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DRAFT: Miscellaneous updates to HTTP API. Tried to finish off Python API ref… (#2909)

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### What problem does this PR solve?



### Type of change


- [x] Documentation Update
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@ -380,7 +380,7 @@ The ID of the document to retrieve. Defaults to `None`.
#### keywords: `str`
The keywords to match document titles. Defaults to `None`.
The keywords used to match document titles. Defaults to `None`.
#### offset: `int`
@ -419,7 +419,7 @@ A `Document` object contains the following attributes:
- `created_by`: `str` The creator of the document. Defaults to `""`.
- `size`: `int` The document size in bytes. Defaults to `0`.
- `token_count`: `int` The number of tokens in the document. Defaults to `0`.
- `chunk_count`: `int` The number of chunks that the document is split into. Defaults to `0`.
- `chunk_count`: `int` The number of chunks in the document. Defaults to `0`.
- `progress`: `float` The current processing progress as a percentage. Defaults to `0.0`.
- `progress_msg`: `str` A message indicating the current progress status. Defaults to `""`.
- `process_begin_at`: `datetime` The start time of document processing. Defaults to `None`.
@ -432,8 +432,8 @@ A `Document` object contains the following attributes:
```python
from ragflow import RAGFlow
rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag.create_dataset(name="kb_1")
rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.create_dataset(name="kb_1")
filename1 = "~/ragflow.txt"
blob = open(filename1 , "rb").read()
@ -482,6 +482,8 @@ dataset.delete_documents(ids=["id_1","id_2"])
DataSet.async_parse_documents(document_ids:list[str]) -> None
```
Parses documents in the current dataset.
### Parameters
#### document_ids: `list[str]`, *Required*
@ -490,7 +492,7 @@ The IDs of the documents to parse.
### Returns
- Success: No value is returned.
- Success: No value is returned.????????????????????
- Failure: `Exception`
### Examples
@ -520,6 +522,8 @@ print("Async bulk parsing initiated.")
DataSet.async_cancel_parse_documents(document_ids:list[str])-> None
```
Stops parsing specified documents.
### Parameters
#### document_ids: `list[str]`, *Required*
@ -554,27 +558,80 @@ print("Async bulk parsing cancelled.")
---
## Add chunk
```python
Document.add_chunk(content:str) -> Chunk ?????????????????????
```
Adds a chunk to the current document.
### Parameters
#### content: `str`, *Required*
The text content of the chunk.
#### important_keywords: `list[str]` ??????????????????????
The key terms or phrases to tag with the chunk.
### Returns
- Success: A `Chunk` object.
- Failure: `Exception`.
A `Chunk` object contains the following attributes:
- `id`: `str`
- `content`: `str` Content of the chunk.
- `important_keywords`: `list[str]` A list of key terms or phrases to tag with the chunk.
- `create_time`: `str` The time when the chunk was created (added to the document).
- `create_timestamp`: `float` The timestamp representing the creation time of the chunk, expressed in seconds since January 1, 1970.
- `knowledgebase_id`: `str` The ID of the associated dataset.
- `document_name`: `str` The name of the associated document.
- `document_id`: `str` The ID of the associated document.
- `available`: `int`???? The chunk's availability status in the dataset. Value options:
- `0`: Unavailable
- `1`: Available
### Examples
```python
from ragflow import RAGFlow
rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.list_datasets(id="123")
dtaset = dataset[0]
doc = dataset.list_documents(id="wdfxb5t547d")
doc = doc[0]
chunk = doc.add_chunk(content="xxxxxxx")
```
---
## List chunks
```python
Document.list_chunks(keywords: str = None, offset: int = 0, limit: int = -1, id : str = None) -> list[Chunk]
```
Retrieves a list of document chunks.
Retrieves a list of chunks from the current document.
### Parameters
#### keywords: `str`
List chunks whose name has the given keywords. Defaults to `None`
The keywords used to match chunk content. Defaults to `None`
#### offset: `int`
The starting index for the chunks to retrieve. Defaults to `1`
The starting index for the chunks to retrieve. Defaults to `1`??????
#### limit
The maximum number of chunks to retrieve. Default: `30`
The maximum number of chunks to retrieve. Default: `30`?????????
#### id
@ -598,42 +655,9 @@ for chunk in doc.list_chunks(keywords="rag", offset=0, limit=12):
print(chunk)
```
## Add chunk
```python
Document.add_chunk(content:str) -> Chunk
```
### Parameters
#### content: *Required*
The text content of the chunk.
