The enterprise AI landscape just shifted dramatically. While most organizations are still wrestling with basic text-based RAG implementations, forward-thinking companies are already deploying multi-modal systems that can process images, documents, and structured data simultaneously. This isn’t just an incremental improvement—it’s a fundamental reimagining of how enterprises can leverage their diverse data assets.
Consider this scenario: Your customer support team receives thousands of tickets daily containing screenshots, technical diagrams, product images, and lengthy text descriptions. Traditional RAG systems force you to choose—either extract text from images (losing visual context) or ignore visual data entirely. OpenAI’s GPT-4o with Vision API eliminates this false choice, enabling systems that understand both textual and visual information in their native formats.
The challenge isn’t just technical complexity—it’s architectural. Multi-modal RAG systems require fundamentally different approaches to data ingestion, vector storage, retrieval strategies, and response generation. Most existing RAG frameworks weren’t designed for this level of sophistication, leaving enterprises to build custom solutions from scratch.
In this comprehensive guide, we’ll walk through building a production-ready multi-modal RAG system that can handle text documents, images, charts, diagrams, and structured data. You’ll learn how to architect scalable ingestion pipelines, implement efficient multi-modal vector storage, design intelligent retrieval strategies, and deploy systems that maintain performance at enterprise scale. By the end, you’ll have a complete implementation that transforms how your organization leverages its diverse data assets.
Understanding Multi-Modal RAG Architecture
Traditional RAG systems follow a linear text-based pipeline: documents are chunked, embedded, stored in vector databases, and retrieved based on semantic similarity. Multi-modal RAG explodes this simplicity into a sophisticated orchestration of parallel processing streams.
The core architectural difference lies in data representation. While text-based systems convert everything to uniform embeddings, multi-modal systems must preserve and leverage the unique characteristics of different data types. Images contain spatial relationships that text descriptions can’t capture. Charts embed quantitative relationships that require mathematical understanding. Technical diagrams convey process flows that demand visual reasoning.
OpenAI’s GPT-4o with Vision API provides the foundation for this architectural shift. Unlike previous approaches that required separate models for different modalities, GPT-4o processes text and images natively within a single model. This unified processing enables sophisticated cross-modal understanding—the system can analyze a chart while referencing related text, or interpret a technical diagram in the context of accompanying documentation.
The storage layer requires equally sophisticated approaches. Traditional vector databases store single-dimensional embeddings, but multi-modal systems need to manage relationships between text chunks, image regions, and metadata. Modern solutions like Qdrant and Pinecone now support multi-vector storage, enabling retrieval strategies that consider multiple data types simultaneously.
Retrieval becomes a complex orchestration problem. Simple similarity search doesn’t work when dealing with heterogeneous data types. Effective multi-modal retrieval requires hybrid approaches that combine semantic similarity, visual similarity, metadata filtering, and cross-modal relationship scoring.
Setting Up the Development Environment
Building production-ready multi-modal RAG systems requires careful environment configuration and dependency management. The integration points between vision models, vector databases, and processing pipelines create unique requirements that standard RAG setups don’t address.
Start by establishing your Python environment with specific version constraints. GPT-4o vision capabilities require OpenAI library version 1.3.0 or higher, while multi-modal vector operations need updated database clients. Here’s the core dependency structure:
# requirements.txt
openai>=1.3.0
qdrant-client>=1.6.0
pillow>=10.0.0
pytesseract>=0.3.10
pdf2image>=3.1.0
numpy>=1.24.0
scipy>=1.11.0
transformers>=4.35.0
torch>=2.0.0
fastapi>=0.104.0
uvicorn>=0.24.0
celery>=5.3.0
redis>=5.0.0
The development environment must handle multiple processing streams simultaneously. Image processing, text extraction, and embedding generation all require different computational resources. Configure separate worker processes for each data type to prevent bottlenecks:
# config.py
PROCESSING_CONFIG = {
'image_workers': 4,
'text_workers': 8,
'embedding_workers': 2,
'max_image_size': 20 * 1024 * 1024, # 20MB
'supported_formats': ['png', 'jpg', 'jpeg', 'pdf', 'tiff'],
'ocr_languages': ['eng', 'spa', 'fra'],
'chunk_overlap': 200,
'max_chunk_size': 1000
}
Vector database configuration requires special attention for multi-modal scenarios. Qdrant’s multi-vector collections enable storing different embedding types for the same document, while maintaining relationships between modalities:
# database_setup.py
from qdrant_client import QdrantClient
from qdrant_client.models import Distance, VectorParams, CollectionInfo
client = QdrantClient(url="http://localhost:6333")
# Create multi-modal collection
client.create_collection(
collection_name="multimodal_knowledge",
vectors_config={
"text": VectorParams(size=1536, distance=Distance.COSINE),
"image": VectorParams(size=1024, distance=Distance.COSINE),
"metadata": VectorParams(size=768, distance=Distance.COSINE)
}
)
Implementing Multi-Modal Data Ingestion
Data ingestion for multi-modal RAG systems requires sophisticated preprocessing pipelines that can handle diverse input formats while preserving semantic relationships between different modalities. Unlike traditional text-based systems, multi-modal ingestion must extract, process, and correlate information across images, documents, and structured data simultaneously.
