Project Overview
An enterprise-grade RAG (Retrieval-Augmented Generation) knowledge base system built for a FinTech company. The system integrates over 50,000 internal technical documents, product manuals, and regulatory specifications, enabling employees to quickly obtain precise information through natural language conversation.
After deployment, average employee query time decreased from 15 minutes to 30 seconds, and customer service response accuracy improved by 40%.
Core Technologies
1. Intelligent Document Processing
The system supports multiple document formats (PDF, Word, Excel, PowerPoint) with automated:
- Document content extraction and cleansing
- Intelligent segmentation and semantic chunking
- Vector embedding generation
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
def process_document(file_path: str):
# Load document
loader = get_loader_for_file(file_path)
documents = loader.load()
# Intelligent chunking
splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
separators=["\n\n", "\n", ".", ",", " "]
)
chunks = splitter.split_documents(documents)
# Generate vector embeddings
embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
vectors = embeddings.embed_documents([c.page_content for c in chunks])
return chunks, vectors
2. Hybrid Search Architecture
Combining vector search and keyword search to improve retrieval accuracy:
- Vector search: Pinecone for semantic similarity matching
- Keyword search: Elasticsearch for exact term matching
- Re-ranking: Cross-Encoder for result re-ranking
3. Source Citation Tracking
Every answer includes original document sources, ensuring information is traceable and verifiable.
Technical Architecture
+----------------------------------------------------------+
| Frontend Interface (React) |
+----------------------------------------------------------+
| API Gateway |
+----------------------------------------------------------+
| +---------------+ +---------------+ +---------------+ |
| | Document | | RAG Service | | Admin | |
| | Processing | | | | Dashboard | |
| +---------------+ +---------------+ +---------------+ |
+----------------------------------------------------------+
| +---------------+ +---------------+ +---------------+ |
| | Pinecone | | PostgreSQL | | Redis | |
| | Vector DB | | Relational DB | | Cache | |
| +---------------+ +---------------+ +---------------+ |
+----------------------------------------------------------+
Project Results
- Query efficiency: Average response time < 3 seconds
- Accuracy: Answer accuracy rate of 92%
- Usage volume: 5,000+ daily queries
- Satisfaction: Employee satisfaction score 4.6/5
Client Testimonial
"This system has dramatically improved our team's productivity. New employee onboarding time has been cut in half." — IT Department Head