#### important_keywords :`list[str]`
List the key terms or phrases that are significant or central to the chunk's content.
### Returns
chunk
### Examples
```python
from ragflow import RAGFlow
rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag.list_datasets(id="123")
dtaset = dataset[0]
doc = dataset.list_documents(id="wdfxb5t547d")
doc = doc[0]
chunk = doc.add_chunk(content="xxxxxxx")
```
---
## Delete chunk
## Delete chunks
```python
Document.delete_chunks(chunk_ids: list[str])
@ -657,10 +681,10 @@ The IDs of the chunks to delete. Defaults to `None`. If not specified, all chunk
```python
from ragflow import RAGFlow
rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
ds = rag.list_datasets(id="123")
ds = ds[0]
doc = ds.list_documents(id="wdfxb5t547d")
rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
dataset = rag_object.list_datasets(id="123")
dataset = dataset[0]
doc = dataset.list_documents(id="wdfxb5t547d")
doc = doc[0]
chunk = doc.add_chunk(content="xxxxxxx")
doc.delete_chunks(["id_1","id_2"])
@ -683,7 +707,7 @@ Updates content or configurations for the current chunk.
A dictionary representing the attributes to update, with the following keys:
- `"content"`: `str` Content of the chunk.
- `"important_keywords"`: `list[str]` A list of key terms to attach to the chunk.
- `"important_keywords"`: `list[str]` A list of key terms or phrases to tag with the chunk.
- `"available"`: `int` The chunk's availability status in the dataset. Value options:
- `0`: Unavailable
- `1`: Available
@ -715,13 +739,15 @@ chunk.update({"content":"sdfx..."})
RAGFlow.retrieve(question:str="", datasets:list[str]=None, document=list[str]=None, offset:int=1, limit:int=30, similarity_threshold:float=0.2, vector_similarity_weight:float=0.3, top_k:int=1024,rerank_id:str=None,keyword:bool=False,higlight:bool=False) -> list[Chunk]
```
???????
### Parameters
#### question: `str` *Required*
The user query or query keywords. Defaults to `""`.
#### datasets: `list[str]`, *Required*
#### datasets: `list[str]`, *Required*?????
The datasets to search from.
@ -735,7 +761,7 @@ The starting index for the documents to retrieve. Defaults to `0`??????.
#### limit: `int`
The maximum number of chunks to retrieve. Defaults to `6`.
The maximum number of chunks to retrieve. Defaults to `6`.???????????????
#### Similarity_threshold: `float`
@ -749,18 +775,18 @@ The weight of vector cosine similarity. Defaults to `0.3`. If x represents the v
The number of chunks engaged in vector cosine computaton. Defaults to `1024`.
#### rerank_id
#### rerank_id: `str`
The ID of the rerank model. Defaults to `None`.
The ID of the rerank model. Defaults to `None`.
#### keyword
#### keyword: `bool`
Indicates whether keyword-based matching is enabled:
- `True`: Enabled.
- `False`: Disabled.
- `False`: Disabled (default).
#### highlight:`bool`
#### highlight: `bool`
Specifying whether to enable highlighting of matched terms in the results (True) or not (False).
@ -775,16 +801,16 @@ Specifying whether to enable highlighting of matched terms in the results (True)
from ragflow import RAGFlow
rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
ds = rag_object.list_datasets(name="ragflow")
ds = ds[0]
dataset = rag_object.list_datasets(name="ragflow")
dataset = dataset[0]
name = 'ragflow_test.txt'
path = './test_data/ragflow_test.txt'
rag_object.create_document(ds, name=name, blob=open(path, "rb").read())
doc = ds.list_documents(name=name)
rag_object.create_document(dataset, name=name, blob=open(path, "rb").read())
doc = dataset.list_documents(name=name)
doc = doc[0]
ds.async_parse_documents([doc.id])
dataset.async_parse_documents([doc.id])
for c in rag_object.retrieve(question="What's ragflow?",
datasets=[ds.id], documents=[doc.id],
datasets=[dataset.id], documents=[doc.id],
offset=1, limit=30, similarity_threshold=0.2,
vector_similarity_weight=0.3,
top_k=1024
@ -818,11 +844,11 @@ Creates a chat assistant.
