Multi-Tenant Hybrid Search RAG for Developer Documentation & Codebases
Client identifiers anonymized under strict Non-Disclosure Agreements (NDAs). Performance metrics reflect architectural benchmark simulations.
About the Project
This project was engineered for a developer tooling enterprise that provides APIs, SDKs, and developer infrastructure to over 40,000 engineering teams globally. Developers consult millions of pages of technical documentation, code snippets, interface definitions, and version migration guides daily.
The primary objective was to replace a frustrating keyword search bar with an intelligent, conversational retrieval assistant capable of resolving nuanced technical questions across multiple programming languages and API versions without leaking tenant-private documentation or returning deprecated syntax.
Challenges We Faced
1. The Lexical vs. Semantic Retrieval Gap in Source Code
Standard dense vector embeddings repeatedly failed on exact code identifiers, method signatures, and error codes (such as `ERR_SOCKET_TIMEOUT` or `client.auth.v2.verify()`), because vector cosine similarity prioritizes conceptual proximity over token precision. Conversely, traditional BM25 keyword matching failed completely on natural language conceptual inquiries like 'how do I handle expired JWT tokens in a background worker?'
2. Arbitrary Token Chunking Ruining Abstract Syntax Trees (AST)
Splitting technical documentation and source code files using standard fixed-token or character-count windows frequently sliced methods in half. Variable declarations were severed from their execution logic, leading the generation model to hallucinate missing parameters and fabricate broken syntax.
3. Multi-Tenant ACLs and API Version Pollution
Enterprise clients hosted custom private plugins and proprietary SDK extensions that could never be exposed to public search indexes or other tenants. Furthermore, returning v1 syntax to a developer building against v3 APIs caused immediate build failures.
Solution Architecture
Acadify engineered a hybrid search architecture combining sparse lexical retrieval with dense vector representation, delivered as part of our enterprise AI development services practice.
Instead of treating code as plain text, we implemented a custom semantic chunking engine using Tree-Sitter to parse source files directly into their Abstract Syntax Trees (AST). Each chunk preserved its enclosing class hierarchy, method signature, parameter types, and docstring context.
- Hybrid Reciprocal Rank Fusion (RRF): Deployed Elasticsearch for BM25 lexical precision alongside Qdrant running
bge-large-en-v1.5dense embeddings. Retrieved candidates from both pipelines were fused and re-ranked using a cross-encoder model (bge-reranker-large), ensuring exact identifiers and conceptual questions were handled reliably. - AST-Aware Semantic Chunking: Parsed source files using Tree-Sitter grammars across TypeScript, Python, and Go, ensuring function boundaries were never split arbitrarily and variable scopes remained intact.
- Namespace Isolation & Mandatory Pre-Filtering: Vector indices were partitioned by tenant ID and documentation version. All incoming queries enforced cryptographic ACL checks and strict version filtering prior to vector similarity calculations, preventing cross-tenant data leakage and version confusion.
Why Acadify's Engineering Approach Fit the Project
Standard RAG implementations break when applied to production codebases because they ignore the formal grammar of programming languages. Acadify's team brings deep compiler engineering and distributed search experience, replacing naive character splitters with AST-aware parsers and reciprocal rank fusion to deliver accurate, syntax-validated technical answers.
System Architecture & Tech Stack
Related Acadify Solution Services
This case study demonstrates capabilities from Acadify Solution's AI Development practice, specifically multi-tenant RAG architecture with tenant data isolation. For teams building AI SaaS products, see our Build AI Product solution.