The ingestion pipeline begins with intelligent format detection and routing. Different file types require specialized processing approaches, and the system must identify not just file formats but content types within those formats. A PDF might contain text, images, charts, and tables—each requiring different extraction strategies:
# ingestion_pipeline.py
import pytesseract
from pdf2image import convert_from_path
from PIL import Image
import openai
from typing import List, Dict, Tuple
class MultiModalIngestionPipeline:
def __init__(self, openai_client):
self.client = openai_client
self.processors = {
'pdf': self._process_pdf,
'image': self._process_image,
'text': self._process_text
}
def ingest_document(self, file_path: str) -> Dict:
"""Process document through appropriate pipeline"""
file_type = self._detect_file_type(file_path)
processor = self.processors.get(file_type)
if not processor:
raise ValueError(f"Unsupported file type: {file_type}")
return processor(file_path)
def _process_pdf(self, file_path: str) -> Dict:
"""Extract text and images from PDF"""
pages = convert_from_path(file_path)
extracted_data = {
'text_chunks': [],
'images': [],
'metadata': {'source': file_path, 'type': 'pdf'}
}
for page_num, page in enumerate(pages):
# Extract text using OCR
text = pytesseract.image_to_string(page)
if text.strip():
extracted_data['text_chunks'].append({
'content': text,
'page': page_num,
'type': 'text'
})
# Analyze page for images and charts
image_analysis = self._analyze_image_content(page)
if image_analysis['has_visual_content']:
extracted_data['images'].append({
'image': page,
'analysis': image_analysis,
'page': page_num
})
return extracted_data
The image analysis component leverages GPT-4o’s vision capabilities to understand visual content contextually. Rather than simply extracting text from images, the system analyzes visual elements, identifies chart types, and understands spatial relationships:
def _analyze_image_content(self, image: Image) -> Dict:
"""Analyze image content using GPT-4o vision"""
# Convert image to base64 for API
import base64
import io
buffer = io.BytesIO()
image.save(buffer, format='PNG')
image_data = base64.b64encode(buffer.getvalue()).decode()
response = self.client.chat.completions.create(
model="gpt-4o",
messages=[
{
"role": "user",
"content": [
{
"type": "text",
"text": "Analyze this image and identify: 1) Type of content (chart, diagram, text, photo), 2) Key information or data points, 3) Relationships between elements, 4) Any text that should be extracted. Provide a structured JSON response."
