10 minutes to learn about the core of AI knowledge base - vector database

As an explorer who is passionate about AI technology, I have recently conducted an in-depth study on the application of vector databases in knowledge base construction and summarized the following content. This article will analyze the current popular vector databases, analyze their applicable scenarios in AI applications, and introduce some excellent open source projects to help everyone better understand and apply these cutting-edge technologies.

1. Core Value and Technical Characteristics of Vector Database

Vector database is a new type of database system designed for storing, managing and retrieving high-dimensional vector data. It has become an important infrastructure in the field of artificial intelligence and machine learning. Its core value and technical features are as follows:

Core Values

1. 1.  Efficiently process complex data Vector databases can efficiently process and retrieve high-dimensional data by converting unstructured data (such as text, images, audio, video, etc.) into vector form. This capability enables AI models to better understand and utilize the semantic features of data, thereby improving the performance and accuracy of the model.
   
2. 2.  Support multimodal applications Vector databases can uniformly process multiple types of data (such as text, images, audio, etc.), providing strong support for multimodal AI applications. For example, through vector embedding technology, functions such as image search and text search can be realized.
   
3. 3.  Improve AI application performance The efficient retrieval capability and real-time support of vector databases enable AI applications to quickly respond to user requests and improve user experience. For example, in recommendation systems and intelligent question-answering, vector databases can quickly retrieve the most relevant data.
   
4. 4.  Semantic understanding capability The vector database can capture the semantic information behind the data through vector representation and support semantic-based similarity search. This capability enables AI applications to better understand user intent and provide more accurate results.
   
5. 5.  Flexible scalability Vector databases support seamless expansion from stand-alone deployment to distributed clusters, and can adapt to application scenarios of different scales. This flexibility enables them to cope with massive data and high-concurrency queries, meeting the needs of large-scale applications.
   

Technical features

1. 1.  High-dimensional data processing Vector databases support efficient storage of thousands or even ten thousand-dimensional vectors to meet the needs of complex data models. This capability enables them to process large-scale high-dimensional data and is suitable for various AI application scenarios.
   
2. 2.  Approximate nearest neighbor search (ANN) vector database achieves sub-second retrieval through algorithms such as HNSW and IVF-PQ, greatly improving retrieval efficiency. These algorithms can quickly find the results most similar to the query vector in large-scale data sets.
   
3. 3.  The multimodal fusion vector database can uniformly process the semantic features of multiple types of data such as text and images, providing support for multimodal AI applications. For example, image vectors can capture information such as color, shape, and texture, and text vectors can contain semantic information.
   
4. 4.  Real-time retrieval capability Vector databases support millisecond-level similarity retrieval, meeting the real-time requirements of scenarios such as recommendation systems and intelligent question answering. This real-time support makes them perform well in applications that require fast response.
   
5. 5.  Flexible index selection The vector database supports a variety of vector indexing algorithms (such as IVF, HNSW, PQ, etc.), and can select the optimal indexing strategy according to different application scenarios and data characteristics.
   
6. 6.  Powerful scalability Vector databases usually adopt a distributed architecture, which is easy to scale horizontally and can handle massive data and high-concurrency queries. This architecture enables them to scale as the workload grows.
   
7. 7.  Rich functional features Vector databases usually provide comprehensive vector data management, index construction, query optimization, monitoring and operation and maintenance functions. Some products also support data version control, multi-tenant architecture and advanced security features.
   

Role in AI

The role of vector databases in AI applications is mainly reflected in the following aspects:

1. 1.  Semantic Search The vector database can realize semantic-based similarity search and support more accurate retrieval of text, image, audio and other data. For example, in a question-answering system, vector retrieval can be used to find the most relevant answer to a user's question.
   
2. 2.  Recommendation system vector database can quickly retrieve user interest vectors and support personalized recommendation system. For example, in e-commerce scenarios, vector retrieval can recommend products that are most similar to the user's historical behavior.
   
3. 3.  Multimodal Applications Vector databases can uniformly process multiple data types such as text, images, and audio, and support cross-modal retrieval. For example, vector retrieval can realize functions such as image search and text search.
   
4. 4.  Anomaly detection vector database can detect abnormal patterns through vector similarity, supporting scenarios such as financial fraud detection and network security.
   
