Enterprise RAG System

Problem Definition

A multinational corporation needs a unified solution to help employees quick-find information across thousands of internal documents, including policies, legal contracts, and product specs.

Key Requirements

• Accurate Retrieval: Precisions matters.
• Security: Respect document access permissions (ACLs).
• Scalability: Handle millions of documents.
• Multilingual: Content in 15+ languages.

System Design

The architecture consists of an ingestion pipeline (OCR, chunking), a hybrid retrieval system (Vector + Keyword), and a DSPy-powered generation layer.

Document Indexer

The indexer handles text extraction, language detection, and semantic chunking.

class DocumentIndexer(dspy.Module):
    def forward(self, document: Dict) -> List[DocumentChunk]:
        text = self._extract_text(document)
        language = self._detect_language(text)
        chunks = self._create_chunks(text, language)
        return [DocumentChunk(content=c, language=language) for c in chunks]

Hybrid Retrieval

Combining vector search for semantics and keyword search for specific terms is crucial for enterprise accuracy.

class HybridRetriever(dspy.Module):
    def forward(self, query):
        vec_res = self._vector_search(query)
        kw_res = self._keyword_search(query)
        return self._combine_and_rerank(vec_res, kw_res)

RAG Generator

The generation module synthesizes answers and verifies them against the source context to minimize hallucinations.

class RAGGenerator(dspy.Module):
    def __init__(self):
        self.generate = dspy.Predict(GenerateAnswerSignature)
        self.verify = dspy.ChainOfThought(VerifyAnswerSignature)

    def forward(self, question, context):
        ans = self.generate(question=question, context=context)
        final = self.verify(answer=ans.answer, context=context)
        return final