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/png;base64,{image_data}"
}
}
]
}
],
max_tokens=500
)
try:
import json
analysis = json.loads(response.choices[0].message.content)
analysis['has_visual_content'] = True
return analysis
except json.JSONDecodeError:
return {
'has_visual_content': False,
'error': 'Failed to parse image analysis'
}
Text processing for multi-modal systems requires enhanced chunking strategies that consider visual context. Traditional text chunking often breaks semantic units that span multiple modalities. The system implements context-aware chunking that preserves relationships between text and associated visual elements:
def _create_contextual_chunks(self, extracted_data: Dict) -> List[Dict]:
"""Create chunks that preserve multi-modal context"""
chunks = []
# Group text and images by proximity
for text_chunk in extracted_data['text_chunks']:
chunk_data = {
'text': text_chunk['content'],
'page': text_chunk['page'],
'associated_images': []
}
# Find images on the same page
for image_data in extracted_data['images']:
if image_data['page'] == text_chunk['page']:
chunk_data['associated_images'].append({
'analysis': image_data['analysis'],
'image_id': f"img_{image_data['page']}_{len(chunks)}"
})
# Enhance text with visual context
if chunk_data['associated_images']:
visual_context = self._generate_visual_context(
chunk_data['associated_images']
)
chunk_data['enhanced_text'] = f"{chunk_data['text']}\n\nVisual Context: {visual_context}"
else:
chunk_data['enhanced_text'] = chunk_data['text']
chunks.append(chunk_data)
return chunks
Building Multi-Vector Storage Architecture
Multi-modal RAG systems require sophisticated storage architectures that can maintain relationships between different data types while enabling efficient retrieval across modalities. Traditional single-vector approaches break down when dealing with documents that contain text, images, charts, and structured data that all contribute to semantic meaning.
The storage architecture must solve several complex problems simultaneously. First, it needs to store multiple embedding types for each document while maintaining their relationships. Second, it must enable retrieval strategies that can search across modalities effectively. Third, it must handle version control and updates when source documents change.
Qdrant’s multi-vector collections provide the foundation for this architecture. Each document gets decomposed into multiple vectors representing different aspects of its content:
# vector_storage.py
from qdrant_client import QdrantClient
from qdrant_client.models import PointStruct, Record
import openai
import numpy as np
from typing import List, Dict, Any
class MultiModalVectorStore:
def __init__(self, qdrant_client: QdrantClient, openai_client):
self.qdrant = qdrant_client
self.openai = openai_client
self.collection_name = "multimodal_knowledge"
def store_document(self, document_data: Dict) -> str:
"""Store multi-modal document with related vectors"""
document_id = self._generate_document_id(document_data)
# Generate embeddings for different modalities
vectors = self._generate_multi_modal_embeddings(document_data)
# Store in Qdrant with relationships
points = []
for chunk_idx, chunk in enumerate(document_data['chunks']):
point_id = f"{document_id}_{chunk_idx}"
point = PointStruct(
id=point_id,
vector={
"text": vectors['text'][chunk_idx],
"visual": vectors['visual'][chunk_idx],
"semantic": vectors['semantic'][chunk_idx]
},
payload={
"document_id": document_id,
"chunk_index": chunk_idx,
"text_content": chunk['enhanced_text'],
"has_images": len(chunk['associated_images']) > 0,
"image_count": len(chunk['associated_images']),
"content_type": self._classify_content_type(chunk),
"source": document_data['metadata']['source']
}
)
points.append(point)
self.qdrant.upsert(
collection_name=self.collection_name,
points=points
)
return document_id
The embedding generation process creates specialized vectors for different aspects of the content. Text embeddings capture semantic meaning, visual embeddings encode image content, and semantic embeddings represent the combined understanding:
def _generate_multi_modal_embeddings(self, document_data: Dict) -> Dict[str, List]:
"""Generate embeddings for different modalities"""
embeddings = {
'text': [],
'visual': [],
'semantic': []
}
for chunk in document_data['chunks']:
# Text embeddings using OpenAI
text_embedding = self._get_text_embedding(chunk['enhanced_text'])
embeddings['text'].append(text_embedding)
# Visual embeddings for associated images
if chunk['associated_images']:
visual_embedding = self._get_visual_embedding(
chunk['associated_images']
)
else:
visual_embedding = np.zeros(1024).tolist() # Default visual vector
embeddings['visual'].append(visual_embedding)
# Combined semantic embedding
semantic_prompt = self._create_semantic_prompt(chunk)
semantic_embedding = self._get_text_embedding(semantic_prompt)
embeddings['semantic'].append(semantic_embedding)
return embeddings
def _get_visual_embedding(self, images: List[Dict]) -> List[float]:
"""Generate embeddings for visual content"""
# Combine image analyses into descriptive text
visual_descriptions = []
for img in images:
if 'analysis' in img and 'description' in img['analysis']:
visual_descriptions.append(img['analysis']['description'])
combined_description = " ".join(visual_descriptions)
if combined_description:
return self._get_text_embedding(
f"Visual content: {combined_description}"
)
else:
return np.zeros(1536).tolist()
def _create_semantic_prompt(self, chunk: Dict) -> str:
"""Create enhanced prompt for semantic understanding"""
prompt_parts = [chunk['enhanced_text']]
if chunk['associated_images']:
for img in chunk['associated_images']:
if 'analysis' in img:
analysis = img['analysis']
if 'content_type' in analysis:
prompt_parts.append(
f"Contains {analysis['content_type']}: {analysis.get('description', '')}"
)
return " ".join(prompt_parts)
Implementing Intelligent Multi-Modal Retrieval
Multi-modal retrieval represents the most complex aspect of these systems, requiring sophisticated strategies that can understand user queries and determine which modalities are most relevant for generating accurate responses. Unlike traditional RAG systems that rely on simple semantic similarity, multi-modal retrieval must orchestrate searches across text, visual, and combined semantic spaces.
