ragflow/api/http_api_reference.md

# DRAFT! HTTP API Reference

**THE API REFERENCES BELOW ARE STILL UNDER DEVELOPMENT.**

---

:::tip NOTE
Dataset Management
:::

---

## Create dataset

**POST** `/api/v1/dataset`

Creates a dataset.

### Request

- Method: POST
- URL: `/api/v1/dataset`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body:
  - `"name"`: `string`
  - `"avatar"`: `string`
  - `"description"`: `string`
  - `"language"`: `string`
  - `"embedding_model"`: `string`
  - `"permission"`: `string`
  - `"parse_method"`: `string`
  - `"parser_config"`: `Dataset.ParserConfig`

#### Request example

```bash
# "name": name is required and can't be duplicated.
# "embedding_model": embedding_model must not be provided.
# "naive" means general.
curl --request POST \
  --url http://{address}/api/v1/dataset \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --data '{
  "name": "test",
  "chunk_count": 0,
  "document_count": 0,
  "chunk_method": "naive"
}'
```

#### Request parameters

- `"name"`: (*Body parameter*), `string`, *Required*  
  The unique name of the dataset to create. It must adhere to the following requirements:  
  - Permitted characters include:
    - English letters (a-z, A-Z)
    - Digits (0-9)
    - "_" (underscore)
  - Must begin with an English letter or underscore.
  - Maximum 65,535 characters.
  - Case-insensitive.

- `"avatar"`: (*Body parameter*), `string`  
    Base64 encoding of the avatar. Defaults to `""`.

- `"description"`: (*Body parameter*), `string`  
  A brief description of the dataset to create. Defaults to `""`.

- `"language"`: (*Body parameter*), `string`  
  The language setting of the dataset to create. Available options:  
  - `"English"` (Default)
  - `"Chinese"`

- `"embedding_model"`: (*Body parameter*), `string`  
  The name of the embedding model to use. For example: `"BAAI/bge-zh-v1.5"`

- `"permission"`: (*Body parameter*), `string`  
  Specifies who can access the dataset to create. You can set it only to `"me"` for now.

- `"chunk_method"`: (*Body parameter*), `enum<string>`  
  The chunking method of the dataset to create. Available options:  
  - `"naive"`: General (default)
  - `"manual`: Manual
  - `"qa"`: Q&A
  - `"table"`: Table
  - `"paper"`: Paper
  - `"book"`: Book
  - `"laws"`: Laws
  - `"presentation"`: Presentation
  - `"picture"`: Picture
  - `"one"`:One
  - `"knowledge_graph"`: Knowledge Graph
  - `"email"`: Email

- `"parser_config"`: (*Body parameter*)  
  The configuration settings for the dataset parser. A `ParserConfig` object contains the following attributes:
  - `"chunk_token_count"`: Defaults to `128`.
  - `"layout_recognize"`: Defaults to `True`.
  - `"delimiter"`: Defaults to `"\n!?。；！？"`.
  - `"task_page_size"`: Defaults to `12`.

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0,
    "data": {
        "avatar": null,
        "chunk_count": 0,
        "create_date": "Thu, 10 Oct 2024 05:57:37 GMT",
        "create_time": 1728539857641,
        "created_by": "69736c5e723611efb51b0242ac120007",
        "description": null,
        "document_count": 0,
        "embedding_model": "BAAI/bge-large-zh-v1.5",
        "id": "8d73076886cc11ef8c270242ac120006",
        "language": "English",
        "name": "test_1",
        "parse_method": "naive",
        "parser_config": {
            "pages": [
                [
                    1,
                    1000000
                ]
            ]
        },
        "permission": "me",
        "similarity_threshold": 0.2,
        "status": "1",
        "tenant_id": "69736c5e723611efb51b0242ac120007",
        "token_num": 0,
        "update_date": "Thu, 10 Oct 2024 05:57:37 GMT",
        "update_time": 1728539857641,
        "vector_similarity_weight": 0.3
    }
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "Duplicated knowledgebase name in creating dataset."
}
```

---

## Delete datasets

**DELETE** `/api/v1/dataset`

Deletes datasets by ID.

### Request

- Method: DELETE
- URL: `/api/v1/dataset`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
  - Body:
    - `"ids"`: `list[string]`


#### Request example

```bash
# Either id or name must be provided, but not both.
curl --request DELETE \
  --url http://{address}/api/v1/dataset \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --data '{
  "ids": ["test_1", "test_2"]
  }'
```

#### Request parameters

- `"ids"`: (*Body parameter*)
  The IDs of the datasets to delete. Defaults to `""`. If not specified, all datasets in the system will be deleted.

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0 
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "You don't own the dataset."
}
```

---

## Update dataset

**PUT** `/api/v1/dataset/{dataset_id}`

Updates configurations for a specified dataset.

### Request

- Method: PUT
- URL: `/api/v1/dataset/{dataset_id}`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
  - Body:
    - `"name"`: `string`
    - `"embedding_model"`: `string`
    - `"chunk_method"`: `enum<string>`

#### Request example

```bash
# "id":  id is required.
# "name": If you update name, it can't be duplicated.
# "embedding_model": If you update embedding_model, it can't be changed.
# "parse_method": If you update parse_method, chunk_count must be 0. 
# "naive" means general.
curl --request PUT \
  --url http://{address}/api/v1/dataset/{dataset_id} \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --data '{
  "name": "test",
  "embedding_model": "BAAI/bge-zh-v1.5",
  "parse_method": "naive"
}'
```

#### Request parameters

- `"name"`: `string` The name of the dataset to update.
- `"embedding_model"`: `string` The embedding model name to update.
  - Ensure that `"chunk_count"` is `0` before updating `"embedding_model"`.
- `"chunk_method"`: `enum<string>` The chunking method for the dataset. Available options:
  - `"naive"`: General
  - `"manual`: Manual
  - `"qa"`: Q&A
  - `"table"`: Table
  - `"paper"`: Paper
  - `"book"`: Book
  - `"laws"`: Laws
  - `"presentation"`: Presentation
  - `"picture"`: Picture
  - `"one"`:One
  - `"knowledge_graph"`: Knowledge Graph
  - `"email"`: Email

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0 
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "Can't change tenant_id."
}
```

---

## List datasets

**GET** `/api/v1/dataset?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id}`

Lists datasets.

### Request

- Method: GET
- URL: `/api/v1/dataset?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id}`
- Headers:
  - `'Authorization: Bearer {YOUR_API_KEY}'`


#### Request example

