A complete reference for RAGFlow's Python APIs. Before proceeding, please ensure you [have your RAGFlow API key ready for authentication](../guides/models/llm_api_key_setup.md).
:::tip NOTE
Run the following command to download the Python SDK:
```bash
pip install ragflow-sdk
```
:::
---
## OpenAI-Compatible API
---
### Create chat completion
Creates a model response for the given historical chat conversation via OpenAI's API.
#### Parameters
##### model: `str`, *Required*
The model used to generate the response. The server will parse this automatically, so you can set it to any value for now.
##### messages: `list[object]`, *Required*
A list of historical chat messages used to generate the response. This must contain at least one message with the `user` role.
##### stream: `boolean`
Whether to receive the response as a stream. Set this to `false` explicitly if you prefer to receive the entire response in one go instead of as a stream.
#### Returns
- Success: Response [message](https://platform.openai.com/docs/api-reference/chat/create) like OpenAI
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Who are you?"},
],
stream=True
)
stream = True
if stream:
for chunk in completion:
print(chunk)
else:
print(completion.choices[0].message.content)
```
## DATASET MANAGEMENT
---
### Create dataset
```python
RAGFlow.create_dataset(
name: str,
avatar: str = "",
description: str = "",
embedding_model: str = "BAAI/bge-large-zh-v1.5",
permission: str = "me",
chunk_method: str = "naive",
parser_config: DataSet.ParserConfig = None
) -> DataSet
```
Creates a dataset.
#### Parameters
##### name: `str`, *Required*
The unique name of the dataset to create. It must adhere to the following requirements:
- Maximum 65,535 characters.
- Case-insensitive.
##### avatar: `str`
Base64 encoding of the avatar. Defaults to `""`
##### description: `str`
A brief description of the dataset to create. Defaults to `""`.
##### permission
Specifies who can access the dataset to create. Available options:
-`"me"`: (Default) Only you can manage the dataset.
-`"team"`: All team members can manage the dataset.
##### chunk_method, `str`
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
Ensure your LLM is properly configured on the **Settings** page before selecting this. Please also note that Knowledge Graph consumes a large number of Tokens!
-`"email"`: Email
##### parser_config
The parser configuration of the dataset. A `ParserConfig` object's attributes vary based on the selected `chunk_method`:
-`"chunk_method"`: `str` 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
Ensure your LLM is properly configured on the **Settings** page before selecting this. Please also note that Knowledge Graph consumes a large number of Tokens!
-`"email"`: Email
-`"parser_config"`: `dict[str, Any]` The parsing configuration for the document. Its attributes vary based on the selected `"chunk_method"`:
The user query or query keywords. Defaults to `""`.
##### dataset_ids: `list[str]`, *Required*
The IDs of the datasets to search. Defaults to `None`. If you do not set this argument, ensure that you set `document_ids`.
##### document_ids: `list[str]`
The IDs of the documents to search. Defaults to `None`. You must ensure all selected documents use the same embedding model. Otherwise, an error will occur. If you do not set this argument, ensure that you set `dataset_ids`.
##### page: `int`
The starting index for the documents to retrieve. Defaults to `1`.
##### page_size: `int`
The maximum number of chunks to retrieve. Defaults to `30`.
##### Similarity_threshold: `float`
The minimum similarity score. Defaults to `0.2`.
##### vector_similarity_weight: `float`
The weight of vector cosine similarity. Defaults to `0.3`. If x represents the vector cosine similarity, then (1 - x) is the term similarity weight.
##### top_k: `int`
The number of chunks engaged in vector cosine computation. Defaults to `1024`.
##### rerank_id: `str`
The ID of the rerank model. Defaults to `None`.
##### keyword: `bool`
Indicates whether to enable keyword-based matching:
doc.add_chunk(content="This is a chunk addition test")
for c in rag_object.retrieve(dataset_ids=[dataset.id],document_ids=[doc.id]):
print(c)
```
---
## CHAT ASSISTANT MANAGEMENT
---
### Create chat assistant
```python
RAGFlow.create_chat(
name: str,
avatar: str = "",
dataset_ids: list[str] = [],
llm: Chat.LLM = None,
prompt: Chat.Prompt = None
) -> Chat
```
Creates a chat assistant.
#### Parameters
##### name: `str`, *Required*
The name of the chat assistant.
##### avatar: `str`
Base64 encoding of the avatar. Defaults to `""`.
##### dataset_ids: `list[str]`
The IDs of the associated datasets. Defaults to `[""]`.
##### llm: `Chat.LLM`
The LLM settings for the chat assistant to create. Defaults to `None`. When the value is `None`, a dictionary with the following values will be generated as the default. An `LLM` object contains the following attributes:
-`model_name`: `str`
The chat model name. If it is `None`, the user's default chat model will be used.
-`temperature`: `float`
Controls the randomness of the model's predictions. A lower temperature results in more conservative responses, while a higher temperature yields more creative and diverse responses. Defaults to `0.1`.