The retrieval system begins with query analysis to understand what types of information the user is seeking. Questions about charts or visual data require different retrieval strategies than pure text queries. The system uses GPT-4o to analyze query intent and determine optimal retrieval approaches:
# retrieval_engine.py
from typing import List, Dict, Tuple
import numpy as np
from qdrant_client.models import Filter, FieldCondition, Range
class MultiModalRetrievalEngine:
def __init__(self, vector_store, openai_client):
self.vector_store = vector_store
self.openai = openai_client
def retrieve(self, query: str, top_k: int = 10) -> List[Dict]:
"""Intelligent multi-modal retrieval"""
# Analyze query to determine retrieval strategy
query_analysis = self._analyze_query_intent(query)
# Generate embeddings for different search vectors
query_vectors = self._generate_query_vectors(query, query_analysis)
# Execute multi-modal search
results = self._execute_hybrid_search(
query_vectors, query_analysis, top_k
)
# Re-rank results based on multi-modal relevance
ranked_results = self._rerank_multimodal_results(
results, query, query_analysis
)
return ranked_results[:top_k]
def _analyze_query_intent(self, query: str) -> Dict:
"""Analyze query to determine optimal retrieval strategy"""
analysis_prompt = f"""
Analyze this query and determine:
1. Primary information type needed (text, visual, data, process)
2. Whether visual content is likely relevant
3. Confidence level for different modalities
4. Suggested search weights for text vs visual vs semantic vectors
Query: {query}
Respond in JSON format with keys: primary_type, visual_relevant,
modality_weights (text, visual, semantic), reasoning
"""
response = self.openai.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": analysis_prompt}],
temperature=0.1
)
try:
import json
return json.loads(response.choices[0].message.content)
except json.JSONDecodeError:
# Fallback to balanced weights
return {
"primary_type": "text",
"visual_relevant": False,
"modality_weights": {"text": 0.6, "visual": 0.2, "semantic": 0.2},
"reasoning": "Analysis failed, using balanced approach"
}
The hybrid search implementation combines multiple retrieval strategies based on the query analysis. For visual-relevant queries, the system increases the weight of visual embeddings and prioritizes chunks with associated images:
def _execute_hybrid_search(self, query_vectors: Dict, analysis: Dict, top_k: int) -> List[Dict]:
"""Execute search across multiple vector spaces"""
all_results = []
weights = analysis['modality_weights']
# Search text vectors
if weights['text'] > 0:
text_results = self.vector_store.qdrant.search(
collection_name=self.vector_store.collection_name,
query_vector=("text", query_vectors['text']),
limit=top_k * 2,
score_threshold=0.5
)
for result in text_results:
result.score *= weights['text']
result.search_type = 'text'
all_results.append(result)
# Search visual vectors if relevant
if weights['visual'] > 0 and analysis['visual_relevant']:
visual_filter = Filter(
must=[
FieldCondition(
key="has_images",
match={"value": True}
)
]
)
visual_results = self.vector_store.qdrant.search(
collection_name=self.vector_store.collection_name,
query_vector=("visual", query_vectors['visual']),
query_filter=visual_filter,
limit=top_k,
score_threshold=0.4
)
for result in visual_results:
result.score *= weights['visual']
result.search_type = 'visual'
all_results.append(result)
# Search semantic vectors for comprehensive understanding
if weights['semantic'] > 0:
semantic_results = self.vector_store.qdrant.search(
collection_name=self.vector_store.collection_name,
query_vector=("semantic", query_vectors['semantic']),
limit=top_k,
score_threshold=0.5
)
for result in semantic_results:
result.score *= weights['semantic']
result.search_type = 'semantic'
all_results.append(result)
return all_results
def _rerank_multimodal_results(self, results: List, query: str, analysis: Dict) -> List[Dict]:
"""Re-rank results considering multi-modal relevance"""
# Group results by document chunk to avoid duplicates
unique_results = {}
for result in results:
chunk_id = result.id
if chunk_id not in unique_results:
unique_results[chunk_id] = {
