Tools: Designing a Scalable Knowledge Base for Large Language Models

1. System Architecture Overview ## Core Responsibilities ## 2. Data Cleaning and Normalization ## Required Processing ## Common Noise Sources ## 3. Semantic Chunking Strategy ## Chunking Goals ## Recommended Hierarchical Approach ## Chunk Size and Overlap ## Parent–Child Chunk Design ## 4. Metadata Schema Design ## Minimum Viable Metadata ## Enhanced Metadata (Recommended) ## Stable Chunk ID Strategy ## 5. Batch Embedding Architecture ## Suggested Data Model ## Key Engineering Practices ## Supporting Multiple Models ## 6. Retrieval Design: Hybrid Search and Reranking ## Recommended Retrieval Pipeline ## 7. Chunk Quality Monitoring ## 8. End-to-End Processing Pipeline ## Final Thoughts A Practical Engineering Guide to Cleaning, Semantic Chunking, Metadata, and Batch Embeddings Large Language Model (LLM) knowledge bases are often misunderstood as simply “vectorizing documents.” In reality, a production-grade knowledge system is a retrieval infrastructure that must be traceable, incremental, and measurable. This article walks through a practical engineering pipeline covering: The focus is not theory, but implementation decisions that work in real systems. Before implementation, define the boundaries of your pipeline. A robust LLM knowledge base usually consists of the following stages: A knowledge base is closer to a search engine than a simple storage system. The goal is not to “clean aggressively,” but to preserve structural signals. Preserve structural blocks: Avoid flattening everything into plain text. Structure improves both retrieval accuracy and traceability. Tables should ideally be converted into Markdown or key: value rows so that LLMs can interpret them correctly. Chunking is the most important factor affecting retrieval performance. A good chunk should be: Typical engineering defaults: Overlap prevents losing context when answers span boundaries. A highly effective production pattern: This significantly improves answer coherence. Metadata is not optional. It enables filtering, access control, versioning, and debugging. Each chunk should include: These fields enable advanced filtering and evaluation later. Chunk IDs must remain stable across re-processing. Only changed content should produce new IDs. Embedding pipelines must be idempotent, incremental, and observable. Embedding records must include: Allow multiple embeddings per chunk for gradual migration between models. Vector search alone is rarely sufficient. Lightweight reranker or LLM scoring Return source_uri + section_path + page Hybrid search dramatically improves precision for exact terms and technical names. Many production issues are caused by poor chunks rather than model failures. Common anti-patterns: Add a simple rule engine that tags chunks with quality_flags. A practical implementation roadmap: Designing an LLM knowledge base is less about models and more about information architecture. The biggest improvements usually come from: If you treat your knowledge base like a search system rather than a document dump, both retrieval accuracy and generation quality improve significantly. Templates let you quickly answer FAQs or store snippets for re-use. Are you sure you want to hide this comment? It will become hidden in your post, but will still be visible via the comment's permalink. Hide child comments as well For further actions, you may consider blocking this person and/or reporting abuse CODE_BLOCK: Ingest → Normalize → Chunk → Enrich → Embed → Index → Retrieve → Monitor Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: Ingest → Normalize → Chunk → Enrich → Embed → Index → Retrieve → Monitor CODE_BLOCK: Ingest → Normalize → Chunk → Enrich → Embed → Index → Retrieve → Monitor CODE_BLOCK: chunk_id = sha1(doc_id + doc_version + section_path + chunk_index + text_hash_prefix) Enter fullscreen mode Exit fullscreen mode CODE_BLOCK: chunk_id = sha1(doc_id + doc_version + section_path + chunk_index + text_hash_prefix) CODE_BLOCK: chunk_id = sha1(doc_id + doc_version + section_path + chunk_index + text_hash_prefix) - Data cleaning and normalization - Semantic chunking strategies - Metadata schema design - Batch embedding architecture - Retrieval and evaluation considerations - Ingest: PDFs, web pages, Markdown, databases, or internal docs - Normalize: Convert raw content into structured blocks - Chunk: Create retrieval-ready units - Enrich: Attach metadata and context - Embed: Generate vectors with version control - Index: Build hybrid search indexes - Serve: Retrieval + reranking + citation - Monitor: Evaluate retrieval quality continuously - Convert all content to UTF-8 - Normalize whitespace and line breaks - Remove duplicated navigation/footer content - Detect headings (H1/H2/H3 or numeric sections) - Preserve structural blocks: Paragraphs Lists Tables Code blocks - Code blocks - Code blocks - Web navigation bars and cookie banners - PDF headers and repeated page numbers - Hyphenated line breaks in scanned PDFs - Template content repeated across pages - Self-contained: understandable without large context - Traceable: linked back to its original location - Searchable: not too long or too fragmented - Structure-aware splitting (Preferred) - Split by document headings first - Merge paragraphs inside each section - Recursive splitting - Paragraph → Line → Sentence → Token boundary - Semantic boundary detection (Advanced) - Use topic shifts or embeddings to find natural breaks - FAQ or policies: 200–450 tokens, overlap 30–80 - Technical docs: 300–700 tokens, overlap 50–120 - Long reports or research: 400–900 tokens, overlap 80–150 - Child chunks: smaller pieces used for vector retrieval - Parent chunks: larger contextual sections passed to the LLM - Retrieve child chunks - Expand to parent chunks - Send parents to the model for generation - section_path - page_start / page_end - created_at / updated_at - hash (content checksum) - tenant/project - acl (access control) - doc_version - effective_date - entities (product names, systems, people) - content_type (faq, guide, spec, code) - quality_flags - doc_id, version, uri, title, checksum - chunk_id, doc_id, text, metadata_json, hash - chunk_id, model_name, dim, vector, text_hash - job_id, status, created_at - job_id, chunk_id, retry_count, error - Only embed chunks whose hash changed - Process in batches (32–256 chunks or token-limited) - Control concurrency to avoid rate limits - Implement exponential retry - Monitor throughput and failure rates - model_version - vector_dimension - normalized_flag - Hybrid retrieval: - Vector similarity - BM25 keyword search Metadata filtering: - Metadata filtering: - tenant/project - document type Reranking: - Lightweight reranker or LLM scoring Source citation: - Source citation: - Return source_uri + section_path + page - Metadata filtering: - Source citation: - Chunks shorter than 50 tokens - Chunks longer than 1200 tokens - Repeated template content - Missing title context - Duplicate sections occupying top results - Ingest documents and generate doc_id - Extract structured blocks - Remove noise and duplicates - Build parent chunks from sections - Generate child chunks with overlap - Attach metadata and hashes - Upsert into chunks table - Create embedding jobs for new/changed chunks - Batch embedding with workers - Build vector and keyword indexes - Run evaluation queries (golden dataset) - Better chunk structure - Strong metadata design - Incremental embedding pipelines - Hybrid retrieval strategies