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Added instructions on embedding agent or assistant into a third-party webpage (#4369)
### What problem does this PR solve? ### Type of change - [x] Documentation Update
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docs/guides/agent/embed_agent_into_webpage.md
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docs/guides/agent/embed_agent_into_webpage.md
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---
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sidebar_position: 3
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slug: /embed_agent_into_webpage
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---
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# Embed agent into a webpage
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You can use iframe to embed an agent into a third-party webpage.
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1. Before proceeding, you must [acquire an API key](https://ragflow.io/docs/dev/acquire_ragflow_api_key); otherwise, an error message would appear.
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2. On the **Agent** page, click an intended agent **>** **Edit** to access its editing page.
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3. Click **Embed into webpage** on the top right corner of the canvas to show the **iframe** window:
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4. Copy the iframe and embed it into a specific location on your webpage.
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@ -1,5 +1,5 @@
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---
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sidebar_position: 3
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sidebar_position: 10
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slug: /text2sql_agent
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---
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@ -9,7 +9,7 @@ Build a Text2SQL agent leverging RAGFlow's RAG capabilities. Contributed by @Tes
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## Scenario
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The Text2SQL agent is designed to bridge the gap between Natural Language Processing (NLP) and Structured Query Language (SQL). Its key advantages are as follows:
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The Text2SQL agent bridges the gap between Natural Language Processing (NLP) and Structured Query Language (SQL). Its key advantages are as follows:
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- **Assisting non-technical users with SQL**: Not all users have a background in SQL or understand the structure of the tables involved in queries. With a Text2SQL agent, users can pose questions or request data in natural language without needing an in-depth knowledge of the database structure or SQL syntax.
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@ -31,7 +31,7 @@ However, traditional Text2SQL solutions often require model fine-tuning, which c
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A list of components required:
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- Begin
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- [Begin](https://ragflow.io/docs/dev/begin_component)
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- Interface
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- Retrieval
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- Generate
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@ -3,7 +3,7 @@ sidebar_position: 7
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slug: /run_health_check
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---
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# Run health check on RAGFlow's dependencies
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# Run dependency health check
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Double-check the health status of RAGFlow's dependencies.
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@ -57,7 +57,7 @@ You start an AI conversation by creating an assistant.
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:::tip NOTE
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Click the light bubble logo above the answer to view the expanded system prompt:
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Click the light bubble icon above the answer to view the expanded system prompt:
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@ -74,10 +74,19 @@ Hover over an intended chat assistant **>** **Edit** to show the chat configurat
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## Integrate chat capabilities into your application
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## Integrate chat capabilities into your application or webpage
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RAGFlow also offers HTTP and Python APIs for you to integrate RAGFlow's capabilities into your applications. Read the following documents for more information:
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RAGFlow offers HTTP and Python APIs for you to integrate RAGFlow's capabilities into your applications. Read the following documents for more information:
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- [Acquire a RAGFlow API key](./develop/acquire_ragflow_api_key.md)
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- [HTTP API reference](../references/http_api_reference.md)
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- [Python API reference](../references/python_api_reference.md)
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- [Acquire a RAGFlow API key](https://ragflow.io/docs/dev/acquire_ragflow_api_key)
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- [HTTP API reference](https://ragflow.io/docs/dev/http_api_reference)
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- [Python API reference](https://ragflow.io/docs/dev/python_api_reference)
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You can use iframe to embed the created chat assistant into a third-party webpage:
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1. Before proceeding, you must [acquire an API key](https://ragflow.io/docs/dev/acquire_ragflow_api_key); otherwise, an error message would appear.
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2. Hover over an intended chat assistant **>** **Edit** to show the **iframe** window:
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3. Copy the iframe and embed it into a specific location on your webpage.
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