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@@ -189,7 +189,8 @@ from paddlex import create_pipeline
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pipeline = create_pipeline(
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pipeline="PP-ChatOCRv3-doc",
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llm_name="ernie-3.5",
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- llm_params={"api_type": "qianfan", "ak": "", "sk": ""} # Please fill in ak and sk, required for LLM.
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+ llm_params={"api_type": "qianfan", "ak": "", "sk": ""} # Please enter your ak and sk; otherwise, the large model cannot be invoked.
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+ # llm_params={"api_type": "aistudio", "access_token": ""} # Please enter your access_token; otherwise, the large model cannot be invoked.
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)
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visual_result, visual_info = pipeline.visual_predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/contract.pdf")
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@@ -199,10 +200,16 @@ for res in visual_result:
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res.save_to_html('./output')
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res.save_to_xlsx('./output')
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-chat_result = pipeline.chat(["乙方", "手机号"])
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+vector = pipeline.build_vector(visual_info=visual_info)
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+
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+chat_result = pipeline.chat(
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+ key_list=["乙方", "手机号"],
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+ visual_info=visual_info,
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+ vector=vector,
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+ )
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chat_result.print()
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```
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-**Note**: Please first obtain your ak and sk on the [Baidu Cloud Qianfan Platform](https://console.bce.baidu.com/qianfan/ais/console/onlineService) (for detailed steps, please refer to the [AK and SK Authentication API Call Process](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Hlwerugt8)), and fill in your ak and sk to the specified locations to enable normal calls to the large model.
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+**Note**: Currently, the large language model only supports Ernie. You can obtain the relevant ak/sk (access_token) on the [Baidu Cloud Qianfan Platform](https://console.bce.baidu.com/qianfan/ais/console/onlineService) or [Baidu AIStudio Community](https://aistudio.baidu.com/). If you use the Baidu Cloud Qianfan Platform, you can refer to the [AK and SK Authentication API Calling Process](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Hlwerugt8) to obtain ak/sk. If you use Baidu AIStudio Community, you can obtain the access_token from the [Baidu AIStudio Community Access Token](https://aistudio.baidu.com/account/accessToken).
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After running, the output is as follows:
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@@ -217,7 +224,7 @@ In the above Python script, the following steps are executed:
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| Parameter | Type | Default | Description |
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|-|-|-|-|
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| `pipeline` | str | None | Pipeline name or pipeline configuration file path. If it's a pipeline name, it must be supported by PaddleX; |
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-| `llm_name` | str | "ernie-3.5" | Large Language Model name; |
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+| `llm_name` | str | "ernie-3.5" | Large Language Model name, we support `ernie-4.0` and `ernie-3.5`, with more models on the way.|
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| `llm_params` | dict | `{}` | API configuration; |
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| `device(kwargs)` | str/`None` | `None` | Running device (`None` meaning automatic selection); |
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@@ -250,6 +257,18 @@ In the above Python script, the following steps are executed:
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|`key_list`|str|-|Keywords used to query. A string composed of multiple keywords with "," as separators, such as "Party B, phone number";|
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|`key_list`|list|-|Keywords used to query. A list composed of multiple keywords.|
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+(3) Obtain prediction results by calling the `predict` method: The `predict` method is a `generator`, so prediction results need to be obtained through calls. The `predict` method predicts data in batches, so the prediction results are represented as a list of prediction results.
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+
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+(4) Interact with the large model by calling the `predict.chat` method, which takes as input keywords (multiple keywords are supported) for information extraction. The prediction results are represented as a list of information extraction results.