'result': result,
'scores': [result.score],
'search_types': [result.search_type]
}
else:
unique_results[chunk_id]['scores'].append(result.score)
unique_results[chunk_id]['search_types'].append(result.search_type)
# Calculate combined relevance scores
final_results = []
for chunk_id, data in unique_results.items():
# Weighted average of scores
combined_score = np.mean(data['scores'])
# Boost for multi-modal matches
if len(set(data['search_types'])) > 1:
combined_score *= 1.2
# Boost for visual content when relevant
if analysis['visual_relevant'] and data['result'].payload.get('has_images', False):
combined_score *= 1.1
result_dict = {
'id': chunk_id,
'score': combined_score,
'content': data['result'].payload.get('text_content', ''),
'metadata': {
'source': data['result'].payload.get('source', ''),
'has_images': data['result'].payload.get('has_images', False),
'image_count': data['result'].payload.get('image_count', 0),
'content_type': data['result'].payload.get('content_type', 'text'),
'search_types': data['search_types']
}
}
final_results.append(result_dict)
# Sort by combined score
return sorted(final_results, key=lambda x: x['score'], reverse=True)
Deploying Production-Ready Multi-Modal RAG
Production deployment of multi-modal RAG systems requires careful orchestration of multiple services, robust error handling, and sophisticated monitoring. Unlike traditional RAG deployments, multi-modal systems must handle varying computational loads, manage large file uploads, and coordinate between vision models, vector databases, and inference engines.
The deployment architecture follows a microservices pattern with specialized services for different aspects of the system. This approach enables independent scaling of compute-intensive operations like image processing while maintaining responsive query handling:
# deployment/main.py
from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
from typing import List, Optional
import asyncio
from celery import Celery
app = FastAPI(title="Multi-Modal RAG API", version="1.0.0")
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Initialize Celery for async processing
celery_app = Celery(
'multimodal_rag',
broker='redis://localhost:6379',
backend='redis://localhost:6379'
)
class MultiModalRAGService:
def __init__(self):
self.ingestion_pipeline = MultiModalIngestionPipeline(openai_client)
self.vector_store = MultiModalVectorStore(qdrant_client, openai_client)
self.retrieval_engine = MultiModalRetrievalEngine(self.vector_store, openai_client)
async def process_document(self, file_path: str) -> Dict:
"""Process document asynchronously"""
try:
# Offload processing to Celery worker
task = process_document_task.delay(file_path)
return {"task_id": task.id, "status": "processing"}
except Exception as e:
raise HTTPException(status_code=500, detail=f"Processing failed: {str(e)}")
async def query(self, query: str, filters: Optional[Dict] = None) -> Dict:
"""Handle multi-modal queries"""
try:
results = self.retrieval_engine.retrieve(query, top_k=10)
# Generate response using retrieved context
response = await self._generate_response(query, results)
return {
"query": query,
"response": response,
"sources": [{
"content": r['content'][:200] + "...",
"source": r['metadata']['source'],
"score": r['score'],
"has_images": r['metadata']['has_images']
} for r in results[:5]]
}
except Exception as e:
raise HTTPException(status_code=500, detail=f"Query failed: {str(e)}")
rag_service = MultiModalRAGService()
@app.post("/upload")
async def upload_document(file: UploadFile = File(...)):
"""Upload and process multi-modal documents"""
# Validate file type and size
if file.size > 50 * 1024 * 1024: # 50MB limit
raise HTTPException(status_code=413, detail="File too large")
# Save uploaded file
file_path = f"uploads/{file.filename}"
with open(file_path, "wb") as buffer:
content = await file.read()
buffer.write(content)
# Process asynchronously
result = await rag_service.process_document(file_path)
return result
@app.post("/query")
async def query_knowledge_base(request: Dict):
"""Query the multi-modal knowledge base"""
query = request.get("query")
filters = request.get("filters")
if not query:
