> For the complete documentation index, see [llms.txt](https://moonshot-ai.gitbook.io/moonshot-ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://moonshot-ai.gitbook.io/moonshot-ai/api-documentation/files.md).

# Files

### Upload File <a href="#upload-file" id="upload-file"></a>

> Note: Each user can upload a maximum of 1,000 files, with each file not exceeding 100MB in size. The total size of all uploaded files must not exceed 10GB. If you need to upload more files, you will need to delete some of the files that are no longer needed. The file parsing service is currently free, but during peak request periods, the platform may implement rate-limiting strategies.

#### Request Endpoint <a href="#request-endpoint" id="request-endpoint"></a>

```
POST https://api.moonshot.ai/v1/files
```

Once the file is successfully uploaded, we will begin extracting information from the file.

#### Example Request <a href="#example-request" id="example-request"></a>

**Python Example**

```
# The file can be of various types# The purpose currently only supports "file-extract"file_object = client.files.create(file=Path("xlnet.pdf"), purpose="file-extract")
```

#### Supported Formats <a href="#supported-formats" id="supported-formats"></a>

The file interface is the same as the one used in the Kimi intelligent assistant for uploading files, and it supports the same file formats. These include `.pdf`, `.txt`, `.csv`, `.doc`, `.docx`, `.xls`, `.xlsx`, `.ppt`, `.pptx`, `.md`, `.jpeg`, `.png`, `.bmp`, `.gif`, `.svg`, `.svgz`, `.webp`, `.ico`, `.xbm`, `.dib`, `.pjp`, `.tif`, `.pjpeg`, `.avif`, `.dot`, `.apng`, `.epub`, `.tiff`, `.jfif`, `.html`, `.json`, `.mobi`, `.log`, `.go`, `.h`, `.c`, `.cpp`, `.cxx`, `.cc`, `.cs`, `.java`, `.js`, `.css`, `.jsp`, `.php`, `.py`, `.py3`, `.asp`, `.yaml`, `.yml`, `.ini`, `.conf`, `.ts`, `.tsx`, etc.

### Extract File Content <a href="#extract-file-content" id="extract-file-content"></a>

> This feature allows the model to obtain information from the file as context. This feature needs to be used in conjunction with file uploading and other related functions.

#### Example Request <a href="#example-request-1" id="example-request-1"></a>

pythoncurlnode.js

```
from pathlib import Pathfrom openai import OpenAI client = OpenAI(    api_key = "$MOONSHOT_API_KEY",    base_url = "https://api.moonshot.ai/v1",) # xlnet.pdf is an example file; we support pdf, doc, and image formats. For images and pdf files, we provide OCR capabilities.file_object = client.files.create(file=Path("xlnet.pdf"), purpose="file-extract") # Retrieve the result# file_content = client.files.retrieve_content(file_id=file_object.id)# Note: The previous retrieve_content API is marked as deprecated in the latest version. You can use the following line instead.# If you are using an older version, you can use retrieve_content.file_content = client.files.content(file_id=file_object.id).text # Include it in the requestmessages = [    {        "role": "system",        "content": "You are Kimi, an AI assistant provided by Moonshot AI. You are particularly skilled in Chinese and English conversations. You provide users with safe, helpful, and accurate answers. You will refuse to answer any questions involving terrorism, racism, pornography, or violence. Moonshot AI is a proper noun and should not be translated into other languages.",    },    {        "role": "system",        "content": file_content,    },    {"role": "user", "content": "Please give a brief introduction of what xlnet.pdf is about"},] # Then call chat-completion to get Kimi's response completion = client.chat.completions.create(  model="moonshot-v1-32k",  messages=messages,  temperature=0.3,) print(completion.choices[0].message)
```

Replace the `$MOONSHOT_API_KEY` part with your own API Key. Alternatively, you can set it as an environment variable before making the call.

