RAG Knowledge Base Prep — Documents Chunked, Tagged & Ready to Embed
Send your source documents. Get them chunked, cleaned, and metadata-tagged for ingestion into any vector database — Pinecone, Weaviate, Chroma, Qdrant, or pgvector.
What's in Your RAG Knowledge Base Package
Source documents transformed into vector-database-ready chunks with rich metadata for precise retrieval.
Smart chunking
Logical boundaries — never mid-sentence or mid-paragraph. Semantic coherence preserved in every chunk
Rich metadata
Source file, section headers, page numbers, content type, and custom tags for filtering
Configurable sizes
Chunk sizes optimised for your embedding model — 256, 512, or 1024 tokens with configurable overlap
Clean content
Headers, footers, page numbers, and formatting artefacts stripped. Pure content only
Ingestion-ready format
JSONL or CSV formatted for direct import into your vector database of choice
Embedding recommendations
Model suggestions, dimension guidance, and distance metric recommendations for your use case
“Our naive chunking was splitting mid-sentence and losing context. The semantically-chunked version improved our RAG retrieval accuracy from 68% to 91% on our eval set.”
RAG Knowledge Base Use Cases
Company knowledge bot
Internal docs, policies, and SOPs chunked and tagged for an AI assistant that answers employee questions with source citations.
Build this workflowProduct documentation search
API docs, tutorials, and changelog entries prepared for a developer-facing search experience with code-aware chunking.
Build this workflowLegal document retrieval
Contracts, regulations, and case law chunked by clause with metadata for jurisdiction, date, and document type filtering.
Build this workflowCustomer support knowledge base
Help articles, FAQ entries, and troubleshooting guides prepared for a support chatbot that retrieves relevant answers.
Build this workflowExample RAG Knowledge Base Output
Here's a sample of chunked and metadata-tagged content ready for vector database ingestion:
[
{
"chunk_id": "doc-001-chunk-003",
"content": "To reset your password, navigate to Settings > Security > Change Password. Enter your current password, then your new password twice. Passwords must be at least 12 characters with one uppercase letter and one number.",
"metadata": {
"source": "help-center/account-security.md",
"section": "Password Management",
"content_type": "how-to",
"tokens": 48,
"page": 3
}
},
{
"chunk_id": "doc-001-chunk-004",
"content": "If you've forgotten your password, click 'Forgot Password' on the login page. A reset link will be sent to your registered email. Links expire after 24 hours.",
"metadata": {
"source": "help-center/account-security.md",
"section": "Password Recovery",
"content_type": "how-to",
"tokens": 38,
"page": 3
}
}
]JSONL chunks with metadata — ready for Pinecone, Weaviate, or Chroma ingestion
From $20 AUD · Prototypes in ~90s
How to Get Your Knowledge Base Prepared
Send Your Documents
Upload your source documents or describe their structure. PDFs, Markdown, HTML, or plain text — we handle all formats.
Compare Chunking Approaches
Multiple AI agents chunk and tag your documents differently. Compare their chunking strategies, metadata schemas, and overlap approaches.
Ingest & Search
Pick the best prepared knowledge base, pay, and load into your vector database. Start getting relevant search results immediately.
Why Custom RAG Prep Beats Automated Chunking
Semantic Chunking
Naive chunking splits on character count. Our agents chunk on semantic boundaries — sections, topics, and logical units that retrieve better.
See Before You Pay
Review competing chunking approaches with quality scores before paying. Compare chunk quality, metadata richness, and retrieval relevance.
Quality-Scored by AI Judge
Every knowledge base is evaluated on chunking quality, metadata richness, completeness, and format compliance.
Any Vector Database
Output formatted for Pinecone, Weaviate, Chroma, Qdrant, Supabase pgvector, or custom implementations. One prep, any target.
RAG Knowledge Base Prep — Common Questions
Which vector databases do you support?
What document formats can you process?
How do you determine chunk sizes?
Do you handle overlapping chunks?
What metadata do you include?
Can I preview chunks before ingesting?
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