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Added 0.17.0 release notes (#5608)

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### Type of change


- [x] Documentation Update
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writinwaters
2025-03-04 19:21:28 +08:00
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docs/develop/_category_.json
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{
"label": "Developer guides",
"label": "Developers",
"position": 4,
"link": {
"type": "generated-index",
"description": "Guides for Hardcore Developers"
"description": "Guides for hardcore developers"
}
}

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docs/develop/acquire_ragflow_api_key.md
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slug: /acquire_ragflow_api_key
---
# Acquire a RAGFlow API key
# Acquire RAGFlow API key
A key is required for the RAGFlow server to authenticate your requests via HTTP or a Python API. This documents provides instructions on obtaining a RAGFlow API key.

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docs/develop/build_docker_image.mdx
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slug: /build_docker_image
---
# Build a RAGFlow Docker Image
# Build RAGFlow Docker image
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';

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docs/develop/launch_ragflow_from_source.md
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slug: /launch_ragflow_from_source
---
# Launch a RAGFlow Service from Source
# Launch RAGFlow service from source
A guide explaining how to set up a RAGFlow service from its source code. By following this guide, you'll be able to debug using the source code.
## Target Audience
## Target audience
Developers who have added new features or modified existing code and wish to debug using the source code, *provided that* their machine has the target deployment environment set up.
@ -22,11 +22,11 @@ Developers who have added new features or modified existing code and wish to deb
If you have not installed Docker on your local machine (Windows, Mac, or Linux), see the [Install Docker Engine](https://docs.docker.com/engine/install/) guide.
:::
## Launch the Service from Source
## Launch a service from source
To launch the RAGFlow service from source code:
To launch a RAGFlow service from source code:
### Clone the RAGFlow Repository
### Clone the RAGFlow repository
```bash
git clone https://github.com/infiniflow/ragflow.git
@ -52,7 +52,7 @@ cd ragflow/
```
*A virtual environment named `.venv` is created, and all Python dependencies are installed into the new environment.*
### Launch Third-party Services
### Launch third-party services
The following command launches the 'base' services (MinIO, Elasticsearch, Redis, and MySQL) using Docker Compose:
@ -70,7 +70,7 @@ docker compose -f docker/docker-compose-base.yml up -d
2. In **docker/service_conf.yaml.template**, update mysql port to `5455` and es port to `1200`, as specified in **docker/.env**.
### Launch the RAGFlow Backend Service
### Launch the RAGFlow backend service
1. Comment out the `nginx` line in **docker/entrypoint.sh**.

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docs/faq.md
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slug: /faq
---
# FAQ
# FAQs
Queries regarding general features, troubleshooting, usage, and more.
Answers to questions about general features, troubleshooting, usage, and more.
---

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docs/guides/agent/embed_agent_into_webpage.md
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slug: /embed_agent_into_webpage
---
# Embed agent into a webpage
# Embed agent into webpage
You can use iframe to embed an agent into a third-party webpage.

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docs/guides/agent/general_purpose_chatbot.md
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slug: /general_purpose_chatbot
---
# Create a general-purpose chatbot
# Create chatbot
Create a general-purpose chatbot.
---
Chatbot is one of the most common AI scenarios. However, effectively understanding user queries and responding appropriately remains a challenge. RAGFlow's general-purpose chatbot agent is our attempt to tackle this longstanding issue.

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docs/guides/chat/accelerate_question_answering.mdx
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slug: /accelerate_question_answering
---
# Accelerate question answering
# Accelerate answering
import APITable from '@site/src/components/APITable';
A checklist to speed up document parsing and question answering.
A checklist to speed up question answering.
---
@ -23,18 +23,18 @@ Please note that some of your settings may consume a significant amount of time.
<APITable>
```
| Item name | Description |
| ----------------- | ------------------------------------------------------------ |
| Item name | Description |
| ----------------- | --------------------------------------------------------------------------------------------- |
| Total | Total time spent on this conversation round, including chunk retrieval and answer generation. |
| Check LLM | Time to validate the specified LLM. |
| Create retriever | Time to create a chunk retriever. |
| Bind embedding | Time to initialize an embedding model instance. |
| Bind LLM | Time to initialize an LLM instance. |
| Tune question | Time to optimize the user query using the context of the mult-turn conversation. |
| Bind reranker | Time to initialize an reranker model instance for chunk retrieval. |
| Generate keywords | Time to extract keywords from the user query. |
| Retrieval | Time to retrieve the chunks. |
| Generate answer | Time to generate the answer. |
| Check LLM | Time to validate the specified LLM. |
| Create retriever | Time to create a chunk retriever. |
| Bind embedding | Time to initialize an embedding model instance. |
| Bind LLM | Time to initialize an LLM instance. |
| Tune question | Time to optimize the user query using the context of the mult-turn conversation. |
| Bind reranker | Time to initialize an reranker model instance for chunk retrieval. |
| Generate keywords | Time to extract keywords from the user query. |
| Retrieval | Time to retrieve the chunks. |
| Generate answer | Time to generate the answer. |
```mdx-code-block
</APITable>

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docs/guides/chat/start_chat.md
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slug: /start_chat
---
# Chat
# Start AI chat
Initiate an AI-powered chat with a configured chat assistant.

