vault-ai
Vault-AI is a React and Go application that lets users upload documents (PDFs, text, EPUB, etc.) and ask questions about their contents using OpenAI embeddings and Pinecone vector search. It provides cited answers with source filenames and text snippets, creating a personal knowledge base with long-term memory.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Repository | pashpashpash/vault-ai |
| Owner | pashpashpash |
| Primary language | JavaScript |
| License | MIT — OSI-approved |
| Stars | 3.4k |
| Forks | 297 |
| Open issues | 50 |
| Latest release | Unknown |
| Last updated | 2025-07-08 |
| Source | https://github.com/pashpashpash/vault-ai |
What vault-ai is
The system combines a Go backend server with OpenAI's embedding API and Pinecone vector database for semantic search. Documents are chunked, embedded via OpenAI, stored in Pinecone with metadata, and queries are answered by retrieving relevant context and feeding it to an LLM with token-limit awareness using the tiktoken library.
Get the vault-ai source
Clone the repository and explore it locally.
git clone https://github.com/pashpashpash/vault-ai.gitcd vault-ai# follow the project's README for install & configurationNeed it deployed, integrated, or customized instead? DEV.co ships production installs.
Best use cases
Implementation considerations
- Requires Node v19 and Go 1.18.9; poppler must be installed separately for PDF text extraction. Dependency versions are older and may need auditing.
- API keys for OpenAI and Pinecone must be stored in a `secret/` folder; ensure secure key management in production (e.g., environment variables, vault systems).
- Max file size defaults to 3 MB per file; total upload cap is 300 MB. Modify constants in fileupload.go if larger documents are needed.
- Pinecone vector size must be set to 1536 (OpenAI default); free tier now restricts namespaces, potentially limiting multi-tenant scenarios.
- Token counting via go-tiktoken ensures OpenAI prompt fits within limits; test with your typical document chunk sizes and question complexity.
When to avoid it — and what to weigh
- Real-Time Scalability Requirements — No release versioning and last update 2025-07-08 suggests limited active maintenance. For mission-critical, high-traffic deployments, stability and update cadence are unclear.
- Strict Cost Controls — Depends on external paid APIs (OpenAI, Pinecone). Per-request embedding and vector storage costs can accumulate quickly with large document volumes.
- Offline or Air-Gapped Environments — Requires live connections to OpenAI API and Pinecone. Not suitable for disconnected or highly regulated networks without significant redesign.
- Zero External Dependencies — Tightly coupled to OpenAI and Pinecone; limited flexibility to swap embedding models or vector stores without major code changes.
License & commercial use
Licensed under MIT (MIT License). Permissive open-source license allowing commercial use, modification, and distribution with minimal restrictions (requires attribution and includes license notice in distributed code).
MIT license permits commercial use. However, commercial viability depends on your ability to manage costs for OpenAI and Pinecone APIs, and to handle the project's maintenance status independently. No commercial support or SLA stated.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
API keys are stored in plain-text files under `secret/` folder; this is insecure for production—use environment variables or secret management tools. No authentication/authorization layer visible; multi-user deployments require custom auth. Document uploads are not validated for malicious content. Pinecone and OpenAI endpoints are external; ensure network policies and data residency requirements are met.
Alternatives to consider
LangChain + LlamaIndex
More modular frameworks supporting multiple LLMs (OpenAI, Anthropic, local), multiple vector stores (Pinecone, Qdrant, Weaviate), and active community. Steeper learning curve but greater flexibility.
Retrieval-Augmented Generation (RAG) with MLflow or Hugging Face
Cloud-managed or self-hosted alternatives with better versioning, monitoring, and enterprise support. No dependency lock-in to specific vector DB.
Pinecone Assistant or OpenAI Assistants API
Fully managed, reduces operational overhead. Simpler setup but less customization and higher per-query costs for some use cases.
Build on vault-ai with DEV.co software developers
Vault-AI offers a solid starting point for document-grounded question-answering. Evaluate your API costs, maintenance capacity, and authentication needs before production deployment.
Talk to DEV.coRelated on DEV.co
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vault-ai FAQ
Can I use this offline or without OpenAI?
What happens if my Pinecone index fills up or my API keys expire?
Does this support multi-user or multi-tenant deployments?
How do I upgrade or patch the project?
Custom software development services
DEV.co helps companies turn open-source tools like vault-ai into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your vector databases stack.
Ready to Build a Knowledge-Base Q&A System?
Vault-AI offers a solid starting point for document-grounded question-answering. Evaluate your API costs, maintenance capacity, and authentication needs before production deployment.