```bash
# If no page parameter is passed, the default is 1
# If no page_size parameter is passed, the default is 1024
# If no order_by parameter is passed, the default is "create_time"
# If no desc parameter is passed, the default is True
curl --request GET \
  --url http://{address}/api/v1/dataset?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id} \
  --header 'Authorization: Bearer {YOUR_API_KEY}'
```

#### Request parameters

- `"page"`: (*Path parameter*)  
  Specifies the page on which the datasets will be displayed. Defaults to `1`.
- `"page_size"`: (*Path parameter*)  
  The number of datasets on each page. Defaults to `1024`.
- `"orderby"`: (*Path parameter*)  
  The field by which datasets should be sorted. Available options:
  - `"create_time"` (default)
  - `"update_time"`
- `"desc"`: (*Path parameter*)  
  Indicates whether the retrieved datasets should be sorted in descending order. Defaults to `True`.
- `"id"`: (*Path parameter*)  
  The ID of the dataset to retrieve. Defaults to `None`.
- `"name"`: (*Path parameter*)  
  The name of the dataset to retrieve. Defaults to `None`.

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0,
    "data": [
        {
            "avatar": "",
            "chunk_count": 59,
            "create_date": "Sat, 14 Sep 2024 01:12:37 GMT",
            "create_time": 1726276357324,
            "created_by": "69736c5e723611efb51b0242ac120007",
            "description": null,
            "document_count": 1,
            "embedding_model": "BAAI/bge-large-zh-v1.5",
            "id": "6e211ee0723611efa10a0242ac120007",
            "language": "English",
            "name": "mysql",
            "parse_method": "knowledge_graph",
            "parser_config": {
                "chunk_token_num": 8192,
                "delimiter": "\\n!?;。；！？",
                "entity_types": [
                    "organization",
                    "person",
                    "location",
                    "event",
                    "time"
                ]
            },
            "permission": "me",
            "similarity_threshold": 0.2,
            "status": "1",
            "tenant_id": "69736c5e723611efb51b0242ac120007",
            "token_num": 12744,
            "update_date": "Thu, 10 Oct 2024 04:07:23 GMT",
            "update_time": 1728533243536,
            "vector_similarity_weight": 0.3
        }
    ]
}
```
  
An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "The dataset doesn't exist"
}
```

---

:::tip API GROUPING
File Management within Dataset
:::

---

## Upload documents

**POST** `/api/v1/dataset/{dataset_id}/document`

Uploads documents to a specified dataset.

### Request

- Method: POST
- URL: `/api/v1/dataset/{dataset_id}/document`
- Headers:
  - `'Content-Type: multipart/form-data'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Form:
  - `'file=@{FILE_PATH}'`

#### Request example

```bash
curl --request POST \
     --url http://{address}/api/v1/dataset/{dataset_id}/document \
     --header 'Content-Type: multipart/form-data' \
     --header 'Authorization: Bearer {YOUR_API_KEY}' \     
     --form 'file=@./test1.txt' \
     --form 'file=@./test2.pdf'
```

#### Request parameters

- `"dataset_id"`: (*Path parameter*)  
  The ID of the dataset to which the documents will be uploaded.
- `"file"`: (*Body parameter*)  
  The document to upload.

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0 
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 101,
    "message": "No file part!"
}
```

---

## Update document

**PUT** `/api/v1/dataset/{dataset_id}/info/{document_id}`

Updates configurations for a specified document.

### Request

- Method: PUT
- URL: `/api/v1/dataset/{dataset_id}/document/{document_id}`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body:
  - `"name"`:`string`
  - `"chunk_method"`:`string`
  - `"parser_config"`:`dict`

#### Request example

```bash
curl --request PUT \
  --url http://{address}/api/v1/dataset/{dataset_id}/info/{document_id} \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --header 'Content-Type: application/json' \
  --data '{
  "name": "manual.txt", 
  "chunk_method": "manual", 
  "parser_config": {"chunk_token_count": 128}
  }'

```

#### Request parameters

- `"name"`: (*Body parameter*), `string`
- `"chunk_method"`: (*Body parameter*), `string`  
  The parsing method to apply to the document:  
  - `"naive"`: General
  - `"manual`: Manual
  - `"qa"`: Q&A
  - `"table"`: Table
  - `"paper"`: Paper
  - `"book"`: Book
  - `"laws"`: Laws
  - `"presentation"`: Presentation
  - `"picture"`: Picture
  - `"one"`: One
  - `"knowledge_graph"`: Knowledge Graph
  - `"email"`: Email
- `"parser_config"`: (*Body parameter*), `dict[string, Any]`
  The parsing configuration for the document:  
  - `"chunk_token_count"`: Defaults to `128`.
  - `"layout_recognize"`: Defaults to `True`.
  - `"delimiter"`: Defaults to `"\n!?。；！？"`.
  - `"task_page_size"`: Defaults to `12`.

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0
}
```
  
An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "The dataset does not have the document."
}
```

---

## Download document

**GET** `/api/v1/dataset/{dataset_id}/document/{document_id}`

Downloads a document from a specified dataset.

### Request

- Method: GET
- URL: `/api/v1/dataset/{dataset_id}/document/{document_id}`
- Headers:
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Output:
  - `'{FILE_NAME}'`

#### Request example

```bash
curl --request GET \
  --url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id} \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --output ./ragflow.txt
```

#### Request parameters

- `"dataset_id"`: (*Path parameter*)
  The dataset ID.
- `"documents_id"`: (*Path parameter*)  
  The ID of the document to download.

### Response

The successful response includes a text object like the following:

```text
test_2.
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "You do not own the dataset 7898da028a0511efbf750242ac1220005."
}
```

---

## List documents

**GET** `/api/v1/dataset/{dataset_id}/info?offset={offset}&limit={limit}&orderby={orderby}&desc={desc}&keywords={keywords}&id={document_id}`

Lists documents in a specified dataset.