The following shows the attributes of a `Chat` object:
#### name: *Required*
#### name: `str`, *Required*????????
The name of the chat assistant. Defaults to `"assistant"`.
#### avatar
#### avatar: `str`
Base64 encoding of the avatar. Defaults to `""`.
@ -830,7 +856,7 @@ Base64 encoding of the avatar. Defaults to `""`.
The IDs of the associated datasets. Defaults to `[""]`.
#### llm
#### llm: `Chat.LLM`
The llm of the created chat. Defaults to `None`. When the value is `None`, a dictionary with the following values will be generated as the default.
@ -849,7 +875,7 @@ An `LLM` object contains the following attributes:
- `max_token`, `int`
This sets the maximum length of the model’s output, measured in the number of tokens (words or pieces of words). Defaults to `512`.
#### Prompt
#### prompt: `Chat.Prompt`
Instructions for the LLM to follow. A `Prompt` object contains the following attributes:
@ -876,17 +902,17 @@ Instructions for the LLM to follow. A `Prompt` object contains the following at
```python
from ragflow import RAGFlow
rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
kbs = rag.list_datasets(name="kb_1")
list_kb=[]
for kb in kbs:
list_kb.append(kb.id)
assi = rag.create_chat("Miss R", knowledgebases=list_kb)
rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
datasets = rag_object.list_datasets(name="kb_1")
dataset_ids = []
for dataset in datasets:
dataset_ids.append(dataset.id)
assistant = rag_object.create_chat("Miss R", knowledgebases=dataset_ids)
```
---
## Update chat
## Update chat assistant
```python
Chat.update(update_message: dict)
@ -934,15 +960,15 @@ A dictionary representing the attributes to update, with the following keys:
```python
from ragflow import RAGFlow
rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
knowledge_base = rag.list_datasets(name="kb_1")
assistant = rag.create_chat("Miss R", knowledgebases=knowledge_base)
rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
datasets = rag_object.list_datasets(name="kb_1")
assistant = rag_object.create_chat("Miss R", knowledgebases=datasets)
assistant.update({"name": "Stefan", "llm": {"temperature": 0.8}, "prompt": {"top_n": 8}})
```
---
## Delete chats
## Delete chat assistants
```python
RAGFlow.delete_chats(ids: list[str] = None)
@ -966,13 +992,13 @@ The IDs of the chat assistants to delete. Defaults to `None`. If not specified,
```python
from ragflow import RAGFlow
rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
rag.delete_chats(ids=["id_1","id_2"])
rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
rag_object.delete_chats(ids=["id_1","id_2"])
```
---
## List chats
## List chat assistants
```python
RAGFlow.list_chats(
@ -989,29 +1015,32 @@ Retrieves a list of chat assistants.
### Parameters
#### page
#### page: `int`
Specifies the page on which the chat assistants will be displayed. Defaults to `1`.
#### page_size
#### page_size: `int`
The number of chat assistants on each page. Defaults to `1024`.
#### order_by
#### orderby: `str`
The attribute by which the results are sorted. Defaults to `"create_time"`.
The attribute by which the results are sorted. Available options:
#### desc
- `"create_time"` (default)
- `"update_time"`
#### desc: `bool`
Indicates whether the retrieved chat assistants should be sorted in descending order. Defaults to `True`.
#### id: `string`
#### id: `str`
The ID of the chat to retrieve. Defaults to `None`.
The ID of the chat assistant to retrieve. Defaults to `None`.
#### name: `string`
#### name: `str`
The name of the chat to retrieve. Defaults to `None`.
The name of the chat assistant to retrieve. Defaults to `None`.
### Returns
@ -1031,7 +1060,7 @@ for assistant in rag_object.list_chats():
---
:::tip API GROUPING
Chat-session APIs
Chat Session APIs
:::
---
@ -1046,7 +1075,7 @@ Creates a chat session.
### Parameters
#### name
#### name: `str`
The name of the chat session to create.
@ -1064,12 +1093,14 @@ The name of the chat session to create.