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+
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+(5) Process the prediction results: The prediction result for each sample is in the form of a dict, which supports printing or saving to a file. The supported file types depend on the specific pipeline, such as:
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+
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+| Method | Description | Method Parameters |
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+|-|-|-|
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+| save_to_img | Saves layout analysis, table recognition, etc. results as image files. | `save_path`: str, the file path to save. |
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+| save_to_html | Saves table recognition results as HTML files. | `save_path`: str, the file path to save. |
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+| save_to_xlsx | Saves table recognition results as Excel files. | `save_path`: str, the file path to save. |
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+
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When executing the above command, the default Pipeline configuration file is loaded. If you need to customize the configuration file, you can use the following command to obtain it:
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```bash
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@@ -287,17 +306,28 @@ For example, if your configuration file is saved at `./my_path/PP-ChatOCRv3-doc.
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```python
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from paddlex import create_pipeline
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+
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pipeline = create_pipeline(
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pipeline="./my_path/PP-ChatOCRv3-doc.yaml",
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llm_name="ernie-3.5",
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- llm_params={"api_type": "qianfan", "ak": "", "sk": ""} # Please fill in ak and sk, required for LLM.
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+ llm_params={"api_type": "qianfan", "ak": "", "sk": ""} # Please enter your ak and sk; otherwise, the large model cannot be invoked.
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+ # llm_params={"api_type": "aistudio", "access_token": ""} # Please enter your access_token; otherwise, the large model cannot be invoked.
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)
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+
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visual_result, visual_info = pipeline.visual_predict("https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/contract.pdf")
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+
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for res in visual_result:
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res.save_to_img("./output")
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res.save_to_html('./output')
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res.save_to_xlsx('./output')
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-chat_result = pipeline.chat(["乙方", "手机号"])
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+
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+vector = pipeline.build_vector(visual_info=visual_info)
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+
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+chat_result = pipeline.chat(
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+ key_list=["乙方", "手机号"],
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+ visual_info=visual_info,
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+ vector=vector,
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+ )
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chat_result.print()
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```
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@@ -598,28 +628,18 @@ Pipeline:
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Subsequently, load the modified pipeline configuration file using the command-line interface or Python script as described in the local experience section.
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## 5. Multi-hardware Support
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+PaddleX supports various mainstream hardware devices such as NVIDIA GPUs, Kunlun XPU, Ascend NPU, and Cambricon MLU. **Seamless switching between different hardware can be achieved by simply setting the `--device` parameter**.
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-For example, to perform inference using the PP-ChatOCRv3-doc Pipeline on an NVIDIA GPU, you run:
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-```python
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-from paddlex import create_pipeline
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-predict = create_pipeline( pipeline="PP-ChatOCRv3-doc",
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- llm_name="ernie-3.5",
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- llm_params = {"api_type":"qianfan","ak":"","sk":""}, ## Please fill in your ak and sk, or you will not be able to call the large model
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- device = "gpu:0" )
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-```
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-
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-At this point, if you wish to switch the hardware to Ascend NPU, simply modify the `--device` in the script to `npu:0`:
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-
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+For example, to perform inference using the PP-ChatOCRv3-doc Pipeline on an NVIDIA GPU.
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+At this point, if you wish to switch the hardware to Ascend NPU, simply modify the `--device` in the script to `npu`:
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```python
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from paddlex import create_pipeline
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-predict = create_pipeline(
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+pipeline = create_pipeline(
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pipeline="PP-ChatOCRv3-doc",
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llm_name="ernie-3.5",
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- llm_params={"api_type": "qianfan", "ak": "", "sk": ""}, # Please fill in ak and sk, required for LLM.
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+ llm_params={"api_type": "qianfan", "ak": "", "sk": ""},
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device="npu:0" # gpu:0 --> npu:0
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- )
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+ )
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```
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-
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-If you want to use the PP-ChatOCRv3-doc Pipeline on more types of hardware, please refer to the [PaddleX Multi-Device Usage Guide](../../../installation/multi_devices_use_guide_en.md).
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-
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+If you want to use the PP-ChatOCRv3-doc Pipeline on more types of hardware, please refer to the [PaddleX Multi-Device Usage Guide](../../../installation/installation_other_devices_en.md).
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cuicheng01