### Request

- Method: GET
- URL: `/api/v1/dataset/{dataset_id}/info?keywords={keyword}&page={page}&page_size={limit}&orderby={orderby}&desc={desc}&name={name`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`

#### Request example

```bash
curl --request GET \
  --url http://{address}/api/v1/dataset/{dataset_id}/info?offset={offset}&limit={limit}&orderby={orderby}&desc={desc}&keywords={keywords}&id={document_id} \
  --header 'Authorization: Bearer {YOUR_API_KEY}'
```

#### Request parameters

- `"dataset_id"`: (*Path parameter*)  
  The dataset ID.
- `"keywords"`: (*Filter parameter*), `string`  
  The keywords used to match document titles. Defaults to `None`.
- `"offset"`: (*Filter parameter*), `integer`  
  The starting index for the documents to retrieve. Typically used in conjunction with `limit`. Defaults to `1`.
- `"limit"`: (*Filter parameter*), `integer`  
  The maximum number of documents to retrieve. Defaults to `1024`.
- `"orderby"`: (*Filter parameter*), `string`  
  The field by which documents should be sorted. Available options:
  - `"create_time"` (default)
  - `"update_time"`
- `"desc"`: (*Filter parameter*), `bool`  
  Indicates whether the retrieved documents should be sorted in descending order. Defaults to `True`.
- `"document_id"`: (*Filter parameter*)  
  The ID of the document to retrieve. Defaults to `None`.

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0,
    "data": {
        "docs": [
            {
                "chunk_count": 0,
                "create_date": "Mon, 14 Oct 2024 09:11:01 GMT",
                "create_time": 1728897061948,
                "created_by": "69736c5e723611efb51b0242ac120007",
                "id": "3bcfbf8a8a0c11ef8aba0242ac120006",
                "knowledgebase_id": "7898da028a0511efbf750242ac120005",
                "location": "Test_2.txt",
                "name": "Test_2.txt",
                "parser_config": {
                    "chunk_token_count": 128,
                    "delimiter": "\n!?。；！？",
                    "layout_recognize": true,
                    "task_page_size": 12
                },
                "parser_method": "naive",
                "process_begin_at": null,
                "process_duation": 0.0,
                "progress": 0.0,
                "progress_msg": "",
                "run": "0",
                "size": 7,
                "source_type": "local",
                "status": "1",
                "thumbnail": null,
                "token_count": 0,
                "type": "doc",
                "update_date": "Mon, 14 Oct 2024 09:11:01 GMT",
                "update_time": 1728897061948
            }
        ],
        "total": 1
    }
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "You don't own the dataset 7898da028a0511efbf750242ac1220005. "
}
```

---

## Delete documents

**DELETE** `/api/v1/dataset/{dataset_id}/document`

Deletes documents by ID.

### Request

- Method: DELETE
- URL: `/api/v1/dataset/{dataset_id}/document`
- Headers:
  - `'Content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body:
  - `"ids"`: `list[string]`

#### Request example

```bash
curl --request DELETE \
  --url http://{address}/api/v1/dataset/{dataset_id}/document \
  --header 'Content-Type: application/json' \
  --header 'Authorization: {YOUR_API_KEY}' \
  --data '{
  "ids": ["id_1","id_2"]
  }'
```

#### Request parameters

- `"ids"`: (*Body parameter*), `list[string]`
  The IDs of the documents to delete.

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0
}.
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "You do not own the dataset 7898da028a0511efbf750242ac1220005."
}
```

---

## Parse documents

**POST** `/api/v1/dataset/{dataset_id}/chunk`

Parses documents in a specified dataset.

### Request

- Method: POST
- URL: `/api/v1/dataset/{dataset_id}/chunk`
- Headers:
  - `'content-Type: application/json'`
  - 'Authorization: Bearer {YOUR_API_KEY}'
- Body:
  - `"document_ids"`: `list[string]`

#### Request example

```bash
curl --request POST \
    --url http://{address}/api/v1/dataset/{dataset_id}/chunk \
    --header 'Content-Type: application/json' \
    --header 'Authorization: Bearer {YOUR_API_KEY}' \
    --data '{"document_ids": ["97a5f1c2759811efaa500242ac120004","97ad64b6759811ef9fc30242ac120004"]}'
```

#### Request parameters

- `"dataset_id"`: (*Path parameter*)
- `"document_ids"`: (*Body parameter*)  
  The ids of the documents to parse.

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0
}
```
  
An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "`document_ids` is required"
}
```

---

## Stop parsing documents

**DELETE** `/api/v1/dataset/{dataset_id}/chunk`

Stops parsing specified documents.

### Request

- Method: DELETE
- URL: `/api/v1/dataset/{dataset_id}/chunk`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body:
  - `"document_ids"`: `list[string]`

#### Request example

```bash
curl --request DELETE \
   --url http://{address}/api/v1/dataset/{dataset_id}/chunk \
   --header 'Content-Type: application/json' \
   --header 'Authorization: Bearer {YOUR_API_KEY}' \
   --data '{"document_ids": ["97a5f1c2759811efaa500242ac120004","97ad64b6759811ef9fc30242ac120004"]}'
```

#### Request parameters

- `"dataset_id"`: (*Path parameter*)
- `"document_ids"`: (*Body parameter*)  
  The IDs of the documents to parse.

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0
}
```
  
An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "`document_ids` is required"
}
```

---


## Add chunks

**POST** `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk`

Adds a chunk to a specified document in a specified dataset.

### Request

- Method: POST
- URL: `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body:
  - `"content"`: string
  - `"important_keywords"`: `list[string]`

#### Request example

```bash
curl --request POST \
  --url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --data '{
    "content": "ragflow content"
}'
```

#### Request parameters

- `"content"`: (*Body parameter*)  
  Contains the main text or information of the chunk.
- `"important_keywords`(*Body parameter*)  
  List the key terms or phrases that are significant or central to the chunk's content.

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0,
    "data": {
        "chunk": {
            "content": "ragflow content",
            "create_time": "2024-10-16 08:05:04",
            "create_timestamp": 1729065904.581025,
            "dataset_id": [
                "c7ee74067a2c11efb21c0242ac120006"
            ],
            "document_id": "5c5999ec7be811ef9cab0242ac120005",
            "id": "d78435d142bd5cf6704da62c778795c5",
            "important_keywords": []
        }
    }
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "`content` is required"
}
```

---

## List chunks

**GET** `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk?keywords={keywords}&offset={offset}&limit={limit}&id={id}`

Lists chunks in a specified document.

### Request

- Method: GET
- URL: `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk?keywords={keywords}&offset={offset}&limit={limit}&id={id}`
- Headers:
  - `'Authorization: Bearer {YOUR_API_KEY}'`

#### Request example

