Internal Documentation Chat with Live Token Streams

An AI chat over internal documentation should answer questions from approved company sources, show where its answers came from, and display replies as they arrive. This walkthrough builds a retrieval-augmented generation application with permission-aware search and live token streaming, so employees can start reading without waiting for the complete response.

§1 Step 1: Define the documentation chat boundary

Start with one useful collection, such as engineering runbooks or employee onboarding guides. Choose questions that collection can answer, identify who may access it, and define what happens when the documentation does not contain an answer.

Keep the first architecture simple: a browser chat interface calls your backend, the backend retrieves authorized passages, and a model generates a grounded answer. Your backend streams the response back to the browser. Keep API credentials and document access checks on the server.

§2 Step 2: Ingest documents with source metadata

Export documents through approved connectors, then normalize their text while preserving headings, lists, and meaningful structure. Split long documents into overlapping passages, using section boundaries where possible. Start around 400–800 tokens per passage and tune against actual questions.

Attach metadata to every passage so retrieval can enforce access controls and answers can cite recognizable sources.

• – Store the document identifier, title, and section heading.
• – Record the document version and last-updated timestamp.
• – Preserve tenant, team, and user access rules.
• – Track deletions and permission changes for prompt index updates.

§3 Step 3: Retrieve only passages the employee can access

Create embeddings with a suitable embedding service and store the resulting vectors alongside passage metadata. For each question, authenticate the employee and restrict retrieval to their authorized documents. Never retrieve unrestricted content and rely on the model to hide sensitive passages.

Combine semantic search with keyword search when your documentation contains error codes, product identifiers, or exact configuration names. Rerank candidates and send only the most relevant passages to generation. Treat chunk size, retrieval count, and relevance thresholds as settings to evaluate, not universal constants.

§4 Step 4: Connect generation through Fluxio

Fluxio fits at the generation layer: it is a streaming-first, pay-per-token AI API relay that provides access to multiple model providers through one key. With an OpenAI-style client, configure the base URL as https://api.fluxioapi.com/v1, keep the key in server-side secrets, and enable streaming.

You could evaluate Claude Haiku 4.5 and Gemini 2.5 Flash on the same internal question set. Confirm the available model identifiers and supported streaming features before implementation. Choose based on grounded answer quality, time to first token, and total cost rather than model reputation alone.

§5 Step 5: Require grounded answers and source citations

Build the generation request from clear instructions, the employee’s question, and numbered retrieved passages. Tell the model to answer from those passages, cite their identifiers, and acknowledge missing evidence. Treat document text as untrusted reference material, never as instructions that can override application rules.

Resolve citation identifiers to authorized document records on your backend. Do not trust generated titles or destinations without validation. If retrieval finds no useful evidence, return a clear insufficient-information response instead of encouraging a plausible guess.

§6 Step 6: Deliver token deltas through SSE

Use a streaming HTTP response with Server-Sent Events formatting between your backend and browser. For a question submitted by POST, the browser can read the response through a streaming fetch request and parse complete SSE events across network chunks.

Emit separate events for source metadata, text deltas, completion, and errors. Append text deltas to the visible answer, but render generated content safely. Cancel the upstream request when the employee stops generation or disconnects. If streaming fails midway, mark the answer incomplete rather than presenting it as finished.

§7 Step 7: Test retrieval, latency, and permission changes

Evaluate answer correctness, citation support, retrieval quality, time to first token, completion latency, and cost per question. Include outdated documents, missing answers, malicious document instructions, and cross-team access attempts. Test revoked permissions and interrupted streams before expanding the rollout. Streaming improves perceived responsiveness; it does not replace reliable retrieval or security.

Sign up for Fluxio to build your streaming documentation chat.