Trying out Ragflow application

    Ragflow (RAGFlow) is an open source retrieval-augmented generation (RAG) engine based on deep document understanding. The following is a detailed introduction to Ragflow:

1. Core Features

1. Deep document understanding: Ragflow has the ability to accurately extract knowledge from complex unstructured data, locate key content in massive data, and improve the accuracy of information retrieval. It supports multiple document formats, such as Word, PPT, Excel, txt, pictures, PDF, structured data, web pages, etc., to meet diverse data needs.
2. Templated block processing: Ragflow provides a variety of templates to support intelligent and explainable data block processing. Users can choose the appropriate template according to specific needs and document types to divide the pre-processed text into smaller blocks to improve processing efficiency and transparency.
3. Reliable citation and reduced illusion: Ragflow supports visualization of text blocks, which is convenient for manual intervention and proofreading. At the same time, it provides clear key citation sources to ensure that the generated answers are based on evidence and reduce the possibility of generating erroneous information.
4. Compatible with multiple heterogeneous data sources: The system can seamlessly process multiple data formats, making it easier for users to integrate data from different sources and provide a more comprehensive information foundation.
5. Automated and simple RAG workflow: Ragflow provides a simplified and automated workflow suitable for personal and corporate use. It supports the configuration of multiple large language models (LLMs) and embedding models, combines multiple retrieval and re-ranking techniques, and is equipped with an intuitive API for rapid integration into various business systems.

2. Technical Architecture and Workflow

1. Document parsing: Ragflow can automatically identify and process various document formats, parse out the text, title, paragraph, line break, picture, table and other elements in the document, and perform fine processing on the table.
2. Embedding representation: Each text block is converted into a vector representation using an embedding model, which captures the semantics and characteristics of the text. At the same time, the user’s question is also embedded in the same way.
3. Index storage: The generated text block vectors are stored in a vector database and indexed for fast retrieval.
4. Similarity retrieval: Use an approximate nearest neighbor search algorithm to find the text block most similar to the user's question vector in the vector database.
5. Information extraction and screening: Extract key information and useful content from the retrieved text blocks, and screen and organize them.
6. Context construction: The extracted and filtered information is integrated with the user’s questions to build an enhanced context that includes external knowledge.
7. Model input and text generation: The fused context is passed as input to the pre-trained large language model (LLM). The LLM will understand and analyze the question and generate the answer text based on the input context and its own language knowledge and generation capabilities.

3. Application Scenarios

Ragflow is widely used in scenarios that require dynamic content generation and rely on external knowledge bases, such as:

1. Intelligent customer service: Able to retrieve relevant information from the enterprise knowledge base in real time and provide customers with accurate and personalized answers.
2. Contract management: Quickly extract key terms and information from contracts to facilitate contract review, risk assessment and management.
3. Auxiliary diagnosis: Medical professionals can use Ragflow to quickly find relevant medical literature and case data, providing a more comprehensive reference for diagnosis and treatment.
4. Literature review: Students and researchers can use Ragflow to quickly locate and analyze relevant academic literature and efficiently complete the writing of literature reviews.
5. News reporting: Journalists can use Ragflow to quickly integrate and refine large amounts of news materials and generate news articles.
6. Investment Analysis: Financial institutions can use Ragflow to collect and analyze market data, financial news and other information in real time and generate investment analysis reports.
   

4. System requirements and installation steps

1. System requirements: CPU with at least 4 cores, memory with at least 16 GB, hard disk with at least 50 GB, Docker version ≥ 24.0.0, Docker compose ≥ v2.26.1.
2. Installation steps: First clone the repository, then run the command in the ragflow/docker directory to start the docker container, then check the server status, and finally enter the server IP address in the browser to access ragflow, and select the required LLM factory in the service_conf.yaml file and update the api_key field, etc.

The following is a practice of deploying DeepSeek 1.5b locally

Natural language to SQL - text2sql

Query the average scores of first-year students

answer

OK, now I need to help the user solve a problem about the database. The user provides a query statement "Query the average score of first-year students", but does not specify the specific database and table name. First, I should consider the user's usage scenario. Maybe they are managing student performance data in a school or educational institution, and hope to use SQL to obtain the average score of first-year students in a certain subject. Next, the user's demand is to query this information, so a database structure that can achieve this needs to be designed. Usually, there will be multiple tables in such a system: such as "student scores" and "grade information". Then, I should think about how to connect these two tables and calculate the average score of first-year students. This may involve aggregate functions such as AVG(). Finally, the user hopes to return only the data in the database of the query result, so it is necessary to ensure that the output contains only the required information and does not involve other content. SELECT AVG(score) AS average score, student scores.* FROM student scores, grade information WHERE grade == 'first grade'; This query will calculate the average score of first-year students from the "student scores" table and return the result to the database in the database.

We use this e-book for testing

1. Improve information retrieval and utilization efficiency

Local deployment of DeepSeek 1.5b and Ragflow knowledge base can greatly improve information retrieval and utilization efficiency. As a retrieval-augmented generation (RAG) technology, Ragflow combines the advantages of information retrieval and generative AI. It can quickly locate relevant information from a huge knowledge base and generate meaningful summaries or answers. This ability is indispensable for individuals or organizations that need to process large amounts of data and information. With local deployment, users can obtain the required information more quickly and accurately, thereby improving work efficiency.

2. Enhance data privacy and security

Another important significance of local deployment is the privacy and security of data. All data is stored locally, and users can fully control access rights to ensure that sensitive information is not stolen from outside. Compared with relying on external services, local deployment avoids the risk of data leakage and protects the information security of individuals or organizations. This is especially important for users who need to process sensitive data.

3. Reduce the cost of use

From a cost perspective, local deployment of DeepSeek 1.5b and Ragflow knowledge base is also a long-term saving option. Although some resources may be required to build and configure the system in the early stage, the subsequent maintenance cost is low. In the long run, this is more cost-effective than relying on external services. In addition, using external knowledge base services may incur ongoing subscription fees, while local deployment is a one-time investment.

4. Support offline use and customized development

Local deployment also supports offline use, which means that users can still use the knowledge base normally without an Internet connection. This is especially important for users who need to process information efficiently in various environments. In addition, local deployment can be highly customized according to personal needs. Users can freely select and configure functions to suit specific usage scenarios and needs.