```bash
curl --request GET \
  --url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk?keywords={keywords}&offset={offset}&limit={limit}&id={id} \
  --header 'Authorization: Bearer {YOUR_API_KEY}' 
```

#### Request parameters

- `"dataset_id"`: (*Path parameter*)
- `"document_id"`: (*Path parameter*)
- `"offset"`(*Filter parameter*)  
  The beginning number of records for paging.
- `"keywords"`(*Filter parameter*)  
  List chunks whose name has the given keywords.
- `"limit"`(*Filter parameter*)  
  Records number to return.
- `"id"`(*Filter parameter*)  
  The ID of chunk to retrieve.

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0,
    "data": {
        "chunks": [],
        "doc": {
            "chunk_num": 0,
            "create_date": "Sun, 29 Sep 2024 03:47:29 GMT",
            "create_time": 1727581649216,
            "created_by": "69736c5e723611efb51b0242ac120007",
            "id": "8cb781ec7e1511ef98ac0242ac120006",
            "kb_id": "c7ee74067a2c11efb21c0242ac120006",
            "location": "sunny_tomorrow.txt",
            "name": "sunny_tomorrow.txt",
            "parser_config": {
                "pages": [
                    [
                        1,
                        1000000
                    ]
                ]
            },
            "parser_id": "naive",
            "process_begin_at": "Tue, 15 Oct 2024 10:23:51 GMT",
            "process_duation": 1435.37,
            "progress": 0.0370833,
            "progress_msg": "\nTask has been received.",
            "run": "1",
            "size": 24,
            "source_type": "local",
            "status": "1",
            "thumbnail": null,
            "token_num": 0,
            "type": "doc",
            "update_date": "Tue, 15 Oct 2024 10:47:46 GMT",
            "update_time": 1728989266371
        },
        "total": 0
    }
}
```
  
An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "You don't own the document 5c5999ec7be811ef9cab0242ac12000e5."
}
```

---

## Delete chunks

**DELETE** `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk`

Deletes chunks by ID.

### Request

- Method: DELETE
- URL: `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body:
  - `"chunk_ids"`: `list[string]`

#### Request example

```bash
curl --request DELETE \
  --url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --data '{
  "chunk_ids": ["test_1", "test_2"]
  }'
```

#### Request parameters

- `"chunk_ids"`: (*Body parameter*)  
  The chunks of the document to delete.

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "`chunk_ids` is required"
}
```

---

## Update chunk

**PUT** `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk/{chunk_id}`

Updates content or configurations for a specified chunk.

### Request

- Method: PUT
- URL: `/api/v1/dataset/{dataset_id}/document/{document_id}/chunk/{chunk_id}`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body:
  - `"content"`: `string`
  - `"important_keywords"`: `string`
  - `"available"`: `integer`

#### Request example

```bash
curl --request PUT \
  --url http://{address}/api/v1/dataset/{dataset_id}/document/{document_id}/chunk/{chunk_id} \
  --header 'Content-Type: application/json' \
  --header 'Authorization: {YOUR_API_KEY}' \
  --data '{   
    "content": "ragflow123",  
    "important_keywords": [],   
}'
```

#### Request parameters

- `"content"`: (*Body parameter*)  
  Contains the main text or information of the chunk.
- `"important_keywords"`: (*Body parameter*)  
  Lists the key terms or phrases that are significant or central to the chunk's content.
- `"available"`: (*Body parameter*)  
   Indicating the availability status, 0 means unavailable and 1 means available.

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "Can't find this chunk 29a2d9987e16ba331fb4d7d30d99b71d2"
}
```

---

## Retrieve chunks

**GET** `/api/v1/retrieval`

Retrieves chunks from specified datasets.

### Request

- Method: POST
- URL: `/api/v1/retrieval`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body:
  - `"question"`: `string`  
  - `"datasets"`: `list[string]`  
  - `"documents"`: `list[string]`
  - `"offset"`: int  
  - `"limit"`: int  
  - `"similarity_threshold"`: float  
  - `"vector_similarity_weight"`: float  
  - `"top_k"`: int  
  - `"rerank_id"`: string  
  - `"keyword"`: bool  
  - `"highlight"`: bool

#### Request example

```bash
curl --request POST \
  --url http://{address}/api/v1/retrieval \
  --header 'Content-Type: application/json' \
  --header 'Authorization: {YOUR_API_KEY}' \
  --data '{
    "question": "What is advantage of ragflow?",
    "datasets": [
        "b2a62730759d11ef987d0242ac120004"
    ],
    "documents": [
        "77df9ef4759a11ef8bdd0242ac120004"
    ]
}'
```

#### Request parameter

- `"question"`: (*Body parameter*)  
  User's question, search keywords  
  `""`
- `"datasets"`: (*Body parameter*)  
  The scope of datasets  
  `None`
- `"documents"`: (*Body parameter*)  
  The scope of document. `None` means no limitation  
  `None`
- `"offset"`: (*Body parameter*)  
  The beginning point of retrieved records  
  `1`

- `"limit"`: (*Body parameter*)  
  The maximum number of records needed to return  
  `30`

- `"similarity_threshold"`: (*Body parameter*)  
  The minimum similarity score  
  `0.2`

- `"vector_similarity_weight"`: (*Body parameter*)  
  The weight of vector cosine similarity, `1 - x` is the term similarity weight  
  `0.3`

- `"top_k"`: (*Body parameter*)  
  Number of records engaged in vector cosine computation  
  `1024`

- `"rerank_id"`: (*Body parameter*)  
  ID of the rerank model  
  `None`

- `"keyword"`: (*Body parameter*)  
  Whether keyword-based matching is enabled  
  `False`

- `"highlight"`: (*Body parameter*)  
  Whether to enable highlighting of matched terms in the results  
  `False`

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0,
    "data": {
        "chunks": [
            {
                "content": "ragflow content",
                "content_ltks": "ragflow content",
                "document_id": "5c5999ec7be811ef9cab0242ac120005",
                "document_keyword": "1.txt",
                "highlight": "<em>ragflow</em> content",
                "id": "d78435d142bd5cf6704da62c778795c5",
                "img_id": "",
                "important_keywords": [
                    ""
                ],
                "kb_id": "c7ee74067a2c11efb21c0242ac120006",
                "positions": [
                    ""
                ],
                "similarity": 0.9669436601210759,
                "term_similarity": 1.0,
                "vector_similarity": 0.8898122004035864
            }
        ],
        "doc_aggs": [
            {
                "count": 1,
                "doc_id": "5c5999ec7be811ef9cab0242ac120005",
                "doc_name": "1.txt"
            }
        ],
        "total": 1
    }
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "`datasets` is required."
}
```

---

:::tip API GROUPING
Chat Assistant Management
:::

---

## Create chat assistant

**POST** `/api/v1/chat`

Creates a chat assistant.