```python
from ragflow import RAGFlow
rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
assistant = rag.list_chats(name="Miss R")
rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
assistant = rag_object.list_chats(name="Miss R")
assistant = assistant[0]
session = assistant.create_session()
```
---
## Update session
```python
@ -1096,8 +1127,8 @@ A dictionary representing the attributes to update, with only one key:
```python
from ragflow import RAGFlow
rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
assistant = rag.list_chats(name="Miss R")
rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
assistant = rag_object.list_chats(name="Miss R")
assistant = assistant[0]
session = assistant.create_session("session_name")
session.update({"name": "updated_name"})
@ -1105,6 +1136,101 @@ session.update({"name": "updated_name"})
---
## List sessions
```python
Chat.list_sessions(
page: int = 1,
page_size: int = 1024,
orderby: str = "create_time",
desc: bool = True,
id: str = None,
name: str = None
) -> list[Session]
```
Lists sessions associated with the current chat assistant.
### Parameters
#### page: `int`
Specifies the page on which the sessions will be displayed. Defaults to `1`.
#### page_size: `int`
The number of sessions on each page. Defaults to `1024`.
#### orderby: `str`
The field by which sessions should be sorted. Available options:
- `"create_time"` (default)
- `"update_time"`
#### desc: `bool`
Indicates whether the retrieved sessions should be sorted in descending order. Defaults to `True`.
#### id: `str`
The ID of the chat session to retrieve. Defaults to `None`.
#### name: `str`
The name of the chat session to retrieve. Defaults to `None`.
### Returns
- Success: A list of `Session` objects associated with the current chat assistant.
- Failure: `Exception`.
### Examples
```python
from ragflow import RAGFlow
rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
assistant = rag_object.list_chats(name="Miss R")
assistant = assistant[0]
for session in assistant.list_sessions():
print(session)
```
---
## Delete sessions
```python
Chat.delete_sessions(ids:list[str] = None)
```
Deletes sessions by ID.
### Parameters
#### ids: `list[str]`
The IDs of the sessions to delete. Defaults to `None`. If not specified, all sessions associated with the current chat assistant will be deleted.
### Returns
- Success: No value is returned.
- Failure: `Exception`
### Examples
```python
from ragflow import RAGFlow
rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
assistant = rag_object.list_chats(name="Miss R")
assistant = assistant[0]
assistant.delete_sessions(ids=["id_1","id_2"])
```
---
## Chat
```python
@ -1115,11 +1241,11 @@ Asks a question to start a conversation.
### Parameters
#### question *Required*
#### question: `str` *Required*
The question to start an AI chat. Defaults to `None`.
The question to start an AI chat.
#### stream
#### stream: `str`
Indicates whether to output responses in a streaming way:
@ -1172,8 +1298,8 @@ A list of `Chunk` objects representing references to the message, each containin
```python
from ragflow import RAGFlow
rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
assistant = rag.list_chats(name="Miss R")
rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
assistant = rag_object.list_chats(name="Miss R")
assistant = assistant[0]
session = assistant.create_session()
@ -1189,98 +1315,3 @@ while True:
print(answer.content[len(cont):], end='', flush=True)
cont = answer.content
```
---
## List sessions
```python
Chat.list_sessions(
page: int = 1,
page_size: int = 1024,
orderby: str = "create_time",
desc: bool = True,
id: str = None,
name: str = None
) -> list[Session]
```
Lists sessions associated with the current chat assistant.
### Parameters
#### page
Specifies the page on which the sessions will be displayed. Defaults to `1`.
#### page_size
The number of sessions on each page. Defaults to `1024`.
#### orderby
The field by which sessions should be sorted. Available options:
- `"create_time"` (default)
- `"update_time"`
#### desc
Indicates whether the retrieved sessions should be sorted in descending order. Defaults to `True`.
#### id
The ID of the chat session to retrieve. Defaults to `None`.
#### name
The name of the chat to retrieve. Defaults to `None`.
### Returns
- Success: A list of `Session` objects associated with the current chat assistant.
- Failure: `Exception`.
### Examples
```python
from ragflow import RAGFlow
rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
assistant = rag_object.list_chats(name="Miss R")
assistant = assistant[0]
for session in assistant.list_sessions():
print(session)
```
---
## Delete sessions
```python
Chat.delete_sessions(ids:list[str] = None)
```
Deletes sessions by ID.
### Parameters
#### ids: `list[str]`
The IDs of the sessions to delete. Defaults to `None`. If not specified, all sessions associated with the current chat assistant will be deleted.
### Returns
- Success: No value is returned.
- Failure: `Exception`
### Examples
```python
from ragflow import RAGFlow
rag = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
assistant = rag.list_chats(name="Miss R")
assistant = assistant[0]
assistant.delete_sessions(ids=["id_1","id_2"])
```