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In the age of digital transformation, large language models (LLMs) are opening up new ways of analyzing huge volumes of text stored in databases. These advanced AI systems deliver on the promise of gaining deep insights and competitive advantages from the wealth of data. However, they reach their limits due to the so-called token limitation, a technical restriction on the amount of data that can be processed. This becomes a particularly challenging hurdle when trying to analyze millions of text documents in depth or gain new insights:
Suppose a company wants to analyze 1,000,000 text documents. If special methods are not used, it faces major problems: Due to token limitation, an LLM can only analyze parts of the documents per run, which leads to a loss of information. Without a breakdown into manageable units and without a semantically intelligent database, there is a considerable loss of context. Documents have to be viewed in isolation, which makes it difficult to gain deeper insights. The analysis is also extremely time-consuming and resource-intensive, as each document has to be processed in full individually.
Chunking & vector data: easily analyze 1,000,000+ documents
An effective method of circumventing this limitation is the combination of smart chunking and the use of vector databases. By breaking down complex texts into smaller sections that can be handled by LLMs (chunking), it is possible to analyze large volumes of data without the restrictions imposed by token limits. In addition, vector databases make it much easier to access and analyze relevant information thanks to their ability to process and query semantic vector representations quickly and efficiently. This combination significantly increases the processing capacity and precision of LLMs and opens up the possibility of using the full power of the technology to gain valuable insights from the flood of data.
When analyzing large amounts of data, such as 1,000,000 text documents, the analysis process changes significantly:
- Efficient data processing:Splitting documents into smaller units (chunking) makes them easier for LLMs to process, as token limitations are bypassed.
- Advanced contextualization: Vector databases enable a deeper context analysis by quickly assigning semantically similar text parts. This significantly improves the understanding and classification of information.
- Time efficiency and scalability: The documents are broken down into smaller parts and information is retrieved efficiently using vector databases. This significantly speeds up processing, optimizes analysis and saves resources.
Real-world examples
A private equity fund uses LLMs to check the compliance of its extensive and transnational contract database. The challenge lies in the enormous amount of data and the need to efficiently identify specific regulatory requirements in different countries.
- Chunking application: Before the analysis, all documents are divided into thematically relevant sections. This enables the LLM to apply its analysis skills specifically to relevant text segments and significantly improve the accuracy of the results.
- Vector database integration: Relevant sections and legal provisions are stored in the vector database. The LLM uses these to retrieve the most relevant legal texts and compliance requirements for specific legal issues.
The results are a much more efficient and in-depth analysis of compliance, minimizing regulatory risks and facilitating adaptation to international laws.
A market research department uses LLMs to derive trends and patterns from millions of consumer feedbacks, market reports and social media posts.
- Chunking application: Splitting the data into smaller, thematically focused segments allows LLM to work more precisely and in a controlled context, improving the accuracy of trend analysis.
- Vector database integration: By storing thematic vectors from the analyzed text chunks in the vector database, LLM can consistently and efficiently track relevant topics and trends across a comprehensive and diverse data set.
This strategy enables the company to react quickly to changing market conditions and develop customized marketing strategies based on in-depth, data-driven insights.
In both cases, chunking and vector databases prove to be indispensable tools for fully exploiting the strengths of LLMs. Through these techniques, companies can increase the power of AI in text analytics, allowing them to gain deeper insights and make more accurate decisions.
Efficiently manage information floods with AI
In the age of information overload, it is more important than ever for companies not only to manage their data, but also to use it intelligently. With its chunking technology developed in Germany and integration into vector databases, Tucan.ai offers a pioneering solution that emphasizes precision, efficiency, and data protection. Whether it’s analyzing complex contracts, identifying market trends or making privacy-compliant decisions, Tucan.ai enables companies to revolutionize their data processing and make informed decisions based on verifiable and accurate data. Discover the transformative power of Tucan.ai and ensure your organization is at the forefront of data-driven decision making.
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