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docs: update docs with go (#20062)
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@@ -1136,9 +1136,7 @@ RAGFlow.retrieve(
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rerank_id: str | None = None,
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keyword: bool = False,
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cross_languages: list[str] | None = None,
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metadata_condition: dict | None = None,
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use_kg: bool = False,
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toc_enhance: bool = False)
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metadata_condition: dict | None = None)
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```
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Retrieves chunks from specified datasets.
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@@ -1196,14 +1194,6 @@ The languages that should be translated into, in order to achieve keywords retri
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filter condition for `meta_fields`.
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##### use_kg: `bool`
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Whether to enable graph-assisted retrieval for multi-hop queries. Defaults to `False`.
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##### toc_enhance: `bool`
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Whether to use extracted table-of-contents information during retrieval. Defaults to `False`.
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#### Returns
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- Success: A list of `Chunk` objects representing the document chunks.
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@@ -1656,102 +1646,6 @@ assistant.delete_sessions(delete_all=True)
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---
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### Converse with chat assistant
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```python
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Session.ask(question: str = "", stream: bool = False, **kwargs) -> Optional[Message, iter[Message]]
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```
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Asks a specified chat assistant a question to start an AI-powered conversation.
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:::tip NOTE
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In streaming mode, not all responses include a reference, as this depends on the system's judgment.
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:::
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#### Parameters
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##### question: `string`, *Required*
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The question to start an AI-powered conversation. Default to `""`
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##### stream: `bool`
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Indicates whether to output responses in a streaming way:
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- `True`: Enable streaming (default).
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- `False`: Disable streaming.
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##### **kwargs
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The parameters in prompt(system).
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#### Returns
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- A `Message` object containing the response to the question if `stream` is set to `False`.
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- An iterator containing multiple `message` objects (`iter[Message]`) if `stream` is set to `True`
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The following shows the attributes of a `Message` object:
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##### id: `string`
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The auto-generated message ID.
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##### content: `string`
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The content of the message. Defaults to `"Hi! I am your assistant, can I help you?"`.
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##### reference: `list[Chunk]`
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A list of `Chunk` objects representing references to the message, each containing the following attributes:
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- `id` `string`
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The chunk ID.
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- `content` `string`
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The content of the chunk.
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- `img_id` `string`
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The ID of the snapshot of the chunk. Applicable only when the source of the chunk is an image, PPT, PPTX, or PDF file.
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- `document_id` `string`
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The ID of the referenced document.
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- `document_name` `string`
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The name of the referenced document.
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- `document_metadata` `dict`
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Optional document metadata, returned only when `extra_body.reference_metadata.include` is `true`.
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- `position` `list[str]`
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The location information of the chunk within the referenced document.
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- `dataset_id` `string`
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The ID of the dataset to which the referenced document belongs.
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- `similarity` `float`
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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`.
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- `vector_similarity` `float`
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A vector similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity between vector embeddings.
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- `term_similarity` `float`
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A keyword similarity score of the chunk ranging from `0` to `1`, with a higher value indicating greater similarity between keywords.
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#### Examples
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```python
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from ragflow_sdk import RAGFlow
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rag_object = RAGFlow(api_key="<YOUR_API_KEY>", base_url="http://<YOUR_BASE_URL>:9380")
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assistant = rag_object.list_chats(name="Miss R")
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assistant = assistant[0]
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session = assistant.create_session()
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print("\n==================== Miss R =====================\n")
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print("Hello. What can I do for you?")
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while True:
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question = input("\n==================== User =====================\n> ")
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print("\n==================== Miss R =====================\n")
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cont = ""
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for ans in session.ask(question, stream=True):
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print(ans.content[len(cont):], end='', flush=True)
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cont = ans.content
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```
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---
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### Create session with agent
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```python
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@@ -2359,9 +2253,9 @@ Configurations to update. Available configurations:
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- `memory_size`: `int`, *Optional*
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Defaults to `5*1024*1024` Bytes. Accounts for each message's content + its embedding vector (≈ Content + Dimensions × 8 Bytes). Example: A 1 KB message with 1024-dim embedding uses ~9 KB. The 5 MB default limit holds ~500 such messages.
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Defaults to `5 MiB` (`5242880` bytes). Accounts for each message's content + its embedding vector (≈ Content + Dimensions × 8 Bytes). Example: A 1 KiB message with a 1024-dimension embedding uses approximately 9 KiB. The 5 MiB default limit holds approximately 500 such messages.
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- Maximum 10 * 1024 * 1024 Bytes
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- Maximum `5 MiB` (`5242880` bytes)
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- `forgetting_policy`: `enum<string>`, *Optional*
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