### Request

- Method: POST
- URL: `/api/v1/chat`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body:
  - `"name"`: `string`
  - `"avatar"`: `string`
  - `"knowledgebases"`: `list[DataSet]`
  - `"id"`: `string`
  - `"llm"`: `LLM`
  - `"prompt"`: `Prompt`

#### Request example

```shell
curl --request POST \
     --url http://{address}/api/v1/chat \
     --header 'Content-Type: application/json' \
     --header 'Authorization: Bearer {YOUR_API_KEY}'
     --data-binary '{
   "knowledgebases": [
    {
      "avatar": null,
      "chunk_count": 0,
      "description": null,
      "document_count": 0,
      "embedding_model": "",
      "id": "0b2cbc8c877f11ef89070242ac120005",
      "language": "English",
      "name": "Test_assistant",
      "parse_method": "naive",
      "parser_config": {
        "pages": [
          [
            1,
            1000000
          ]
        ]
      },
      "permission": "me",
      "tenant_id": "4fb0cd625f9311efba4a0242ac120006"
    }
  ],
    "name":"new_chat_1"
}'
```

#### Request parameters

- `"name"`: (*Body parameter*)  
  The name of the created chat.  
  - `"assistant"`

- `"avatar"`: (*Body parameter*)  
  The icon of the created chat.  
  - `"path"`

- `"knowledgebases"`: (*Body parameter*)  
  Select knowledgebases associated.  
  - `["kb1"]`

- `"id"`: (*Body parameter*)  
  The id of the created chat.  
  - `""`

- `"llm"`: (*Body parameter*)  
  The LLM of the created chat.  
  - If the value is `None`, a dictionary with default values will be generated.

- `"prompt"`: (*Body parameter*)  
  The prompt of the created chat.  
  - If the value is `None`, a dictionary with default values will be generated.

---

##### Chat.LLM parameters

- `"model_name"`: (*Body parameter*)  
  Large language chat model.  
  - If it is `None`, it will return the user's default model.

- `"temperature"`: (*Body parameter*)  
  Controls the randomness of predictions by the model. A lower temperature makes the model more confident, while a higher temperature makes it more creative and diverse.  
  - `0.1`

- `"top_p"`: (*Body parameter*)  
  Also known as "nucleus sampling," it focuses on the most likely words, cutting off the less probable ones.  
  - `0.3`

- `"presence_penalty"`: (*Body parameter*)  
  Discourages the model from repeating the same information by penalizing repeated content.  
  - `0.4`

- `"frequency_penalty"`: (*Body parameter*)  
  Reduces the model’s tendency to repeat words frequently.  
  - `0.7`

- `"max_tokens"`: (*Body parameter*)  
  Sets the maximum length of the model’s output, measured in tokens (words or pieces of words).  
  - `512`

---

##### Chat.Prompt parameters

- `"similarity_threshold"`: (*Body parameter*)  
  Filters out chunks with similarity below this threshold.  
  - `0.2`

- `"keywords_similarity_weight"`: (*Body parameter*)  
  Weighted keywords similarity and vector cosine similarity; the sum of weights is 1.0.  
  - `0.7`

- `"top_n"`: (*Body parameter*)  
  Only the top N chunks above the similarity threshold will be fed to LLMs.  
  - `8`

- `"variables"`: (*Body parameter*)  
  Variables help with different chat strategies by filling in the 'System' part of the prompt.  
  - `[{"key": "knowledge", "optional": True}]`

- `"rerank_model"`: (*Body parameter*)  
  If empty, it uses vector cosine similarity; otherwise, it uses rerank score.  
  - `""`

- `"empty_response"`: (*Body parameter*)  
  If nothing is retrieved, this will be used as the response. Leave blank if LLM should provide its own opinion.  
  - `None`

- `"opener"`: (*Body parameter*)  
  The welcome message for clients.  
  - `"Hi! I'm your assistant, what can I do for you?"`

- `"show_quote"`: (*Body parameter*)  
  Indicates whether the source of the original text should be displayed.  
  - `True`

- `"prompt"`: (*Body parameter*)  
  Instructions for LLM to follow when answering questions, such as character design or answer length.  
  - `"You are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence 'The answer you are looking for is not found in the knowledge base!' Answers need to consider chat history. Here is the knowledge base: {knowledge} The above is the knowledge base."`

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0,
    "data": {
        "avatar": "",
        "create_date": "Fri, 11 Oct 2024 03:23:24 GMT",
        "create_time": 1728617004635,
        "description": "A helpful Assistant",
        "do_refer": "1",
        "id": "2ca4b22e878011ef88fe0242ac120005",
        "knowledgebases": [
            {
                "avatar": null,
                "chunk_count": 0,
                "description": null,
                "document_count": 0,
                "embedding_model": "",
                "id": "0b2cbc8c877f11ef89070242ac120005",
                "language": "English",
                "name": "Test_assistant",
                "parse_method": "naive",
                "parser_config": {
                    "pages": [
                        [
                            1,
                            1000000
                        ]
                    ]
                },
                "permission": "me",
                "tenant_id": "4fb0cd625f9311efba4a0242ac120006"
            }
        ],
        "language": "English",
        "llm": {
            "frequency_penalty": 0.7,
            "max_tokens": 512,
            "model_name": "deepseek-chat___OpenAI-API@OpenAI-API-Compatible",
            "presence_penalty": 0.4,
            "temperature": 0.1,
            "top_p": 0.3
        },
        "name": "new_chat_1",
        "prompt": {
            "empty_response": "Sorry! 知识库中未找到相关内容！",
            "keywords_similarity_weight": 0.3,
            "opener": "您好，我是您的助手小樱，长得可爱又善良，can I help you?",
            "prompt": "你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。\n            以下是知识库：\n            {knowledge}\n            以上是知识库。",
            "rerank_model": "",
            "similarity_threshold": 0.2,
            "top_n": 6,
            "variables": [
                {
                    "key": "knowledge",
                    "optional": false
                }
            ]
        },
        "prompt_type": "simple",
        "status": "1",
        "tenant_id": "69736c5e723611efb51b0242ac120007",
        "top_k": 1024,
        "update_date": "Fri, 11 Oct 2024 03:23:24 GMT",
        "update_time": 1728617004635
    }
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "Duplicated chat name in creating dataset."
}
```

---

## Update chat assistant

**PUT** `/api/v1/chat/{chat_id}`

Updates configurations for a specified chat assistant.

### Request

- Method: PUT
- URL: `/api/v1/chat/{chat_id}`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body: (Refer to the "Create chat" for the complete structure of the request body.)
  
#### Request example

```bash
curl --request PUT \
  --url http://{address}/api/v1/chat/{chat_id} \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --data '{
    "name":"Test"
}'
```

#### Parameters

Refer to the "Create chat" for the complete structure of the request parameters.

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "Duplicated chat name in updating dataset."
}
```

---

## Delete chat assistants

**DELETE** `/api/v1/chat`

Deletes chat assistants by ID.

