- **Slim edition**: excludes built-in embedding models and is identified by a **-slim** suffix added to the version name. Example: `infiniflow/ragflow:v0.20.1-slim`
- **Full edition**: includes built-in embedding models and has no suffix added to the version name. Example: `infiniflow/ragflow:v0.20.1`
These two embedding models are optimized specifically for English and Chinese, so performance may be compromised if you use them to embed documents in other languages.
- SQL Assistant Workflow: Empowers non-technical teams (e.g., operations, product) to independently query business data.
- Choose Your Knowledge Base Workflow: Lets users select a knowledge base to query during conversations. [#9325](https://github.com/infiniflow/ragflow/pull/9325)
- Choose Your Knowledge Base Agent: Delivers higher-quality responses with extended reasoning time, suited for complex queries. [#9325](https://github.com/infiniflow/ragflow/pull/9325)
### Fixed Issues
- The **Agent** component was unable to invoke models installed via vLLM.
- Agents could not be shared with the team.
- Embedding an Agent into a webpage was not functioning properly.
From v0.20.0 onwards, Agents are no longer compatible with earlier versions, and all existing Agents from previous versions must be rebuilt following the upgrade.
### New features
- Unified orchestration of both Agents and Workflows.
- A comprehensive refactor of the Agent, greatly enhancing its capabilities and usability, with support for Multi-Agent configurations, planning and reflection, and visual functionalities.
- Fully implemented MCP functionality, allowing for MCP Server import, Agents functioning as MCP Clients, and RAGFlow itself operating as an MCP Server.
- Multi-Agent based Deep Research: Collaborative Agent teamwork led by a Lead Agent with multiple Subagents, distinct from traditional workflow orchestration.
- An intelligent Q&A chatbot leveraging internal knowledge bases, designed for customer service and training scenarios.
- A resume analysis template used by the RAGFlow team to screen, analyze, and record candidate information.
- A blog generation workflow that transforms raw ideas into SEO-friendly blog content.
- An intelligent customer service workflow.
- A user feedback analysis template that directs user feedback to appropriate teams through semantic analysis.
- Trip Planner: Uses web search and map MCP servers to assist with travel planning.
- Image Lingo: Translates content from uploaded photos.
- An information search assistant that retrieves answers from both internal knowledge bases and the web.
- A context error occurring when using Sandbox in standalone mode. [#8340](https://github.com/infiniflow/ragflow/pull/8340)
- An excessive CPU usage issue caused by Ollama. [#8216](https://github.com/infiniflow/ragflow/pull/8216)
- A bug in the Code Component. [#7949](https://github.com/infiniflow/ragflow/pull/7949)
- Added support for models installed via Ollama or VLLM when creating a knowledge base through the API. [#8069](https://github.com/infiniflow/ragflow/pull/8069)
- Enabled role-based authentication for S3 bucket access. [#8149](https://github.com/infiniflow/ragflow/pull/8149)
- [Cross-language search](./references/glossary.mdx#cross-language-search) is supported in the Knowledge and Chat modules, enhancing search accuracy and user experience in multilingual environments, such as in Chinese-English knowledge bases.
- Agent component: A new Code component supports Python and JavaScript scripts, enabling developers to handle more complex tasks like dynamic data processing.
- Enhanced image display: Images in Chat and Search now render directly within responses, rather than as external references. Knowledge retrieval testing can retrieve images directly, instead of texts extracted from images.
From this release onwards, built-in rerank models have been removed because they have minimal impact on retrieval rates but significantly increase retrieval time.
- DeepDoc supports adopting VLM model as a processing pipeline during document layout recognition, enabling in-depth analysis of images in PDF and DOCX files.
- AI chat: Implements Deep Research for agentic reasoning. To activate this, enable the **Reasoning** toggle under the **Prompt engine** tab of your chat assistant dialogue.
- AI chat: Leverages Tavily-based web search to enhance contexts in agentic reasoning. To activate this, enter the correct Tavily API key under the **Assistant settings** tab of your chat assistant dialogue.
- Dataset: Adds a **PDF parser**, aka **Document parser**, dropdown menu to dataset configurations. This includes a DeepDoc model option, which is time-consuming, a much faster **naive** option (plain text), which skips DLA (Document Layout Analysis), OCR (Optical Character Recognition), and TSR (Table Structure Recognition) tasks, and several currently *experimental* large model options. See [here](./guides/dataset/select_pdf_parser.md).
- Agent component: **(x)** or a forward slash `/` can be used to insert available keys (variables) in the system prompt field of the **Generate** or **Template** component.
- Object storage: Supports using Aliyun OSS (Object Storage Service) as a file storage option.
- GraphRAG refactor: Knowledge graph is dynamically built on an entire knowledge base (dataset) rather than on an individual file, and automatically updated when a newly uploaded file starts parsing. See [here](https://ragflow.io/docs/dev/construct_knowledge_graph).
- Adds an **Iteration** agent component and a **Research report generator** agent template. See [here](./guides/agent/agent_component_reference/iteration.mdx).
Adds [Infinity's configuration file](https://github.com/infiniflow/ragflow/blob/main/docker/infinity_conf.toml) to facilitate integration and customization of [Infinity](https://github.com/infiniflow/infinity) as a document engine. From this release onwards, updates to Infinity's configuration can be made directly within RAGFlow and will take effect immediately after restarting RAGFlow using `docker compose`. [#3715](https://github.com/infiniflow/ragflow/pull/3715)
- Supports [Infinity](https://github.com/infiniflow/infinity) or Elasticsearch (default) as document engine for vector storage and full-text indexing. [#2894](https://github.com/infiniflow/ragflow/pull/2894)
From this release onwards, **service_config.yaml.template** replaces **service_config.yaml** for configuring backend services. Upon Docker container startup, the environment variables defined in this template file are automatically populated and a **service_config.yaml** is auto-generated from it. [#3341](https://github.com/infiniflow/ragflow/pull/3341)
This approach eliminates the need to manually update **service_config.yaml** after making changes to **.env**, facilitating dynamic environment configurations.
Ensure that you [upgrade **both** your code **and** Docker image to this release](https://ragflow.io/docs/dev/upgrade_ragflow#upgrade-ragflow-to-the-most-recent-officially-published-release) before trying this new approach.
- Implements an **Excel to HTML** toggle in the **General** chunking method, allowing users to parse a spreadsheet into either HTML tables or key-value pairs by row.
From this release onwards, RAGFlow offers slim editions of its Docker images to improve the experience for users with limited Internet access. A slim edition of RAGFlow's Docker image does not include built-in BGE/BCE embedding models and has a size of about 1GB; a full edition of RAGFlow is approximately 9GB and includes both built-in embedding models and embedding models that will be downloaded once you select them in the RAGFlow UI.
- Integrates reranker and embedding models: [BCE](https://github.com/netease-youdao/BCEmbedding), [BGE](https://github.com/FlagOpen/FlagEmbedding), and [Jina](https://jina.ai/embeddings/).
- Supports LLMs Baichuan and VolcanoArk.
- Implements [RAPTOR](https://arxiv.org/html/2401.18059v1) for improved text retrieval.
