Methods and practices for building a privatized knowledge base based on large language models and localized documents This topic covers the application of artificial intelligence technology to create an intelligent, efficient, and personalized knowledge base in the modern enterprise environment. A private knowledge base can help businesses improve productivity, optimize customer service, reduce costs, and more. Combining large language models (such as OpenAI's GPT-3) and localized documentation, enterprises can achieve targeted and strongly relevant knowledge base services.
First, it is very important to understand the Large Language Model (LLM). In recent years, large-scale pre-trained models have shown amazing performance in many natural language processing tasks. The GPT-3 is one of the well-known LLMs that has proven powerful in generative, classification, translation, and other NLP tasks. In this topic of ours, we can use LLM, such as GPT-3, to build a private knowledge base.
Second, localized documentation plays a key role in establishing a private knowledge base. Enterprises need to analyze internal data, expertise, and experience and turn it into quantifiable vector data. This can be achieved through document vectorization, data slicing, and storage. Such technical practices include: loading and reading local files, text segmentation, text vectorization, and combining algorithms such as cosine similarity to calculate the similarity between the problem and the content of the knowledge base.
In terms of specific operations, feature extraction using techniques such as NLP (such as TF-IDF, word2vec, or pre-trained language models) is used to complete text vectorization. These vectors can be stored in databases such as Milvus, Chroma, etc. for subsequent retrieval and computation. After also converting the query question into a semantic vector, find out the text related to the problem according to the similarity calculation.
When the text relevant to the question is identified, we can submit these fragments to LLM for answering along with the question. This requires prompt authoring to combine questions and related knowledge base text into a single input so that LLM can better provide accurate answers.
Through such methods and practices, companies can successfully build a privatization knowledge base using big language models and localized documentation. Some possible application scenarios include intelligent customer service, internal knowledge bases, and industry-specific knowledge bases (such as medical, financial, legal, etc.).
However, there are still many challenges in this area. For example, analyze complex document structures (such as charts, tables, chapters, etc.), ensure the accuracy of text similarity calculation, and efficiently use LLM to complete Q&A tasks. At the same time, strict adherence to data privacy and security regulations is also a point to note when building a private knowledge base.
In summary, there is great potential to build a privatization knowledge base based on large language models and localized documents. With the development of artificial intelligence and natural language processing technology, we can expect more relevant methods and practices to emerge to further enhance the intelligence and value of private knowledge bases.