### Request

- Method: DELETE
- URL: `/api/v1/chat`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body:
  - `"ids"`: `list[string]`

#### Request example

```bash
# Either id or name must be provided, but not both.
curl --request DELETE \
  --url http://{address}/api/v1/chat \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --data '{
  "ids": ["test_1", "test_2"]
  }'
}'
```

#### Request parameters

- `"ids"`: (*Body parameter*)  
  IDs of the chats to delete.  
  - `None`

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "ids are required"
}
```

---

## List chats

**GET** `/api/v1/chat?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={chat_name}&id={chat_id}`

Lists chat assistants.

### Request

- Method: GET
- URL: `/api/v1/chat?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id}`
- Headers:
  - `'Authorization: Bearer {YOUR_API_KEY}'`

#### Request example

```bash
curl --request GET \
  --url http://{address}/api/v1/chat?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id} \
  --header 'Authorization: Bearer {YOUR_API_KEY}'
```

#### Request parameters

- `"page"`: (*Path parameter*)  
  The current page number to retrieve from the paginated data. This parameter determines which set of records will be fetched.  
  - `1`

- `"page_size"`: (*Path parameter*)  
  The number of records to retrieve per page. This controls how many records will be included in each page.  
  - `1024`

- `"orderby"`: (*Path parameter*)  
  The field by which the records should be sorted. This specifies the attribute or column used to order the results.  
  - `"create_time"`

- `"desc"`: (*Path parameter*)  
  A boolean flag indicating whether the sorting should be in descending order.  
  - `True`

- `"id"`: (*Path parameter*)  
  The ID of the chat to retrieve.  
  - `None`

- `"name"`: (*Path parameter*)  
  The name of the chat to retrieve.  
  - `None`

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0,
    "data": [
        {
            "avatar": "",
            "create_date": "Fri, 11 Oct 2024 03:23:24 GMT",
            "create_time": 1728617004635,
            "description": "A helpful Assistant",
            "do_refer": "1",
            "id": "2ca4b22e878011ef88fe0242ac120005",
            "knowledgebases": [
                {
                    "avatar": "",
                    "chunk_num": 0,
                    "create_date": "Fri, 11 Oct 2024 03:15:18 GMT",
                    "create_time": 1728616518986,
                    "created_by": "69736c5e723611efb51b0242ac120007",
                    "description": "",
                    "doc_num": 0,
                    "embd_id": "BAAI/bge-large-zh-v1.5",
                    "id": "0b2cbc8c877f11ef89070242ac120005",
                    "language": "English",
                    "name": "test_delete_chat",
                    "parser_config": {
                        "chunk_token_count": 128,
                        "delimiter": "\n!?。；！？",
                        "layout_recognize": true,
                        "task_page_size": 12
                    },
                    "parser_id": "naive",
                    "permission": "me",
                    "similarity_threshold": 0.2,
                    "status": "1",
                    "tenant_id": "69736c5e723611efb51b0242ac120007",
                    "token_num": 0,
                    "update_date": "Fri, 11 Oct 2024 04:01:31 GMT",
                    "update_time": 1728619291228,
                    "vector_similarity_weight": 0.3
                }
            ],
            "language": "English",
            "llm": {
                "frequency_penalty": 0.7,
                "max_tokens": 512,
                "model_name": "deepseek-chat___OpenAI-API@OpenAI-API-Compatible",
                "presence_penalty": 0.4,
                "temperature": 0.1,
                "top_p": 0.3
            },
            "name": "Test",
            "prompt": {
                "empty_response": "Sorry! 知识库中未找到相关内容！",
                "keywords_similarity_weight": 0.3,
                "opener": "您好，我是您的助手小樱，长得可爱又善良，can I help you?",
                "prompt": "你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。\n            以下是知识库：\n            {knowledge}\n            以上是知识库。",
                "rerank_model": "",
                "similarity_threshold": 0.2,
                "top_n": 6,
                "variables": [
                    {
                        "key": "knowledge",
                        "optional": false
                    }
                ]
            },
            "prompt_type": "simple",
            "status": "1",
            "tenant_id": "69736c5e723611efb51b0242ac120007",
            "top_k": 1024,
            "update_date": "Fri, 11 Oct 2024 03:47:58 GMT",
            "update_time": 1728618478392
        }
    ]
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "The chat doesn't exist"
}
```

## Create chat session

**POST** `/api/v1/chat/{chat_id}/session`

Create a chat session.

### Request

- Method: POST
- URL: `/api/v1/chat/{chat_id}/session`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body:
  - `"name"`: `string`

#### Request example

```bash
curl --request POST \
  --url http://{address}/api/v1/chat/{chat_id}/session \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --data '{
    "name": "new session"
  }'
```

#### Request parameters

- `"id"`: (*Body parameter*)  
  The ID of the created session used to identify different sessions.  
  - `None`  
  - `id` cannot be provided when creating.

- `"name"`: (*Body parameter*)  
  The name of the created session.  
  - `"New session"`

- `"messages"`: (*Body parameter*)  
  The messages of the created session.  
  - `[{"role": "assistant", "content": "Hi! I am your assistant, can I help you?"}]`  
  - `messages` cannot be provided when creating.

- `"chat_id"`: (*Path parameter*)  
  The ID of the associated chat.  
  - `""`  
  - `chat_id` cannot be changed.


### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0,
    "data": {
        "chat_id": "2ca4b22e878011ef88fe0242ac120005",
        "create_date": "Fri, 11 Oct 2024 08:46:14 GMT",
        "create_time": 1728636374571,
        "id": "4606b4ec87ad11efbc4f0242ac120006",
        "messages": [
            {
                "content": "Hi! I am your assistant，can I help you?",
                "role": "assistant"
            }
        ],
        "name": "new session",
        "update_date": "Fri, 11 Oct 2024 08:46:14 GMT",
        "update_time": 1728636374571
    }
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "Name can not be empty."
}
```

---

:::tip API GROUPING
Chat Session APIs
:::

---

=========MISSING CREATE SESSION API!==============

---
## Update a chat session

**PUT** `/api/v1/chat/{chat_id}/session/{session_id}`

Update a chat session

### Request

- Method: PUT
- URL: `/api/v1/chat/{chat_id}/session/{session_id}`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body:
  - `"name`: string

#### Request example
```bash
curl --request PUT \
  --url http://{address}/api/v1/chat/{chat_id}/session/{session_id} \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --data '{
    "name": "Updated session"
  }'

```

#### Request Parameter

- `"name`: (*Body Parameter)  
  The name of the created session.  
  - `None`

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "Name can not be empty."
}
```

---

## List sessions

**GET** `/api/v1/chat/{chat_id}/session?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id}`

Lists sessions associated with a specified????????????? chat assistant.

### Request

- Method: GET
- URL: `/api/v1/chat/{chat_id}/session?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id}`
- Headers:
  - `'Authorization: Bearer {YOUR_API_KEY}'`

#### Request example

```bash
curl --request GET \
  --url http://{address}/api/v1/chat/{chat_id}/session?page={page}&page_size={page_size}&orderby={orderby}&desc={desc}&name={dataset_name}&id={dataset_id} \
  --header 'Authorization: Bearer {YOUR_API_KEY}'
```

#### Request Parameters

- `"page"`: (*Path parameter*)  
  The current page number to retrieve from the paginated data. This parameter determines which set of records will be fetched.  
  - `1`

- `"page_size"`: (*Path parameter*)  
  The number of records to retrieve per page. This controls how many records will be included in each page.  
  - `1024`

- `"orderby"`: (*Path parameter*)  
  The field by which the records should be sorted. This specifies the attribute or column used to order the results.  
  - `"create_time"`

- `"desc"`: (*Path parameter*)  
  A boolean flag indicating whether the sorting should be in descending order.  
  - `True`

- `"id"`: (*Path parameter*)  
  The ID of the session to retrieve.  
  - `None`

- `"name"`: (*Path parameter*)  
  The name of the session to retrieve.  
  - `None`

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0,
    "data": [
        {
            "chat": "2ca4b22e878011ef88fe0242ac120005",
            "create_date": "Fri, 11 Oct 2024 08:46:43 GMT",
            "create_time": 1728636403974,
            "id": "578d541e87ad11ef96b90242ac120006",
            "messages": [
                {
                    "content": "Hi! I am your assistant，can I help you?",
                    "role": "assistant"
                }
            ],
            "name": "new session",
            "update_date": "Fri, 11 Oct 2024 08:46:43 GMT",
            "update_time": 1728636403974
        }
    ]
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "The session doesn't exist"
}
```

---

## Delete sessions

**DELETE** `/api/v1/chat/{chat_id}/session`

Deletes sessions by ID.

### Request

- Method: DELETE
- URL: `/api/v1/chat/{chat_id}/session`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body:
  - `"ids"`: `list[string]`

#### Request example

```bash
# Either id or name must be provided, but not both.
curl --request DELETE \
--url http://{address}/api/v1/chat/{chat_id}/session \
--header 'Content-Type: application/json' \
--header 'Authorization: Bear {YOUR_API_KEY}' \
  --data '{
  "ids": ["test_1", "test_2"]
  }'
```

#### Request Parameters

- `"ids"`: (*Body Parameter*)  
  IDs of the sessions to delete.
  - `None`

### Response

A successful response includes a JSON object like the following:

```json
{
    "code": 0
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "The chat doesn't own the session"
}
```

---

## Chat with a chat session???

**POST** `/api/v1/chat/{chat_id}/completion`

Asks a question to start a conversation.

### Request

- Method: POST
- URL: `/api/v1/chat/{chat_id}/completion`
- Headers:
  - `'content-Type: application/json'`
  - `'Authorization: Bearer {YOUR_API_KEY}'`
- Body:
  - `"question"`: `string`
  - `"stream"`: `bool`
  - `"session_id"`: `string`

#### Request example

```bash
curl --request POST \
  --url http://{address} /api/v1/chat/{chat_id}/completion \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer {YOUR_API_KEY}' \
  --data-binary '{
    "question":  "你好!",
    "stream": true
  }'
```

#### Request Parameters

- `"question"`: (*Body Parameter*)  
  The question you want to ask.  
  - question is required.
  `None`
- `"stream"`: (*Body Parameter*)  
  The approach of streaming text generation.  
  `False`
- `"session_id"`: (*Body Parameter*)  
  The ID of session. If not provided, a new session will be generated.

### Response

A successful response includes a JSON object like the following:

```json
data: {
    "code": 0,
    "data": {
        "answer": "您好！有什么具体的问题或者需要的帮助",
        "reference": {},
        "audio_binary": null,
        "id": "31153052-7bac-4741-a513-ed07d853f29e"
    }
}

data: {
    "code": 0,
    "data": {
        "answer": "您好！有什么具体的问题或者需要的帮助可以告诉我吗？我在这里是为了帮助",
        "reference": {},
        "audio_binary": null,
        "id": "31153052-7bac-4741-a513-ed07d853f29e"
    }
}

data: {
    "code": 0,
    "data": {
        "answer": "您好！有什么具体的问题或者需要的帮助可以告诉我吗？我在这里是为了帮助您的。如果您有任何疑问或是需要获取",
        "reference": {},
        "audio_binary": null,
        "id": "31153052-7bac-4741-a513-ed07d853f29e"
    }
}

data: {
    "code": 0,
    "data": {
        "answer": "您好！有什么具体的问题或者需要的帮助可以告诉我吗？我在这里是为了帮助您的。如果您有任何疑问或是需要获取某些信息，请随时提出。",
        "reference": {},
        "audio_binary": null,
        "id": "31153052-7bac-4741-a513-ed07d853f29e"
    }
}