#### Multi-File Chat Example <a href="#multi-file-chat-example" id="multi-file-chat-example"></a>

If you want to upload multiple files at once and have a conversation with Kimi based on these files, you can refer to the following example:

```
from typing import * import osimport jsonfrom pathlib import Path from openai import OpenAI client = OpenAI(    base_url="https://api.moonshot.ai/v1",    # We will get the value of MOONSHOT_DEMO_API_KEY from the environment variable as the API Key.    # Please make sure you have correctly set the value of MOONSHOT_DEMO_API_KEY in the environment variable.    api_key=os.environ["MOONSHOT_DEMO_API_KEY"],)  def upload_files(files: List[str]) -> List[Dict[str, Any]]:    """    upload_files will upload all the files (paths) through the file upload interface '/v1/files' and get the uploaded file content to generate file messages.    Each file will be an independent message, and the role of these messages will be system. The Kimi large language model will correctly identify the file content in these system messages.     :param files: A list containing the paths of the files to be uploaded. The paths can be absolute or relative, and please pass the file paths in the form of strings.    :return: A list of messages containing the file content. Please add these messages to the context, i.e., the messages parameter when requesting the `/v1/chat/completions` interface.    """    messages = []     # For each file path, we will upload the file, extract the file content, and finally generate a message with the role of system, and add it to the final list of messages to be returned.    for file in files:        file_object = client.files.create(file=Path(file), purpose="file-extract")        file_content = client.files.content(file_id=file_object.id).text        messages.append({            "role": "system",            "content": file_content,        })     return messages  def main():    file_messages = upload_files(files=["upload_files.py"])     messages = [        # We use the * syntax to deconstruct the file_messages messages, making them the first N messages in the messages list.        *file_messages,        {            "role": "system",            "content": "You are Kimi, an AI assistant provided by Moonshot AI. You are more proficient in Chinese and English conversations. You provide users with safe, helpful, and accurate answers. You will refuse to answer any questions related to terrorism, racism, pornography, or violence. Moonshot AI is a proper noun and should not be translated into other languages.",        },        {            "role": "user",            "content": "Summarize the content of these files.",        },    ]     print(json.dumps(messages, indent=2, ensure_ascii=False))     completion = client.chat.completions.create(        model="moonshot-v1-128k",        messages=messages,    )     print(completion.choices[0].message.content)  if __name__ == '__main__':    main()
```

If you have a large number of files that are bulky and lengthy, and you don't want to carry the entire content of these large files with each request, or if you are looking for a more efficient and cost-effective way to interact with files, please refer to the [file upload example that uses Context Caching technology](https://platform.moonshot.ai/docs/api/caching).

### List Files <a href="#list-files" id="list-files"></a>

> This feature is used to list all the files that a user has uploaded.

#### Request Address <a href="#request-address" id="request-address"></a>

```
GET https://api.moonshot.ai/v1/files
```

#### Example Request <a href="#example-request-2" id="example-request-2"></a>

**Python Request**

```
file_list = client.files.list() for file in file_list.data:    print(file) # Check the information of each file
```

### Delete File <a href="#delete-file" id="delete-file"></a>

> This feature can be used to delete files that are no longer needed.

#### Request Address <a href="#request-address-1" id="request-address-1"></a>

```
DELETE https://api.moonshot.ai/v1/files/{file_id}
```

#### Example Request <a href="#example-request-3" id="example-request-3"></a>

**Python Request**

```
client.files.delete(file_id=file_id)
```

### Get File Information <a href="#get-file-information" id="get-file-information"></a>

> This feature is used to obtain the basic information of a specified file.

#### Request Address <a href="#request-address-2" id="request-address-2"></a>

```
GET https://api.moonshot.ai/v1/files/{file_id}
```

#### Example Request <a href="#example-request-4" id="example-request-4"></a>

**Python Request**

```
client.files.retrieve(file_id=file_id)# FileObject(# id='clg681objj8g9m7n4je0',# bytes=761790,# created_at=1700815879,# filename='xlnet.pdf',# object='file',# purpose='file-extract',# status='ok', status_details='') # If status is error, extraction has failed
```

### Get File Content <a href="#get-file-content" id="get-file-content"></a>

> This feature supports obtaining the extraction results of a specified file. Typically, it is a valid JSON formatted string and aligns with our recommended format. If you need to extract multiple files, you can concatenate them into a large string separated by newline characters \n in a message, and add them to the history with the role set to system.

#### Request Address <a href="#request-address-3" id="request-address-3"></a>

```
GET https://api.moonshot.ai/v1/files/{file_id}/content
```

#### Example Request <a href="#example-request-5" id="example-request-5"></a>

pythoncurl

```
# file_content = client.files.retrieve_content(file_id=file_object.id)# The type of file_content is `str`# Note: The previous retrieve_content API is marked with a warning in the latest version. You can use the following line instead.# If you are using an older version, you can use retrieve_content.file_content = client.files.content(file_id=file_object.id).text# Our output is currently a JSON in an internally agreed format, but it should be placed in the message as text.
```