Retrieval-Augmented Generation (RAG) has become the cornerstone of modern AI applications, enabling organizations to leverage their proprietary data for intelligent question-answering systems. However, building robust RAG pipelines that are scalable, reliable, and maintainable remains a significant challenge. This article demonstrates how to overcome these challenges by integrating LangChain with Cisco Crosswork Workflow Manager (CWM) to create enterprise-grade RAG workflows.
The Challenge: From Prototype to Scale
Although creating a simple RAG system with LangChain is relatively easy, scaling and managing it brings significant challenges:
- Scalability: Handling multiple documents and concurrent requests efficiently.
- Reliability: Ensuring error handling, retries, and fault tolerance for robust operation.
- Orchestration: Managing complex, multi-step processes that involve data ingestion, processing, and generation.
- Monitoring: Tracking workflow execution, performance, and resource utilization.
- Configuration: Supporting different models, parameters, and environments without code changes.
The Solution: LangChain + Cisco Crosswork Workflow Manager
By creating a LangChain adapter for Cisco Crosswork Workflow Manager, we can leverage the best of both worlds:
- LangChain: Provides advanced AI/ML capabilities for document processing, embeddings, text generation and creating powerful chains to orchestrate language models with other components.
- Cisco Crosswork Workflow Manager: Delivers durable workflow orchestration with built-in reliability, monitoring, and scalability.
Architecture Overview
This solution consists of two main workflows that work in tandem:
- PDF-to-Vector Pipeline: A robust workflow that processes PDF documents into searchable vector embeddings.
- Question-Answer Workflow: A retrieval and generation workflow that searches the vector database and generatescontextual answers.
Implementation Deep Dive
1. PDF Processing Workflow
The document ingestion workflow handles the complete pipeline from PDF to searchable vectors, ensuring data is processed efficiently and accurately.
Key Features:
- Configurable chunking strategies (size, overlap)
- Metadata extraction and preservation
- Batch embedding generation for efficiency
- Vector storage with payload indexing for fast retrieval
Sample Input Configuration:
{
"chunk_overlap": 300,
"chunk_size": 1500,
"collection_name": "pdf-test-collection",
"embedding_model": "text-embedding-ada-002",
"extract_metadata": true,
"pdf_path": "/path/to/document.pdf",
"preserve_formatting": true,
"split_pages": true
}
2. Question-Answer Workflow
The retrieval and generation workflow provides intelligent, context-aware answers based on the documents stored in the vector database.
Key Features:
- Semantic similarity search with configurable thresholds
- Context aggregation and optimization
- Multi-model support for embeddings and generation
- Structured response formatting for easy consumption
Sample Input Configuration:
{
"collection_name": "pdf-test-collection",
"content_field": "text",
"embedding_model": "text-embedding-ada-002",
"question": "Why is IoT important?",
"score_threshold": 0.7,
"search_limit": 5
}
Technical Implementation Highlights
LangChain Adapter Architecture
Our Go-based LangChain adapter exposes key document and model operations as CWM activities, creating a seamless bridge between the two systems.
Tools Used
- Open Source LangChain Go Package: Used to build adapter activities that bridge LangChain capabilities with CWM workflows.
- Qdrant Vector Database: Deployed on a dedicated server for high-performance vector storage and similarity search.
- OpenAI Models: Utilized `text-embedding-ada-002` for embeddings and `GPT-4` for answer generation.
- Sample Document: Cisco IoT whitepaper - IoT_IBSG_0411FINAL.pdf
Monitoring & Observability
CWM provides comprehensive, out-of-the-box monitoring capabilities for every workflow execution:
- Real-time workflow execution events and logs.
- Error rates and retry patterns in workflows.
- Custom business metrics (e.g., documents processed, queries answered).
Conclusion
The integration of LangChain with Cisco Crosswork Workflow Manager demonstrates a powerful pattern for deploying durable and scalable AI/ML workflows. By combining LangChain's rich AI capabilities with CWM's orchestration, we have created a solution that is both powerful and durable.
This approach enables organizations to:
- Leverage existing AI investments through standardized adapters.
- Scale confidently with proven enterprise workflow patterns.
- Maintain reliability through robust error handling and monitoring.
- Iterate quickly with declarative, low-code workflow configurations.
As AI continues to transform business processes, the combination of specialized AI frameworks with enterprise workflow orchestration platforms will become increasingly critical for organizations seeking to deploy AI at scale.