-`top_p`: `float`
Also known as “nucleus sampling”, this parameter sets a threshold to select a smaller set of words to sample from. It focuses on the most likely words, cutting off the less probable ones. Defaults to `0.3`
-`presence_penalty`: `float`
This discourages the model from repeating the same information by penalizing words that have already appeared in the conversation. Defaults to `0.2`.
-`frequency penalty`: `float`
Similar to the presence penalty, this reduces the model’s tendency to repeat the same words frequently. Defaults to `0.7`.
-`max_token`: `int`
The maximum length of the model's output, measured in the number of tokens (words or pieces of words). Defaults to `512`. If disabled, you lift the maximum token limit, allowing the model to determine the number of tokens in its responses.
##### prompt: `Chat.Prompt`
Instructions for the LLM to follow. A `Prompt` object contains the following attributes:
-`similarity_threshold`: `float` RAGFlow employs either a combination of weighted keyword similarity and weighted vector cosine similarity, or a combination of weighted keyword similarity and weighted reranking score during retrieval. If a similarity score falls below this threshold, the corresponding chunk will be excluded from the results. The default value is `0.2`.
-`keywords_similarity_weight`: `float` This argument sets the weight of keyword similarity in the hybrid similarity score with vector cosine similarity or reranking model similarity. By adjusting this weight, you can control the influence of keyword similarity in relation to other similarity measures. The default value is `0.7`.
-`top_n`: `int` This argument specifies the number of top chunks with similarity scores above the `similarity_threshold` that are fed to the LLM. The LLM will *only* access these 'top N' chunks. The default value is `8`.
-`variables`: `list[dict[]]` This argument lists the variables to use in the 'System' field of **Chat Configurations**. Note that:
-`knowledge` is a reserved variable, which represents the retrieved chunks.
- All the variables in 'System' should be curly bracketed.
- The default value is `[{"key": "knowledge", "optional": True}]`.
-`rerank_model`: `str` If it is not specified, vector cosine similarity will be used; otherwise, reranking score will be used. Defaults to `""`.
-`top_k`: `int` Refers to the process of reordering or selecting the top-k items from a list or set based on a specific ranking criterion. Default to 1024.
-`empty_response`: `str` If nothing is retrieved in the dataset for the user's question, this will be used as the response. To allow the LLM to improvise when nothing is found, leave this blank. Defaults to `None`.
-`opener`: `str` The opening greeting for the user. Defaults to `"Hi! I am your assistant, can I help you?"`.
-`show_quote`: `bool` Indicates whether the source of text should be displayed. Defaults to `True`.
-`prompt`: `str` The prompt content.
#### Returns
- Success: A `Chat` object representing the chat assistant.
A dictionary representing the attributes to update, with the following keys:
-`"name"`: `str` The revised name of the chat assistant.
-`"avatar"`: `str` Base64 encoding of the avatar. Defaults to `""`
-`"dataset_ids"`: `list[str]` The datasets to update.
-`"llm"`: `dict` The LLM settings:
-`"model_name"`, `str` The chat model name.
-`"temperature"`, `float` Controls the randomness of the model's predictions. A lower temperature results in more conservative responses, while a higher temperature yields more creative and diverse responses.
-`"top_p"`, `float` Also known as “nucleus sampling”, this parameter sets a threshold to select a smaller set of words to sample from.
-`"presence_penalty"`, `float` This discourages the model from repeating the same information by penalizing words that have appeared in the conversation.
-`"frequency penalty"`, `float` Similar to presence penalty, this reduces the model’s tendency to repeat the same words.
-`"max_token"`, `int` The maximum length of the model's output, measured in the number of tokens (words or pieces of words). Defaults to `512`. If disabled, you lift the maximum token limit, allowing the model to determine the number of tokens in its responses.
-`"prompt"` : Instructions for the LLM to follow.
-`"similarity_threshold"`: `float` RAGFlow employs either a combination of weighted keyword similarity and weighted vector cosine similarity, or a combination of weighted keyword similarity and weighted rerank score during retrieval. This argument sets the threshold for similarities between the user query and chunks. If a similarity score falls below this threshold, the corresponding chunk will be excluded from the results. The default value is `0.2`.
-`"keywords_similarity_weight"`: `float` This argument sets the weight of keyword similarity in the hybrid similarity score with vector cosine similarity or reranking model similarity. By adjusting this weight, you can control the influence of keyword similarity in relation to other similarity measures. The default value is `0.7`.
-`"top_n"`: `int` This argument specifies the number of top chunks with similarity scores above the `similarity_threshold` that are fed to the LLM. The LLM will *only* access these 'top N' chunks. The default value is `8`.
-`"variables"`: `list[dict[]]` This argument lists the variables to use in the 'System' field of **Chat Configurations**. Note that:
-`knowledge` is a reserved variable, which represents the retrieved chunks.
- All the variables in 'System' should be curly bracketed.
- The default value is `[{"key": "knowledge", "optional": True}]`.