5. 5.  The knowledge graph extended vector database can vectorize the entities and relationships in the knowledge graph, supporting more efficient graph retrieval and reasoning.
   

2. In-depth comparison of mainstream vector databases

1. Open source friendly

1. PGVector

PGVector is a vector database extension based on PostgreSQL that supports the storage and similarity search of vector data.

• •  Applicable scenarios : Suitable for scenarios where the write performance requirements are not high and the development team is accustomed to SQL development.
• •  Advantages : Relying on the mature ecosystem of PostgreSQL, it is easy to integrate and supports ACID transactions.
• •  Disadvantages : The import performance under large data sets and the recall rate in filtering scenarios are poor.

2. Chroma

Chroma is an open source vector database focused on simplifying the storage and retrieval of text embeddings.

• •  Applicable scenarios : Suitable for processing multimedia content, especially audio and video search.
• •  Advantages : Easy to use, supports multiple storage backends and multi-language SDKs.
• •  Disadvantages : Currently in Alpha stage, not suitable for production use.

2. Performance-oriented players

1. Milvus/Zilliz

Milvus is a high-performance open source vector database that is particularly suitable for processing large-scale data sets. It supports distributed architecture, can handle PB-level data volumes, and can achieve second-level retrieval of tens of billions of vectors through GPU acceleration. Zilliz Cloud, as a fully managed service of Milvus, further simplifies the complexity of deployment and expansion.

• •  Applicable scenarios : image, audio, and video retrieval, and large-scale machine learning deployment.
• •  Advantages : Distributed architecture, supports large-scale data processing, and fast retrieval speed.
• •  Disadvantages : Operation and maintenance are complex and require support from a professional team.

2. Pinecone

Pinecone is a fully managed vector database service that provides out-of-the-box vector search capabilities. It has built-in automatic index optimization capabilities and can achieve low-latency, high-recall search on tens of millions of datasets.

• •  Applicable scenarios : knowledge graph, intelligent question-answering system, and recommendation system.
• •  Pros : Fully managed service, easy to use, suitable for rapid prototyping.
• •  Disadvantages : Long-term use costs are high and need to be carefully evaluated.

3. Ecological integration

1. Redis

Redis is a high-performance in-memory database that supports vector retrieval through the RedisSearch module. It can be seamlessly integrated with the existing cache system to provide extremely low retrieval latency.

• •  Applicable scenarios : chatbot cache, recommendation system.
• •  Advantages : Good compatibility with existing technology stack and fast retrieval speed.
• •  Disadvantages : The persistence capability is relatively weak, and attention should be paid to the configuration strategy.

2. Elasticsearch

Elasticsearch is a widely used search engine that natively supports vector field types after version 8.0. It combines inverted index and vector hybrid search to improve search accuracy.

• •  Applicable scenarios : log analysis, e-commerce search, and enterprise-level data retrieval.
• •  Advantages : Comprehensive functions and strong community support.
• •  Disadvantages : High resource consumption, complex deployment and configuration.

4. Innovative technology

1. Weaviate

Weaviate is an AI-native database that supports vector-object hybrid storage architecture. It provides custom module extension capabilities to simplify the construction of complex queries.

• •  Applicable scenarios : enterprise document search, intelligent customer service.
• •  Advantages : Supports hybrid search and has an active developer community.
• •  Disadvantages : Performance optimization requires certain technical experience.

2. LanceDB

LanceDB is a developer-friendly open source database that is particularly suitable for multimodal AI applications. It is based on Apache Arrow's memory-optimized design and can quickly process multimodal data.

• •  Applicable scenarios : multimodal fusion applications, such as cross-media search.
• •  Advantages : Excellent performance, supporting efficient storage and query.
• •  Disadvantages : The ecosystem is relatively new and there is little relevant information.

3. Selection Decision Matrix

When choosing a vector database, you need to weigh the pros and cons based on the specific application scenarios and requirements. The following is a more comprehensive selection decision matrix that combines the core advantages, applicable scenarios, and potential challenges of each database:

Detailed analysis

1. 1.  Rapid verification requirements

• •  Chroma  is a lightweight open source vector database suitable for rapid development and prototyping. It provides a simple API and a rich Python ecosystem, allowing you to get started quickly.
• •  Advantages : Easy to install and use, suitable for startup teams to conduct proof of concept (PoC) development.
• •  Challenges : Relatively limited functionality, and advanced features (such as distributed deployment) are not perfect.