raise HTTPException(status_code=400, detail="Query is required")
result = await rag_service.query(query, filters)
return result
The Celery task configuration handles compute-intensive processing operations asynchronously, preventing API timeouts during large document processing:
# tasks.py
from celery import Celery
import logging
celery_app = Celery(
'multimodal_rag',
broker='redis://localhost:6379',
backend='redis://localhost:6379'
)
@celery_app.task(bind=True, max_retries=3)
def process_document_task(self, file_path: str):
"""Process document in background worker"""
try:
logging.info(f"Processing document: {file_path}")
# Initialize services in worker
ingestion_pipeline = MultiModalIngestionPipeline(openai_client)
vector_store = MultiModalVectorStore(qdrant_client, openai_client)
# Process document
document_data = ingestion_pipeline.ingest_document(file_path)
document_id = vector_store.store_document(document_data)
logging.info(f"Successfully processed document: {document_id}")
return {
"status": "completed",
"document_id": document_id,
"chunks_processed": len(document_data['chunks'])
}
except Exception as exc:
logging.error(f"Document processing failed: {str(exc)}")
raise self.retry(countdown=60, exc=exc)
# Docker configuration for deployment
# docker-compose.yml
version: '3.8'
services:
api:
build: .
ports:
- "8000:8000"
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY}
- QDRANT_URL=http://qdrant:6333
- REDIS_URL=redis://redis:6379
depends_on:
- qdrant
- redis
worker:
build: .
command: celery -A tasks worker --loglevel=info
environment:
- OPENAI_API_KEY=${OPENAI_API_KEY}
- QDRANT_URL=http://qdrant:6333
- REDIS_URL=redis://redis:6379
depends_on:
- qdrant
- redis
qdrant:
image: qdrant/qdrant:latest
ports:
- "6333:6333"
volumes:
- qdrant_data:/qdrant/storage
redis:
image: redis:alpine
ports:
- "6379:6379"
volumes:
qdrant_data:
Monitoring and observability become critical for production multi-modal systems. The complexity of processing pipelines requires detailed metrics and alerting:
# monitoring.py
import prometheus_client
from prometheus_client import Counter, Histogram, Gauge
import time
import logging
# Metrics definitions
DOCUMENT_PROCESSING_TIME = Histogram(
'document_processing_seconds',
'Time spent processing documents',
['document_type']
)
QUERY_RESPONSE_TIME = Histogram(
'query_response_seconds',
'Query response time',
['query_type']
)
ERROR_COUNTER = Counter(
'errors_total',
'Total errors by type',
['error_type', 'service']
)
ACTIVE_WORKERS = Gauge(
'active_workers',
'Number of active Celery workers'
)
class MonitoringMixin:
def monitor_processing_time(self, func):
"""Decorator to monitor processing time"""
def wrapper(*args, **kwargs):
start_time = time.time()
try:
result = func(*args, **kwargs)
DOCUMENT_PROCESSING_TIME.labels(
document_type=kwargs.get('file_type', 'unknown')
).observe(time.time() - start_time)
return result
except Exception as e:
ERROR_COUNTER.labels(
error_type=type(e).__name__,
service='processing'
).inc()
raise
return wrapper
Multi-modal RAG systems represent the next evolution in enterprise AI, enabling organizations to leverage their diverse data assets in ways that were previously impossible. The implementation approaches covered in this guide provide the foundation for building production-ready systems that can handle real-world complexity while maintaining performance and reliability.
The key to success lies in understanding that multi-modal systems aren’t just enhanced text-based RAG—they require fundamentally different architectural approaches, from data ingestion through retrieval and response generation. Organizations that master these implementation patterns will gain significant competitive advantages by unlocking insights trapped in their visual and structured data assets.
Ready to transform your organization’s approach to enterprise AI? Start by implementing the multi-modal ingestion pipeline outlined in this guide, then gradually expand to full multi-vector storage and intelligent retrieval. The code examples provide a solid foundation, but remember that production systems require careful testing, monitoring, and iterative refinement based on your specific use cases and data characteristics.