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docs/guides/dataset/accelerate_doc_indexing.mdx
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slug: /accelerate_doc_indexing
---
# Accelerate document indexing
# Accelerate indexing
import APITable from '@site/src/components/APITable';
A checklist to speed up document parsing.
A checklist to speed up document parsing and indexing.
---

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docs/guides/dataset/configure_knowledge_base.md
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@ -134,7 +134,7 @@ As of RAGFlow v0.17.0, the search feature is still in a rudimentary form, suppor
## Delete knowledge base
You are allowed to delete a knowledge base. Hover your mouse over the three dot of the intended knowledge base card and the **Delete** option appears. Once you delete a knowledge base, the associated folder under **root/.knowledge** directory is AUTOMATICALLY REMOVED. The consequence is:
You are allowed to delete a knowledge base. Hover your mouse over the three dot of the intended knowledge base card and the **Delete** option appears. Once you delete a knowledge base, the associated folder under **root/.knowledge** directory is AUTOMATICALLY REMOVED. The consequence is:
- The files uploaded directly to the knowledge base are gone;
- The file references, which you created from within **File Management**, are gone, but the associated files still exist in **File Management**.

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docs/guides/manage_files.md
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# Files
Knowledge base, hallucination-free chat, and file management are the three pillars of RAGFlow. RAGFlow's file management allows you to upload files individually or in bulk. You can then link an uploaded file to multiple target knowledge bases. This guide showcases some basic usages of the file management feature.
Knowledge base, hallucination-free chat, and file management are the three pillars of RAGFlow. RAGFlow's file management allows you to upload files individually or in bulk. You can then link an uploaded file to multiple target knowledge bases. This guide showcases some basic usages of the file management feature.
:::danger IMPORTANT
Compared to uploading files directly to various knowledge bases, uploading them to RAGFlow's file management and then linking them to different knowledge bases is *not* an unnecessary step, particularly when you want to delete some parsed files or an entire knowledge base but retain the original files.
:::
## Create folder
@ -35,7 +39,7 @@ RAGFlow's file management supports previewing files in the following formats:
## Link file to knowledge bases
RAGFlow's file management allows you to *link* an uploaded file to multiple knowledge bases, creating a file reference in each target knowledge base. Therefore, deleting a file in your file management will AUTOMATICALLY REMOVE all related file references across the knowledge bases.
RAGFlow's file management allows you to *link* an uploaded file to multiple knowledge bases, creating a file reference in each target knowledge base. Therefore, deleting a file in your file management will AUTOMATICALLY REMOVE all related file references across the knowledge bases.
![link knowledgebase](https://github.com/infiniflow/ragflow/assets/93570324/6c6b8db4-3269-4e35-9434-6089887e3e3f)

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docs/guides/models/deploy_local_llm.mdx
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slug: /deploy_local_llm
---
# Deploy a local LLM
# Deploy LLM locally
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';

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docs/references/supported_models.mdx
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@ -42,7 +42,6 @@ A complete list of models supported by RAGFlow, which will continue to expand.
| Ollama | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: | | |
| OpenAI | :heavy_check_mark: | :heavy_check_mark: | | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| OpenAI-API-Compatible | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
| VLLM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
| OpenRouter | :heavy_check_mark: | | | :heavy_check_mark: | | |
| PerfXCloud | :heavy_check_mark: | :heavy_check_mark: | | | | |
| Replicate | :heavy_check_mark: | :heavy_check_mark: | | | | |
@ -54,6 +53,7 @@ A complete list of models supported by RAGFlow, which will continue to expand.
| TogetherAI | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
| Tongyi-Qianwen | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |
| Upstage | :heavy_check_mark: | :heavy_check_mark: | | | | |
| VLLM | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
| VolcEngine | :heavy_check_mark: | | | | | |
| Voyage AI | | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | | |
| Xinference | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: | :heavy_check_mark: |

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docs/release_notes.md
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Key features, improvements and bug fixes in the latest releases.
## v0.17.0
Released on March 3, 2025.
### New features
1. 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.
2. 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 Setting** tab of your chat assistant dialogue.
3. AI chat: Supports initiating a chat without specifying knowledge bases.
4. AI chat: HTML files can also be previewed and referenced, in addition to PDF files.
5. Dataset: Adds a **Layout recognition & OCR** dropdown menu to dataset configurations. This includes a DeepDoc model option, which is time-consuming, a much faster **naive** option (plain text), which skips DLR (Document Layout Recognition), OCR (Optimal Character Recognition), and TSR (Table Structure Recognition) tasks, and several currently *experimental* large model options.
6. 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.
7. Object storage: Supports using Aliyun OSS (Object Storage Service) as a file storage option.
8. Models: Updates the supported model list for Tongyi-Qianwen, adding DeepSeek-specific models; adds ModelScope as a model provider.
9. APIs: Document metadata can be updated through an API.
The following diagram illustrates the workflow of RAGFlow's Deep Research:
![Image](https://github.com/user-attachments/assets/f65d4759-4f09-4d9d-9549-c0e1fe907525)
The following is a screenshot of a conversation that integrates Deep Research:
![Image](https://github.com/user-attachments/assets/165b88ff-1f5d-4fb8-90e2-c836b25e32e9)
### Related APIs
#### HTTP APIs
Adds a body parameter `"meta_fields"` to the [Update document](./references/http_api_reference.md#update-document) method.
#### Python APIs
Adds a key option `"meta_fields"` to the [Update document](./references/python_api_reference.md#update-document) method.
### Documentation
#### Added documents
[Run retrieval test](./guides/dataset/run_retrieval_test.md)
## v0.16.0
Released on February 6, 2025.