• 2.  Multimodal Processing
• •  Both LanceDB  and  Weaviate  support multimodal data (such as text, images, videos, etc.) and are suitable for cross-media content platforms.
• •  Advantages : Able to handle multiple data types and support complex semantic retrieval.
• •  Challenges : Weaviate’s performance optimization requires certain technical experience.
• 3.  High concurrency and low latency
• •  Redis  is a high-performance in-memory database that supports vector retrieval through the RedisSearch module and is suitable for real-time recommendation systems.
• •  Advantages : Extremely low latency and able to respond quickly to user requests.
• •  Challenges : The persistence capability is weak and attention should be paid to data backup.
• 4.  Massive data storage
• •  Milvus  is a high-performance open source vector database that supports distributed architecture and can handle PB-level data volumes.
• •  Advantages : Distributed architecture, supports large-scale data processing, and fast retrieval speed.
• •  Challenges : Operation and maintenance are complex and require support from a professional team.
• 5.  Transaction consistency requirements
• •  PGVector  is a vector database extension based on PostgreSQL that supports ACID transactions.
• •  Advantages : Relying on the mature ecosystem of PostgreSQL, it is easy to integrate and suitable for scenarios with high requirements for transaction consistency, such as financial risk control.
• •  Challenges : Poor import performance and recall rate under large data sets.
• 6.  Cost Sensitive
• •  Both PGVector  and  Chroma  are open source and free, suitable for projects with limited budgets.
• •  Advantages : Open source and free, with good community support.
• •  Challenges : Chroma’s advanced features are limited, and performance optimization of PGVector requires additional investment.
• 7.  Prioritize Usability
• •  Both Chroma  and  Weaviate  provide developer-friendly APIs and rich documentation.
• •  Advantages : Chroma provides a simple API and a rich Python ecosystem, suitable for quick start.
• •  Challenges : Weaviate’s performance and capabilities may be limited on large-scale datasets.

4. Knowledge Base Open Source Project Practice Framework

1. Dify

IntroductionDify is an open source large language model (LLM) application development platform designed to help developers easily build and operate generative AI native applications. It integrates the concepts of Backend as a Service (BaaS) and LLMOps, and provides a full range of capabilities from Agent construction to AI workflow orchestration, RAG retrieval, model management, etc.


Core Advantages

• •  Low-code/no-code development : Quickly build AI applications through a visual interface that supports drag-and-drop operations and is suitable for users with non-technical backgrounds.
• •  Real-time and accuracy : Knowledge base data can be updated at any time to ensure that the model obtains the latest contextual information.
• •  Powerful integration capabilities : supports multiple model vendors (such as OpenAI, Anthropic, etc.) and provides rich API interfaces.
• •  Multi-scenario support : applicable to various scenarios such as intelligent customer service, content generation, data analysis, etc.

Practice Framework

• •  Data source integration : supports creating knowledge bases from data sources such as local files (TXT, PDF, Markdown, etc.), Notion, web pages, etc.
• •  Knowledge base management : Provides a visual knowledge base management interface that supports segmented preview and recall effect testing.
• •  Application Development : Design and deploy complex AI applications through a visual workflow orchestration interface.
• •  Deployment method : supports cloud usage ( Dify Cloud ) and self-hosted deployment.

Open source address : https://github.com/langgenius/dify





(2) RAGFlow

IntroductionRAGFlow is an open source RAG (Retrieval-Augmented Generation) engine built on deep document understanding, designed to provide a simplified RAG workflow for businesses and individuals. It combines large language models (LLM) and deep document understanding technology, can process unstructured data in complex formats, and provide high-quality question-answering capabilities.