data: {
    "code": 0,
    "data": {
        "answer": "您好！有什么具体的问题或者需要的帮助可以告诉我吗 ##0$$？我在这里是为了帮助您的。如果您有任何疑问或是需要获取某些信息，请随时提出。",
        "reference": {
            "total": 19,
            "chunks": [
                {
                    "chunk_id": "9d87f9d70a0d8a7565694a81fd4c5d5f",
                    "content_ltks": "当所有知识库内容都与问题无关时 ,你的回答必须包括“知识库中未找到您要的答案!”这句话。回答需要考虑聊天历史。\r\n以下是知识库:\r\n{knowledg}\r\n以上是知识库\r\n\"\"\"\r\n 1\r\n 2\r\n 3\r\n 4\r\n 5\r\n 6\r\n总结\r\n通过上面的介绍,可以对开源的 ragflow有了一个大致的了解,与前面的有道qanyth整体流程还是比较类似的。 ",
                    "content_with_weight": "当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。\r\n    以下是知识库：\r\n    {knowledge}\r\n    以上是知识库\r\n\"\"\"\r\n1\r\n2\r\n3\r\n4\r\n5\r\n6\r\n总结\r\n通过上面的介绍，可以对开源的 RagFlow 有了一个大致的了解，与前面的 有道 QAnything 整体流程还是比较类似的。",
                    "doc_id": "5c5999ec7be811ef9cab0242ac120005",
                    "docnm_kwd": "1.txt",
                    "kb_id": "c7ee74067a2c11efb21c0242ac120006",
                    "important_kwd": [],
                    "img_id": "",
                    "similarity": 0.38337178633282265,
                    "vector_similarity": 0.3321336754679629,
                    "term_similarity": 0.4053309767034769,
                    "positions": [
                        ""
                    ]
                },
                {
                    "chunk_id": "895d34de762e674b43e8613c6fb54c6d",
                    "content_ltks": "\r\n\r\n实际内容可能会超过大模型的输入token数量,因此在调用大模型前会调用api/db/servic/dialog_service.py文件中 messag_fit_in ()根据大模型可用的 token数量进行过滤。这部分与有道的 qanyth的实现大同小异,就不额外展开了。\r\n\r\n将检索的内容,历史聊天记录以及问题构造为 prompt ,即可作为大模型的输入了 ,默认的英文prompt如下所示:\r\n\r\n\"\"\"\r\nyou are an intellig assistant. pleas summar the content of the knowledg base to answer the question. pleas list thedata in the knowledg base and answer in detail. when all knowledg base content is irrelev to the question , your answer must includ the sentenc\"the answer you are lookfor isnot found in the knowledg base!\" answer needto consid chat history.\r\n here is the knowledg base:\r\n{ knowledg}\r\nthe abov is the knowledg base.\r\n\"\"\"\r\n1\r\n 2\r\n 3\r\n 4\r\n 5\r\n 6\r\n对应的中文prompt如下所示:\r\n\r\n\"\"\"\r\n你是一个智能助手,请总结知识库的内容来回答问题,请列举知识库中的数据详细回答。 ",
                    "content_with_weight": "\r\n\r\n实际内容可能会超过大模型的输入 token 数量，因此在调用大模型前会调用 api/db/services/dialog_service.py 文件中 message_fit_in() 根据大模型可用的 token 数量进行过滤。这部分与有道的 QAnything 的实现大同小异，就不额外展开了。\r\n\r\n将检索的内容，历史聊天记录以及问题构造为 prompt，即可作为大模型的输入了，默认的英文 prompt 如下所示：\r\n\r\n\"\"\"\r\nYou are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\r\n      Here is the knowledge base:\r\n      {knowledge}\r\n      The above is the knowledge base.\r\n\"\"\"\r\n1\r\n2\r\n3\r\n4\r\n5\r\n6\r\n对应的中文 prompt 如下所示：\r\n\r\n\"\"\"\r\n你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。",
                    "doc_id": "5c5999ec7be811ef9cab0242ac120005",
                    "docnm_kwd": "1.txt",
                    "kb_id": "c7ee74067a2c11efb21c0242ac120006",
                    "important_kwd": [],
                    "img_id": "",
                    "similarity": 0.2788204323926715,
                    "vector_similarity": 0.35489427679953667,
                    "term_similarity": 0.2462173562183008,
                    "positions": [
                        ""
                    ]
                }
            ],
            "doc_aggs": [
                {
                    "doc_name": "1.txt",
                    "doc_id": "5c5999ec7be811ef9cab0242ac120005",
                    "count": 2
                }
            ]
        },
        "prompt": "你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。\n            以下是知识库：\n            当所有知识库内容都与问题无关时，你的回答必须包括“知识库中未找到您要的答案！”这句话。回答需要考虑聊天历史。\r\n    以下是知识库：\r\n    {knowledge}\r\n    以上是知识库\r\n\"\"\"\r\n1\r\n2\r\n3\r\n4\r\n5\r\n6\r\n总结\r\n通过上面的介绍，可以对开源的 RagFlow 有了一个大致的了解，与前面的 有道 QAnything 整体流程还是比较类似的。\n\n------\n\n\r\n\r\n实际内容可能会超过大模型的输入 token 数量，因此在调用大模型前会调用 api/db/services/dialog_service.py 文件中 message_fit_in() 根据大模型可用的 token 数量进行过滤。这部分与有道的 QAnything 的实现大同小异，就不额外展开了。\r\n\r\n将检索的内容，历史聊天记录以及问题构造为 prompt，即可作为大模型的输入了，默认的英文 prompt 如下所示：\r\n\r\n\"\"\"\r\nYou are an intelligent assistant. Please summarize the content of the knowledge base to answer the question. Please list the data in the knowledge base and answer in detail. When all knowledge base content is irrelevant to the question, your answer must include the sentence \"The answer you are looking for is not found in the knowledge base!\" Answers need to consider chat history.\r\n      Here is the knowledge base:\r\n      {knowledge}\r\n      The above is the knowledge base.\r\n\"\"\"\r\n1\r\n2\r\n3\r\n4\r\n5\r\n6\r\n对应的中文 prompt 如下所示：\r\n\r\n\"\"\"\r\n你是一个智能助手，请总结知识库的内容来回答问题，请列举知识库中的数据详细回答。\n            以上是知识库。\n\n### Query:\n你好，请问有什么问题需要我帮忙解答吗？\n\n### Elapsed\n  - Retrieval: 9131.1 ms\n  - LLM: 12802.6 ms",
        "id": "31153052-7bac-4741-a513-ed07d853f29e"
    }
}

data:{
    "code": 0,
    "data": true
}
```

An error response includes a JSON object like the following:

```json
{
    "code": 102,
    "message": "Please input your question."
}
```