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sidebar_position: 1
slug: /start_chat
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# Start AI chat

Initiate an AI-powered chat with a configured chat assistant.

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Knowledge base, hallucination-free chat, and file management are the three pillars of RAGFlow. Chats in RAGFlow are based on a particular knowledge base or multiple knowledge bases. Once you have created your knowledge base, finished file parsing, and [run a retrieval test](../dataset/run_retrieval_test.md), you can go ahead and start an AI conversation.

## Start an AI chat

You start an AI conversation by creating an assistant.

1. Click the **Chat** tab in the middle top of the page **>** **Create an assistant** to show the **Chat Configuration** dialogue *of your next dialogue*.

   > RAGFlow offers you the flexibility of choosing a different chat model for each dialogue, while allowing you to set the default models in **System Model Settings**.

2. Update **Assistant Setting**:

   - **Assistant name** is the name of your chat assistant. Each assistant corresponds to a dialogue with a unique combination of knowledge bases, prompts, hybrid search configurations, and large model settings.
   - **Empty response**:
     - If you wish to *confine* RAGFlow's answers to your knowledge bases, leave a response here. Then, when it doesn't retrieve an answer, it *uniformly* responds with what you set here.
     - If you wish RAGFlow to *improvise* when it doesn't retrieve an answer from your knowledge bases, leave it blank, which may give rise to hallucinations.
   - **Show quote**: This is a key feature of RAGFlow and enabled by default. RAGFlow does not work like a black box. Instead, it clearly shows the sources of information that its responses are based on.
   - Select the corresponding knowledge bases. You can select one or multiple knowledge bases, but ensure that they use the same embedding model, otherwise an error would occur.

3. Update **Prompt Engine**:

   - In **System**, you fill in the prompts for your LLM, you can also leave the default prompt as-is for the beginning.
   - **Similarity threshold** sets the similarity "bar" for each chunk of text. The default is 0.2. Text chunks with lower similarity scores are filtered out of the final response.
   - **Keyword similarity weight** is set to 0.7 by default. RAGFlow uses a hybrid score system to evaluate the relevance of different text chunks. This value sets the weight assigned to the keyword similarity component in the hybrid score.
     - If **Rerank model** is left empty, the hybrid score system uses keyword similarity and vector similarity, and the default weight assigned to the vector similarity component is 1-0.7=0.3.
     - If **Rerank model** is selected, the hybrid score system uses keyword similarity and reranker score, and the default weight assigned to the reranker score is 1-0.7=0.3.
   - **Top N** determines the *maximum* number of chunks to feed to the LLM. In other words, even if more chunks are retrieved, only the top N chunks are provided as input.
   - **Multi-turn optimization** enhances user queries using existing context in a multi-round conversation. It is enabled by default. When enabled, it will consume additional LLM tokens and significantly increase the time to generate answers.
   - **Rerank model** sets the reranker model to use. It is left empty by default.
     - If **Rerank model** is left empty, the hybrid score system uses keyword similarity and vector similarity, and the default weight assigned to the vector similarity component is 1-0.7=0.3.
     - If **Rerank model** is selected, the hybrid score system uses keyword similarity and reranker score, and the default weight assigned to the reranker score is 1-0.7=0.3.
   - **Variable** refers to the variables (keys) to be used in the system prompt. `{knowledge}` is a reserved variable. Click **Add** to add more variables for the system prompt.
      - If you are uncertain about the logic behind **Variable**, leave it *as-is*.

4. Update **Model Setting**:

   - In **Model**: you select the chat model. Though you have selected the default chat model in **System Model Settings**, RAGFlow allows you to choose an alternative chat model for your dialogue.
   - **Preset configurations** refers to the level that the LLM improvises. From **Improvise**, **Precise**, to **Balance**, each preset configuration corresponds to a unique combination of **Temperature**, **Top P**, **Presence penalty**, and **Frequency penalty**.
   - **Temperature**: Level of the prediction randomness of the LLM. The higher the value, the more creative the LLM is.
   - **Top P** is also known as "nucleus sampling". See [here](https://en.wikipedia.org/wiki/Top-p_sampling) for more information.
   - **Max Tokens**: The maximum length of the LLM's responses. Note that the responses may be curtailed if this value is set too low.

5. Now, let's start the show:

   ![question1](https://github.com/user-attachments/assets/c4114a3d-74ff-40a3-9719-6b47c7b11ab1)

:::tip NOTE

1. Click the light bulb icon above the answer to view the expanded system prompt:

![](https://github.com/user-attachments/assets/515ab187-94e8-412a-82f2-aba52cd79e09)

   *The light bulb icon is available only for the current dialogue.*

2. Scroll down the expanded prompt to view the time consumed for each task:

![enlighten](https://github.com/user-attachments/assets/fedfa2ee-21a7-451b-be66-20125619923c)
:::

## Update settings of an existing chat assistant

Hover over an intended chat assistant **>** **Edit** to show the chat configuration dialogue:

![edit_chat](https://github.com/user-attachments/assets/5c514cf0-a959-4cfe-abad-5e42a0e23974)

![chat_config](https://github.com/user-attachments/assets/1a4eaed2-5430-4585-8ab6-930549838c5b)

## Integrate chat capabilities into your application or webpage

RAGFlow offers HTTP and Python APIs for you to integrate RAGFlow's capabilities into your applications. Read the following documents for more information:

- [Acquire a RAGFlow API key](../../develop/acquire_ragflow_api_key.md)
- [HTTP API reference](../../references/http_api_reference.md)
- [Python API reference](../../references/python_api_reference.md)

You can use iframe to embed the created chat assistant into a third-party webpage:

1. Before proceeding, you must [acquire an API key](../models/llm_api_key_setup.md); otherwise, an error message would appear.
2. Hover over an intended chat assistant **>** **Edit** to show the **iframe** window:

   ![chat-embed](https://github.com/user-attachments/assets/13ea3021-31c4-4a14-9b32-328cd3318fb5)

3. Copy the iframe and embed it into a specific location on your webpage.