Core Features

1. 1.  Deep document understanding
• • Supports documents in multiple formats (such as PDF, Word, PPT, Excel, TXT, pictures, etc.), and can accurately extract key information such as text, tables, images, etc.
• • Provides template-based parsing strategies that support intelligent document layout recognition and diversified templates to adapt to different industries and scenarios.
• 2.  High-quality Q&A
• • Through the concept of “high-quality input, high-quality output”, reduce hallucinations in generated results and ensure the authenticity and reliability of answers.
• • Provide key reference and traceability functions to support users in verifying the source of information.
• 3.  Automated RAG workflow
• • Provides an end-to-end RAG process, including document parsing, text slicing, vectorization, index building, multi-way recall, and fusion re-ranking.
• • Supports dynamic Agent orchestration, suitable for a variety of scenarios from personal applications to large enterprises.
• 4.  Visualization and Interpretability
• • The text slicing process is visualized to support manual adjustment and intervention, improving the transparency and credibility of the system.
• 5.  Flexible integration capabilities
• • Provides rich API interfaces for easy integration with existing systems.
• • Supports docking of multiple models and is compatible with different language models.

System ArchitectureRAGFlow 's system architecture is divided into two flows:


1. 1.  Knowledge construction flow : document parsing, data identification, text slicing, vectorization, and index construction.
2. 2.  Question-answering retrieval enhancement flow : query processing, multi-way recall, re-ranking, LLM generation and citation tracking.

Application ScenariosRAGFlow is widely used in finance, industry, biopharmaceuticals, scientific research and other industries, supporting enterprise-level knowledge base construction, intelligent question and answer, document management and other functions.


The open source address is https://github.com/infiniflow/ragflow. Users can also experience the online demonstration at https://demo.ragflow.io.






3. Knowledge base integration between Dify and RAGFlow

Integration Advantages The combination of Dify and RAGFlow provides powerful complementary functions for smart application development, mainly in the following aspects:


1. 1.  Hybrid retrieval and deep understanding
• • Combined with RAGFlow’s retrieval-augmented generation technology, Dify is able to retrieve relevant information from large amounts of text while leveraging pre-trained models to generate coherent and accurate responses.
• • Supports multimodal data (such as text, pictures, tables, etc.) and can handle more complex information structures.
• 2.  Real-time updates and dynamic adaptation
• • Through RAGFlow, Dify is able to update the knowledge base in real time, ensuring that the answers generated are always based on the latest information.
• 3.  Optimize the development process
• • Dify’s platform simplifies the model integration and deployment process, making it easy for developers to apply RAGFlow’s search-enhanced generation technology to their own projects.
• • Provide API support to facilitate developers to flexibly customize and expand application functions.

Integration Methods There are three ways to integrate Dify with RAGFlow:


1. 1.  Native docking
• • Starting from RAGFlow 0.13.0, it supports adding external knowledge bases to Dify. Developers can configure API Endpoint and API Key in the knowledge base page of Dify to integrate RAGFlow as an external knowledge base.
• • This method is recommended because it provides more efficient retrieval and a more tightly integrated experience.
• 2.  HTTP calls to Chats API
• • Call RAGFlow’s Chats API through the HTTP component, send the user request to RAGFlow for processing, and then return the result to Dify for display.
• • The advantage is that the knowledge base query effect is basically equivalent to the native RAGFlow, but the running speed is slower and does not support data source display.
• 3.  HTTP call retrieval API
• • Call RAGFlow’s Retrieval API through the HTTP component to let RAGFlow recall document fragments (chunks), and Dify passes the fragments to the large model to summarize and answer the questions.
• • The advantage is that it is relatively fast, but when the chunk is large, the process is prone to failure and needs manual restriction.

Integration Example The following are the specific configuration steps for native integration between Dify and RAGFlow:


1. 1.  Add external knowledge base API
• • In the upper right corner of Dify’s knowledge base page, click “External Knowledge Base API” and set the name, API Endpoint and API Key.
• • The API Endpoint format is:http://[ragflow-ip|ragflow-domain]/api/v1/dify.
• 2.  Connect to external knowledge base
• • Fill in the knowledge base ID in DIFY (which can be obtained through RAGFlow’s API).
• • Set recall parameters (such as Top K and Score thresholds) to complete the connection of the knowledge base.
• 3.  Testing and application
• • Call RAGFlow’s knowledge base in Dify’s workflow for testing.
• • It is recommended to select only one knowledge base at a time to avoid empty query results.

V. Conclusion

The development of vector database and knowledge base open source projects has brought new opportunities and possibilities for the construction of AI applications, provided developers with a wealth of choices, and facilitated efficient and intelligent application development. I hope that the content of this article can provide valuable references and help everyone better choose the appropriate technical solutions. You are also welcome to leave a message to discuss!