configure rate limits based on provider tiers
Set up fallback models for error recovery
Implement token budget controls for cost management
Test provider failover mechanisms

Monitoring

Track processing coverage metrics
Monitor extraction accuracy against test sets
Set up cost tracking per document type
Alert on anomalous latency or error rates

Compliance

Implement PII redaction before model calls
Set up audit trails with lineage tracking
Configure data residency requirements
Document processing decisions for compliance reviews

Operations

Test error recovery paths
Document fallback procedures
Set up monitoring dashboards
Plan capacity for peak loads

Getting Started

Start building visual processing pipelines with Fenic:

GitHub Repository https://github.com/typedef-ai/fenic

Documentation https://docs.fenic.ai

Example Implementations https://github.com/typedef-ai/fenic/tree/main/examples

Community Support https://discord.gg/pKDRPAY8pB

Framework Overview https://typedef.ai/blog/fenic-open-source

Latest Release https://typedef.ai/blog/fenic-0-5-0-smarter-docs-date-data-types-openrouter-plus-planning-and-reliability-upgrades

Technical Deep Dive https://typedef.ai/resources/build-reliable-ai-pipelines-fenic-semantic-operators

Processing visual data at scale requires infrastructure that treats images and video as native data types. Fenic's DataFrame abstraction brings the reliability of traditional data processing to multimodal content. Build pipelines that scale from prototype to production with declarative operations, automatic optimization, and type-safe extraction. How to Handle Video and Ima ... fcf08012880ecb242d70ee5c.md External Displaying How to Handle Video and Image Data in Unstructured 290df41efcf08012880ecb242d70ee5c.md.