Chat with your enterprise data using LLM vs knowledge-gpt
Side-by-side comparison of two AI agent tools
Short answer
- Pick Chat with your enterprise data using LLM for: open-source sample for chatting with uploaded enterprise data using Azure OpenAI and vector search. Pick knowledge-gpt for: accurate answers and instant citations for your documents.
From GitHub data refreshed daily.
Chat with your enterprise data using LLMopen-source
Open-source sample for chatting with uploaded enterprise data using Azure OpenAI and vector search
knowledge-gptopen-source
Accurate answers and instant citations for your documents.
Metrics
| Chat with your enterprise data using LLM | knowledge-gpt | |
|---|---|---|
| Stars | 865 | 1.6k |
| Star velocity /mo | -0.4736842105263158 | -3.7894736842105265 |
| Commits (90d) | 0 | 0 |
| Releases (6m) | 0 | 0 |
| Overall score | 0.12103260760690508 | 0.10861700850703523 |
Pros
- +Supports multiple vector stores (Pinecone, Redis, Azure Cognitive Search) providing flexibility in deployment options
- +Includes comprehensive evaluation framework with Prompt Flow integration and metrics like groundedness and Ada similarity
- +Active development with regular updates and refactoring to improve core functionality and remove complexity
- +Provides instant citations with answers, ensuring transparency and verifiability of information sources
- +Easy local deployment with both Poetry and Docker installation options, giving users full control over their data
- +Built on established frameworks (Streamlit + Langchain) with active development and clear roadmap for advanced features
Cons
- -Designed as a sample application rather than production-ready solution, requiring additional development for enterprise deployment
- -Specifically tied to Azure OpenAI Service, limiting flexibility in LLM provider choice
- -Has undergone multiple refactoring cycles that removed features, suggesting potential instability in feature set
- -Requires paid OpenAI API key for optimal performance and to avoid rate limits
- -Limited to 25MB file upload size in the hosted version, which may restrict use with larger documents
- -Currently supports limited document formats, though expansion is planned on the roadmap
Use Cases
- •Enterprise document Q&A systems where employees need to query internal knowledge bases using natural language
- •Internal chatbots for customer support teams to quickly access company policies and procedures
- •Research and development teams building custom RAG applications for proprietary data analysis
- •Academic research where scholars need to quickly find and cite specific information from multiple research papers
- •Legal document review where attorneys need to extract relevant clauses and precedents with exact citations
- •Corporate knowledge management where teams need to query internal documentation and reports for specific information
FAQ
- Which is more popular, Chat with your enterprise data using LLM or knowledge-gpt?
- knowledge-gpt has more GitHub stars (1,628 vs 865).
- Which is more actively developed, Chat with your enterprise data using LLM or knowledge-gpt?
- Chat with your enterprise data using LLM had more commits in the last 90 days (0 vs 0).
- Should I use Chat with your enterprise data using LLM or knowledge-gpt?
- Compare their capabilities, limitations and "best for" notes above. Both are open source, so trying each on a small task is the fastest way to decide.