- Limited context windowLLM cannot understand and analyze large amounts of information.
- Knowledge is scattered and difficult to integrateThe knowledge information within the organization is often scattered in various systems or documents, and it is difficult to coordinate and manage.
- LLM lacks specific domain knowledgeLLM does not have the knowledge reserve of a specific team, organization, enterprise or industry, and cannot respond.
- Knowledge online editingNeed to modify the uploaded knowledge? Just save it directly after online editing, no need to modify it offline and resubmit it.
- Web page knowledge updateWeb page information often changes? One click can re-crawl the page information and update it to the knowledge base, no need to re-add.
- Knowledge slice managementIt provides a flexible slicing method, which allows knowledge fragments to be managed more reasonably. Support adding, modifying and deleting knowledge slices, making knowledge management more detailed and flexible.
- High-quality knowledge embedding modelAccess to the world's top embedding model, making semantic matching of knowledge more accurate and reasonable.
- Mixed retrieval modeExclusive development of "dense vector + sparse vector" mixed retrieval mode, taking into account "vector retrieval" and traditional "keyword retrieval", allowing retrieval to get more accurate recall results in various scenarios.
- Vector retrieval testNot sure how the knowledge retrieval effect is? Direct retrieval test, observe the recalled knowledge slices and relevance, intuitively understand the retrieval results, and optimize the Bot configuration and knowledge data reasonably based on this.
The knowledge form that best conforms to the RAG architecture knowledge recall and LLM fine-tuning, supports online adding and editing, and also supports intelligently converting articles into multiple "Q&A".
The structured "Q&A" storage method is more conducive to accurate recall of knowledge.
Use real chat history records, provide feedback, and precipitate the knowledge in the chat record into "Q&A" to improve Bot, so that Bot can improve in each interaction.
The "Q&A" structured knowledge can be directly used in LLM fine-tuning, which makes the fine-tuning work easier.