When researchers at our materials science division needed to extract insights from thousands of scientific papers, our traditional keyword search was returning noise instead of knowledge. I rebuilt the entire research workflow using production-grade RAG pipelines that transformed how scientists interact with literature.
The Research Problem
Our materials scientists were drowning in information. Picture this: thousands of research papers across multiple domains, literature reviews that took weeks for each project, and critical insights buried so deep in technical jargon that even experts were missing them. We had all this knowledge, but no practical way to actually use it.
Traditional search returned paper titles, but researchers needed answers.
Solution Architecture: Production RAG at Scale
1. Document Processing Pipeline
Built a comprehensive PDF processing system using LangChain:
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
class DocumentProcessor:
def __init__(self):
self.text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
separators=["\n\n", "\n", ".", "!", "?"]
)
self.embeddings = HuggingFaceEmbeddings(
model_name="sentence-transformers/all-MiniLM-L6-v2"
)
def process_pdf(self, pdf_path):
# Load and split documents
loader = PyPDFLoader(pdf_path)
pages = loader.load_and_split()
# Intelligent chunking preserving context
chunks = self.text_splitter.split_documents(pages)
# Generate embeddings for vector storage
return self.embeddings.embed_documents([chunk.page_content for chunk in chunks])
2. Vector Database Architecture
Implemented enterprise-grade vector storage using Weaviate:
import weaviate
from weaviate.util import generate_uuid5
class VectorStore:
def __init__(self):
self.client = weaviate.Client(
url="http://weaviate:8080",
additional_headers={"X-OpenAI-Api-Key": os.getenv("OPENAI_API_KEY")}
)
def create_schema(self):
schema = {
"classes": [{
"class": "ResearchDocument",
"vectorizer": "text2vec-openai",
"properties": [
{"name": "content", "dataType": ["text"]},
{"name": "title", "dataType": ["string"]},
{"name": "authors", "dataType": ["string[]"]},
{"name": "publication_date", "dataType": ["date"]},
{"name": "doi", "dataType": ["string"]},
{"name": "research_domain", "dataType": ["string"]}
]
}]
}
self.client.schema.create(schema)
3. LLMOps Pipeline with HuggingFace and LoRA
Deployed fine-tuned models using efficient parameter updates:
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import LoraConfig, get_peft_model, TaskType
class ResearchQAModel:
def __init__(self):
# Load base model
self.base_model = AutoModelForCausalLM.from_pretrained(
"microsoft/DialoGPT-medium",
torch_dtype=torch.float16,
device_map="auto"
)
# Configure LoRA for efficient fine-tuning
lora_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
r=16, # Low rank
lora_alpha=32,
lora_dropout=0.1,
target_modules=["query", "value", "key", "dense"]
)
self.model = get_peft_model(self.base_model, lora_config)
self.tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
def fine_tune_on_domain(self, research_qa_dataset):
# Domain-specific fine-tuning with minimal parameters
training_args = TrainingArguments(
output_dir="./lora-research-qa",
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
learning_rate=1e-4,
num_train_epochs=3,
save_strategy="epoch"
)
trainer = Trainer(
model=self.model,
args=training_args,
train_dataset=research_qa_dataset
)
trainer.train()
MCP Framework Implementation
Implemented Model Control Protocol for standardized AI model communication:
from typing import Dict, Any
import asyncio
class MCPFramework:
def __init__(self):
self.models = {}
self.message_queue = asyncio.Queue()
async def register_model(self, model_id: str, model_config: Dict[str, Any]):
"""Register AI model with standardized interface"""
self.models[model_id] = {
'config': model_config,
'status': 'active',
'performance_metrics': {},
'last_health_check': datetime.now()
}
async def route_request(self, request: Dict[str, Any]) -> Dict[str, Any]:
"""Route requests to appropriate model based on capability"""
model_id = self.select_best_model(request['task_type'])
# Standardized request format
formatted_request = {
'model_id': model_id,
'input': request['input'],
'parameters': request.get('parameters', {}),
'context': request.get('context', {})
}
return await self.execute_request(formatted_request)
Production Deployment Results
The Results Were Pretty Amazing
What used to take researchers 45 minutes of digging through papers now happens in seconds. The system finds relevant information with impressive accuracy, and it handles hundreds of queries every day without breaking a sweat. Most importantly, it actually answers the questions scientists are asking instead of just returning paper titles.
Under the hood, we’re running this on a robust infrastructure with a massive vector database, optimized model serving, and smart monitoring so we know when something’s not working right. The whole thing scales automatically based on demand.
Key Engineering Insights
1. Context Window Optimization
Implemented sliding window technique for large documents:
- Overlapping chunks preserve semantic continuity
- Dynamic chunk sizing based on document structure
- Citation tracking for source attribution
2. Multi-Modal RAG
Extended system to handle figures and tables:
- OCR integration for image-based content
- Table structure recognition and embedding
- Cross-modal similarity search
3. Continuous Learning Pipeline
Built feedback loop for model improvement:
- User interaction tracking and rating
- Automated retraining with new research papers
- A/B testing for prompt optimization
Lessons Learned
- Good embeddings beat big models: I spent most of my time getting the document preprocessing and chunking strategy right, and it was totally worth it.
- Domain expertise is everything: Generic language models just couldn’t handle materials science terminology - we had to fine-tune specifically for our field.
- Database optimization is crucial: Properly indexing our vector database made the difference between usable and unusable query times.
- Scientists want to understand, not just get answers: The interface needed to show confidence scores and sources, not just spit out responses.
Future Roadmap
Right now we’re working on some exciting next steps. We want to build smarter workflows that can plan multi-step research tasks, handle papers in different languages (so much good research happens outside English), automatically incorporate new publications as they’re published, and create collaborative workspaces where research teams can share insights.
This RAG system has completely transformed how our researchers work with scientific literature. Instead of being overwhelmed by information, they can now quickly find exactly what they need and spend their time on the interesting stuff - actually doing research.
The secret was treating this like a real production system from the beginning: proper monitoring, systematic testing, and continuous improvement based on how people actually use it.
I’ve got more technical details and code examples if you’re interested. Always happy to discuss LLMOps architecture and RAG optimization challenges.