Documentation Ingestion
Documentation ingestion is the pipeline that converts raw documentation (web pages, text files, PDFs, API docs) into a searchable, AI-ready format. The process typically involves fetching content, cleaning and chunking text, generating vector embeddings, and storing the indexed data for fast retrieval during customer queries.
The ingestion pipeline
A typical ingestion pipeline: (1) Fetch or receive raw content from URLs, files, or text input. (2) Parse and clean the content, removing navigation, headers, and irrelevant markup. (3) Chunk the content into semantically meaningful segments. (4) Generate vector embeddings for each chunk. (5) Store embeddings in a vector database for similarity search.
Keeping content fresh
Documentation changes over time — features get added, pricing updates, and processes evolve. Stale indexed content leads to incorrect AI answers. The best approach is to re-ingest documentation regularly or trigger re-ingestion when source content changes, ensuring the AI always has access to current information.
How EchoSDK handles ingestion
EchoSDK accepts documentation via URL (crawls and parses the page) or direct text input through the API. Content is automatically chunked, embedded, and indexed in Firestore Vector Search. Re-ingesting a URL updates the existing index with fresh content. The entire process takes seconds, not hours.
Related terms
Knowledge Base
A structured collection of documentation, FAQs, and guides that serves as the source of truth for customer support — both for human agents and AI systems.
Vector Embeddings
Numerical representations of text that capture semantic meaning, enabling AI systems to find relevant content through similarity search.
Retrieval-Augmented Generation (RAG)
An AI technique that combines a language model with a retrieval system to generate answers grounded in specific documents or data sources.