GPTBots seamlessly connects LLM with enterprise data and service capabilities, efficiently building AI Bot services. It can easily integrate AI Bot capabilities deeply into actual business operations, driving business growth and efficiency improvement through AI.
GPTBots Product Features
GPTBots product has the following advantages and features:
- Supports out-of-the-box mainstream commercial large models, open-source large models, professional domain models, and customized models with fine-tuning.
- Eliminates the need to invest significant effort in LLM deployment and fine-tuning, allowing developers to focus more on core enterprise business.
- Regardless of commercial or open-source models, it can quickly generate the data needed for model fine-tuning based on knowledge base data and user dialogue data.
- Supports various types of knowledge data such as doc/docx, pdf, txt, markdown, csv, xls/xlsx, web crawling, Q&A, etc.
- Utilizes different data parsing and segmentation schemes for different types of data to improve data quality and completeness.
- Supports a mixed search scheme of sparse vectors and dense vectors to improve knowledge retrieval accuracy.
- Supports the management, editing, and updating of knowledge documents in sliced dimensions.
- For specific domain requirements, developers can obtain excellent solutions through Plugins (e.g., investment analysis, output files, product recommendations, service reservations, etc.).
- Developers can seamlessly connect with enterprise data and service capabilities through plugins while ensuring the security of enterprise data.
- GPTBots not only provides official plugins but also supports third-party developers to publicly release plugins based on their own service capabilities.
- When facing complex requirements and issues, developers can use Flow to visualize the orchestration of multiple LLMs.
- Defines single-function, clearly defined "vertical LLMs" to improve quality and stability.
- Multiple "vertical LLMs" and "functional components" work serially or in parallel through Flow to solve complex problems.
- Chat records support quality scoring, keywords, and topic summarization, making it easier for developers to understand user interests.
- Supports summarizing, summarizing, and categorizing user questions to help developers understand high-frequency user questions and optimize and supplement relevant knowledge in a targeted manner.
- Bot training mode supports real-time correction of "dialogue content" to continuously train the Bot for better responsiveness.
How does GPTBots product solve the challenges of LLM landing in enterprises?
LLM Illusion Problem
The illusion of LLM is mainly related to the underlying architecture of the model and training data. Illusions can make enterprise LLM applications unreliable, untrustworthy, and even potentially harmful.
- Accurate knowledge supplementation of context.
- Bot training and LLM fine-tuning to correct the model.
- Designing reflection mechanisms and verification tools for LLM.
- Optimizing Prompt to limit the range of responses.
General LLM lacks domain knowledge
Due to the lack of domain knowledge, general LLMs cannot provide correct responses, making it difficult for enterprises to use LLM to solve business problems. Additionally, training models separately for various vertical scenarios is cost-prohibitive.
- Knowledge base supports precise knowledge retrieval.
- Easily import unstructured knowledge data.
- Supports connection and identification of structured data.
- Plugins bridge internal domain knowledge within the enterprise.
Single LLM cannot solve complex tasks in enterprise business scenarios
The complex reasoning ability of LLM is still weak and cannot effectively solve complex tasks in enterprise operations. Additionally, single-point single-thread tasks cannot meet the needs of actual business scenarios in enterprises.
- Break down complex problems into multiple branches.
- Flow supports the collaboration of multiple versions of LLM.
- LLM has capabilities such as long short-term memory, plugins, and knowledge base.
- Incorporate external feedback and information into the LLM response process.
LLM cannot solve complex tasks in enterprise business scenarios
LLM landing involves compliance, data, computing power, engineering, and algorithms. Any quality issues in any of these areas will significantly impact the application of LLM in scenarios, especially when using open-source models, leading to a sharp increase in costs for hardware and manpower.
- Provides a simple and efficient LLMOps platform.
- Solves the challenges of knowledge data loading and retrieval.
- Provides out-of-the-box AI Bot building capabilities.
- Rich and comprehensive API and SDK.
Enterprises lack talent reserves in the AI field
Enterprises need talent in the AI field with capabilities in data, algorithms, engineering, and business. For enterprises, the shortage of AI talent, slow cultivation, and high costs are significant challenges.
- Nearly zero-threshold use of GPTBots.
- Bot training and fine-tuning LLM capabilities for product operation personnel.
- No need for extensive AI domain knowledge; enterprise business personnel can also train and optimize the Bot.
- Developers can complete integration through API interfaces.