Humata
ProductivitySearch, summarize and compare uploaded documents with source-linked answers. Humata adds team-oriented document access and page-based plans for ongoing knowledge work.
Questions across filesCited passages
QANYTHING / ASK QUESTIONS WITH YOUR DOCUMENTS IN VIEW
QAnything
NetEase Youdao’s self-hosted knowledge-base question-answering project, combining document ingestion, retrieval and answer generation. It offers a free software route for teams prepared to manage models, hardware and document quality.
Official QAnything architecture diagram from the NetEase Youdao repository, illustrating the retrieval and answering workflowExplore the image ↓
What you can do with it
What this tool is for
QAnything is a document question-answering system rather than a standalone general chatbot. Its purpose is to ingest a collection, retrieve relevant passages and use those passages to support an answer. The project is particularly relevant when the source material belongs to a specific organization or topic and should remain under the operator’s control.
A useful deployment depends on more than the language model. Document parsing, chunking, retrieval and reranking all influence which evidence reaches the answer stage. This means the first evaluation should inspect source matches as carefully as the generated prose. A fluent answer can still be based on the wrong manual, an obsolete policy or an incomplete passage.
01
Internal product manuals
02
Research reading collection
03
Team onboarding reference
04
Compare policy versions
A closer look at the product
Official product image. Click to inspect the details.
Product interface and examples
Key features, practical tasks
Capabilities in practice
- Document ingestion: The project lists formats including PDF, Word, presentations, spreadsheets, text, Markdown, email, images and web links. Start with the formats that dominate your collection and review parsing quality before assuming every attachment is equally searchable.
- Two-stage retrieval: QAnything combines retrieval with a reranking stage to improve which passages are selected. This is a useful architecture for knowledge questions, but the operator should still inspect retrieved evidence and tune the collection rather than treating the ranking as infallible.
- Chinese and English questions: The project describes cross-language Chinese and English question answering. Test your actual terminology and acronyms, since a technically supported language does not guarantee that specialized vocabulary is interpreted correctly.
- Multiple knowledge bases: Separate collections can be selected for a question. Organize them by a meaningful boundary such as document ownership or product version, and avoid combining sources that users are not permitted to read together.
- Local deployment: The documented deployment routes allow a locally managed system, with offline capabilities depending on the route and prepared resources. A local interface can still call an external model if configured that way, so inspect the model endpoint.
- Model flexibility: The project documents different model choices and API-compatible integrations. Choose a model that fits the hardware, language and license requirements, and distinguish a free application from a paid inference endpoint used behind it.
- API integration: The API guide describes operations for knowledge-base and file workflows as well as question answering. An integration should track ingestion state and errors instead of assuming that an upload is immediately available for reliable retrieval.
Get started, step by step
- 01
Choose a deployment route
- 02
Create a clean collection
- 03
Test answerable and missing cases
- 04
Connect users carefully
A practical first workflow
A practical first workflow
- Choose a deployment route. Read the current Python and Docker comparison and select the route matching your platform and workload. Confirm memory, acceleration and model requirements before downloading large resources.
- Create a clean collection. Import a small set of permitted documents with clear titles and versions. Wait for ingestion to complete and inspect failed files or unusual parsing output before asking evaluation questions.
- Test answerable and missing cases. Ask questions with known answers, then include a question the collection cannot answer. Review retrieved passages and require uncertainty when the evidence is insufficient.
- Connect users carefully. Expose the application only to the intended audience, protect the service and maintain document updates. Add an API integration after the ingestion and retrieval behavior are understood.
Example prompt or task: Answer only from the selected product manual. Identify the relevant source passage, preserve version-specific conditions, and say when the documents do not contain enough information.
This is a proposed answer policy. Enforce document access in the application rather than relying only on prompt wording.
Find the right plan for you
Free software
Explore official plans ↗Free software Local infrastructure and model costs apply
Python and Docker routes have different platform, concurrency and offline capabilities. Follow the guide for the chosen version.
What is free and what can cost extra
The application code is available under Apache 2.0. A local deployment with suitable local models can avoid a mandatory hosted AI subscription, but the operator still pays for hardware, storage, electricity and maintenance. External model APIs can introduce usage charges.
Model licenses are separate from the application license. The repository explicitly points users to the relevant model terms for commercial use. Hosted demonstrations or commercial customization should not be assumed to share the same unlimited free entitlement as downloading the software.
A few things worth knowing
Frequently asked questions
Is QAnything free software?
The application is released under Apache 2.0.
Can it answer from local documents?
Yes, that is its central use case.
Does local installation guarantee no external calls?
No. Check whether the configured model or another component uses an external endpoint.
Does it support Chinese questions?
The project documents Chinese and English cross-language Q&A.
Are Python and Docker deployments identical?
No. The guide lists differences in platforms, concurrency and offline use.
Are model licenses included automatically?
No. Review the license of each selected model separately.
Can every generated answer be trusted?
No. Inspect the retrieved evidence, document version and unsupported claims.
The details behind your decision
Explore platforms, inputs and outputs, licensing, and access requirements.
Four useful starting projects
Four useful starting projects
- Internal product manuals: Build a collection for one product version and ask practical support questions. Keep the source manual visible so an operator can verify the answer before sending it to a customer.
- Research reading collection: Group a small set of papers around a specific question. Ask for the evidence supporting each conclusion and distinguish what a paper reports from what the assistant infers across several sources.
- Team onboarding reference: Use approved onboarding documents for routine questions. Exclude personal employee records and review whether each intended user has access to all documents in the selected knowledge base.
- Compare policy versions: Keep old and current versions clearly labeled and ask a narrow comparison question. Verify dates and exceptions against the documents instead of letting the system silently blend incompatible rules.
Platforms, inputs, outputs and setup
Platforms, inputs, outputs and setup
The official guide distinguishes a Python route aimed at trying features from a Docker route with different production, concurrency and offline characteristics. CPU and Apple Silicon support also differ by route; consult the comparison rather than applying one hardware claim to every version.
Retrieval quality depends on document quality and collection design. Keep obsolete files out of the default collection, use consistent names and review whether tables or scanned pages were extracted correctly. A larger collection can make governance more important even when the search architecture is designed to scale.
Data, permissions and licensing
Data, permissions and licensing
A private knowledge base needs actual access controls, secure deployment and a clear retention policy. Keep source documents, vector indexes and logs within the intended boundary, and review whether questions themselves contain confidential information.
Retrieved document text can include irrelevant or malicious instructions. Treat it as source material, not authority to run commands or disclose other files. Any agent or external action connected to the Q&A system needs its own permissions and review rules.
Sources, documentation and help
Official sources and review date
Official sources and review date
Reviewed October 3, 2026. Product facts come from the official sources below. Suggested projects, prompts and review methods are FindGoodAI editorial guidance, not measured performance results.
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