Knowledge Base QA Agent
企业知识库问答 Agent
Point it at your docs and get a customer-facing Q&A agent that answers from your own knowledge base — with sources, confidence scores and human handoff.
它能做什么
Knowledge Base QA Agent turns your existing internal or public documentation into an always-on Q&A assistant built on a RAG pipeline (Dify plus a self-hosted vector store). You upload PDFs, Notion exports, markdown and FAQ spreadsheets; the agent chunks, embeds and indexes them, then answers visitor questions strictly from that corpus with a source citation and a confidence score. When confidence is low or the visitor asks for a human, it hands off cleanly to your support inbox or Slack. It runs against your own LLM key and your own data — no third party trains on your documents — and it is delivered as a working stack with ingestion scripts, API endpoints and an embeddable chat widget rather than a diagram. A first index of a few hundred documents is measured in tens of minutes.
功能
- ✓ Ingest PDF, DOCX, Markdown, Notion exports, HTML and CSV/FAQ spreadsheets into a searchable vector index
- ✓ RAG answers grounded strictly in your documents — every reply carries the source excerpt and document link
- ✓ Confidence scoring with a configurable threshold; below it the agent says "I'm not sure" instead of hallucinating
- ✓ Built on Dify + a self-hosted vector store (bge-m3 embeddings, bge-reranker) — your documents never leave your control
- ✓ Human-handoff that routes low-confidence or explicit "talk to a person" requests to email or Slack
- ✓ Multi-language answers using your own LLM key (DeepSeek, OpenAI-compatible or Anthropic models)
- ✓ Embeddable chat widget plus a REST API endpoint so the same brain powers your website, app or Slack
- ✓ Scheduled re-indexing script so answers track document updates without manual work
- ✓ Multiple independent corpora: index a public help center, an API docs site and internal HR policies as separate knowledge bases, each with its own access rules and confidence threshold
- ✓ Answer feedback loop: thumbs up/down on every reply is logged to a local table so you can see which topics the bot gets wrong and fix the underlying source doc instead of guessing
- ✓ Query analytics and cost view: questions per day, average tokens per answer and last re-index time shown in the dashboard so you know what the assistant costs and whether the index is fresh
- ✓ Guardrail layer: off-topic, abusive or legally-sensitive queries are declined or flagged for review via the system prompt instead of being answered from thin air
适用场景
- ▸ A SaaS support team cuts repetitive ticket volume by letting customers self-serve answers from the help center, with human takeover for edge cases
- ▸ A hardware startup puts its manuals and spec sheets behind a Q&A widget so pre-sales compatibility questions answer themselves
- ▸ An HR or operations team points the agent at policy documents and gives employees a private bot for "how do I expense X" questions
- ▸ A consulting firm indexes its engagement playbooks and lets junior staff query methodology instead of emailing seniors
- ▸ A DTC e-commerce brand indexes shipping, returns and FAQ pages and deflects roughly 60% of repetitive after-sales messages to the widget, with human handoff reserved for refund edge cases
- ▸ A SaaS onboarding team connects the agent to its changelog plus docs and lets the in-app assistant answer "what changed in v2.4 and how do I migrate" with a source link to the actual release note
交付内容
- 📦 RAG pipeline built on Dify with self-hosted vector store and reranker
- 📦 Document ingestion scripts (PDF, DOCX, Markdown, Notion export, CSV)
- 📦 Embeddable chat widget and REST API endpoint
- 📦 config.yaml for model, confidence threshold and human-handoff routing
- 📦 README.md — illustrated setup guide with version pins
- 📦 demo/ — sample knowledge base with working Q&A examples
- 📦 1 year of updates
FAQ
Does it really answer only from my documents?
Yes — retrieval is restricted to your indexed corpus, and every answer shows the source chunk it came from. This is a RAG setup, not a raw chat model, so out-of-corpus questions are declined or flagged as low confidence.
Which model and vector database do I need?
It is built around Dify with a self-hosted vector store (bge-m3 embeddings, bge-reranker for ranking) and calls your own LLM key for generation. The setup guide gives exact version pins so it works on a single Docker host.
I am not technical. Can I still run this?
If you can run docker compose and fill in a config file, you can get a first index live — the README walks it step by step, and the demo includes a ready-to-import sample knowledge base you can test against.
How is this different from pasting my docs into a chat AI?
Pasting loses freshness, sources and scale. This keeps a versioned index, lets you re-index on schedule, adds source citations and confidence gating, handles human handoff, and can be embedded in your product rather than used one-off.
What does it cost — is this a subscription?
One-time $99 with 12 months of updates, no per-seat or per-resolution fees. The ongoing cost is your own LLM API usage (typically a fraction of a cent per short answer) plus the electricity for a Docker host you likely already run.
What is your refund and update policy?
14-day refund if the stack does not work as described when you follow the README. Updates for 12 months cover Dify and embedding-model version bumps, ingestion-script fixes and new file-format converters.
What are the hardware and deployment requirements?
A single Docker host with 8 GB of RAM and a few GB of disk is enough for a few-hundred-document corpus; you can start smaller and scale the vector store up as the corpus grows. It runs on any Linux VM, macOS or Windows with Docker, on-premises or on a $10–$20/month VPS.
Can I customize the answers, add my own formats, and get help if something breaks?
Yes — the system prompt, chunk size and confidence threshold live in config.yaml, and you can add custom ingestion for unusual file types via the documented loader interface. If an ingestion run fails or an answer looks wrong, email support with the log excerpt and the source file and you will get a specific fix.
只需你的 API key
填上 config.yaml 里的 key,15 分钟跑通第一个结果
一次性买断 · 含 1 年更新 · 14 天退款保障