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Updated HTTP API reference and Python API reference based on test results (#3090)

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### What problem does this PR solve?



### Type of change


- [x] Documentation Update
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api/http_api_reference.md
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@ -94,8 +94,10 @@ curl --request POST \
The configuration settings for the dataset parser, a JSON object containing the following attributes:
- `"chunk_token_count"`: Defaults to `128`.
- `"layout_recognize"`: Defaults to `true`.
- `"html4excel"`: Indicates whether to convert Excel documents into HTML format. Defaults to `false`.
- `"delimiter"`: Defaults to `"\n!?。；！？"`.
- `"task_page_size"`: Defaults to `12`.
- `"task_page_size"`: Defaults to `12`. For PDF only.
- `"raptor"`: Raptor-specific settings. Defaults to: `{"use_raptor": false}`.
### Response
@ -515,7 +517,7 @@ curl --request PUT \
- `"chunk_token_count"`: Defaults to `128`.
- `"layout_recognize"`: Defaults to `true`.
- `"delimiter"`: Defaults to `"\n!?。；！？"`.
- `"task_page_size"`: Defaults to `12`.
- `"task_page_size"`: Defaults to `12`. For PDF only.
### Response
@ -1169,7 +1171,7 @@ Failure:
## Retrieve chunks
**GET** `/api/v1/retrieval`
**POST** `/api/v1/retrieval`
Retrieves chunks from specified datasets.

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api/python_api_reference.md
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@ -1253,7 +1253,7 @@ Asks a question to start an AI-powered conversation.
#### question: `str` *Required*
The question to start an AI chat.
The question to start an AI-powered conversation.
#### stream: `bool`
@ -1286,7 +1286,7 @@ A list of `Chunk` objects representing references to the message, each containin
- `content` `str`
The content of the chunk.
- `image_id` `str`
The ID of the snapshot of the chunk.
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`
@ -1296,13 +1296,12 @@ A list of `Chunk` objects representing references to the message, each containin
- `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.
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.
### Examples
```python