-`"rerank_model"`: `str` If it is not specified, vector cosine similarity will be used; otherwise, reranking score will be used. Defaults to `""`.
-`"empty_response"`: `str` If nothing is retrieved in the dataset for the user's question, this will be used as the response. To allow the LLM to improvise when nothing is retrieved, leave this blank. Defaults to `None`.
-`"opener"`: `str` The opening greeting for the user. Defaults to `"Hi! I am your assistant, can I help you?"`.
-`"show_quote`: `bool` Indicates whether the source of text should be displayed Defaults to `True`.
Chat.create_session(name: str = "New session") -> Session
```
Creates a session with the current chat assistant.
#### Parameters
##### name: `str`
The name of the chat session to create.
#### Returns
- Success: A `Session` object containing the following attributes:
-`id`: `str` The auto-generated unique identifier of the created session.
-`name`: `str` The name of the created session.
-`message`: `list[Message]` The opening message of the created session. Default: `[{"role": "assistant", "content": "Hi! I am your assistant，can I help you?"}]`
-`chat_id`: `str` The ID of the associated chat assistant.
Deletes sessions of the current chat assistant by ID.
#### Parameters
##### ids: `list[str]`
The IDs of the sessions to delete. Defaults to `None`. If it is not specified, all sessions associated with the current chat assistant will be deleted.
Asks a specified chat assistant a question to start an AI-powered conversation.
:::tip NOTE
In streaming mode, not all responses include a reference, as this depends on the system's judgement.
:::
#### Parameters
##### question: `str`, *Required*
The question to start an AI-powered conversation. Default to `""`
##### stream: `bool`
Indicates whether to output responses in a streaming way:
-`True`: Enable streaming (default).
-`False`: Disable streaming.
##### **kwargs
The parameters in prompt(system).
#### Returns
- A `Message` object containing the response to the question if `stream` is set to `False`.
- An iterator containing multiple `message` objects (`iter[Message]`) if `stream` is set to `True`
The following shows the attributes of a `Message` object:
##### id: `str`
The auto-generated message ID.
##### content: `str`
The content of the message. Defaults to `"Hi! I am your assistant, can I help you?"`.
##### reference: `list[Chunk]`
A list of `Chunk` objects representing references to the message, each containing the following attributes:
-`id``str`
The chunk ID.
-`content``str`
The content of the chunk.
-`img_id``str`
The ID of the snapshot of the chunk. Applicable only when the source of the chunk is an image, PPT, PPTX, or PDF file.
-`document_id``str`
The ID of the referenced document.
-`document_name``str`
The name of the referenced document.
-`position``list[str]`
The location information of the chunk within the referenced document.
-`dataset_id``str`
The ID of the dataset to which the referenced document belongs.
-`similarity``float`
A composite similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity. It is the weighted sum of `vector_similarity` and `term_similarity`.
-`vector_similarity``float`
A vector similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity between vector embeddings.
-`term_similarity``float`
A keyword similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity between keywords.
- Success: A `Session` object containing the following attributes:
-`id`: `str` The auto-generated unique identifier of the created session.
-`message`: `list[Message]` The messages of the created session assistant. Default: `[{"role": "assistant", "content": "Hi! I am your assistant，can I help you?"}]`
-`agent_id`: `str` The ID of the associated agent.
Asks a specified agent a question to start an AI-powered conversation.
:::tip NOTE
In streaming mode, not all responses include a reference, as this depends on the system's judgement.
:::
#### Parameters
##### question: `str`
The question to start an AI-powered conversation. Ifthe **Begin** component takes parameters, a question is not required.
##### stream: `bool`
Indicates whether to output responses in a streaming way:
-`True`: Enable streaming (default).
-`False`: Disable streaming.
#### Returns
- A `Message` object containing the response to the question if `stream` is set to `False`
- An iterator containing multiple `message` objects (`iter[Message]`) if `stream` is set to `True`
The following shows the attributes of a `Message` object:
##### id: `str`
The auto-generated message ID.
##### content: `str`
The content of the message. Defaults to `"Hi! I am your assistant, can I help you?"`.
##### reference: `list[Chunk]`
A list of `Chunk` objects representing references to the message, each containing the following attributes:
-`id``str`
The chunk ID.
-`content``str`
The content of the chunk.
-`image_id``str`
The ID of the snapshot of the chunk. Applicable only when the source of the chunk is an image, PPT, PPTX, or PDF file.
-`document_id``str`
The ID of the referenced document.
-`document_name``str`
The name of the referenced document.
-`position``list[str]`
The location information of the chunk within the referenced document.
-`dataset_id``str`
The ID of the dataset to which the referenced document belongs.
-`similarity``float`
A composite similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity. It is the weighted sum of `vector_similarity` and `term_similarity`.
-`vector_similarity``float`
A vector similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity between vector embeddings.
-`term_similarity``float`
A keyword similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity between keywords.
