dustyposa/rasa_ch_faq

用 rasa 实现 rasa demo 机器人，有一些惊奇的功能，faq，图谱，多轮等

Hi there 👋

rasa_ch_faq's Issues

有计划升级到3.0吗？

除了几个yml文件，其他要改动的地方是不是很多呢？

在前端中无法正常问答，该排查哪一部分呢？

运行截图如下

后端已经开启：

actions也是开启的：

而且使用rasa shell的话是可以正常问答的，现在就是前端无法回答，想问一下您之前跑的时候遇到过类似的问题吗？

关于前端文件的使用

我使用了您开源出来的前端项目，另外我没有使用 npm 部署前端服务, 而是直接使用了您给出的命令行: --cors "*" 加载了这个前端文件。但是我有一个问题：

1. rasa 的 core 服务是如何和这个前端服务打通的，也就是说，打开前端后，我每次从前端输入的一句话是如何进入到 rasa 内部中，并且 rasa 可以维持住这个对话的状态的。
2. 这个问题基础上面的第一点，因为我现在有一个训练好的 rasa 服务，但是我需要把这个服务添加（或者说引入）到一个目前现有的聊天对话页面中，我应该如何做呢？

还请您不吝赐教。期待得到您的回复，我的 VX：18336304089
如果您愿意提供帮助，我愿意有偿获得您的指导与帮助。

RASA提问，增加RASA FAQ的语料库

想到一个加一个吧。

• 1. rasa run和rasa run actions的区别？
• 2. Rasa和RasaX的区别？
• 3. Rasa在何处调用Bert?
• 4. 回译文本工具的作用是什么？
• 5. API的形式调用Rasa的地址是多少以及需要传递什么参数？(答案)

rasa3.0如何实现图片的上传

你好，我想问一下，rasa3.0如何实现图片的上传。

多轮对话确定一个推荐，怎么实现？

多轮对话确定一个推荐，怎么实现？
人：你好
AI：你好
人：我要找大学
AI：你什么学历
人：高中
AI：你什么年龄
人：18岁
AI：你想读什么专业
人：计算机
AI：你想再什么地区
人：北京
AI：推荐你北京大学

如果要实现多轮条件合并才能推出结果，怎么实现呢?

运行rasa train时报错显示OSError

运行rasa train时报错，报错信息如下：
Traceback (most recent call last):
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/transformers/modeling_tf_utils.py", line 1232, in from_pretrained
missing_keys, unexpected_keys = load_tf_weights(model, resolved_archive_file)
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/transformers/modeling_tf_utils.py", line 459, in load_tf_weights
with h5py.File(resolved_archive_file, "r") as f:
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/h5py/_hl/files.py", line 406, in init
fid = make_fid(name, mode, userblock_size,
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/h5py/_hl/files.py", line 173, in make_fid
fid = h5f.open(name, flags, fapl=fapl)
File "h5py/_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
File "h5py/_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
File "h5py/h5f.pyx", line 88, in h5py.h5f.open
OSError: Unable to open file (file signature not found)

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "/Users/anaconda3/envs/rasa/bin/rasa", line 8, in
sys.exit(main())
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/main.py", line 117, in main
cmdline_arguments.func(cmdline_arguments)
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/cli/train.py", line 59, in
train_parser.set_defaults(func=lambda args: run_training(args, can_exit=True))
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/cli/train.py", line 91, in run_training
training_result = train_all(
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/api.py", line 109, in train
return rasa.utils.common.run_in_loop(
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/utils/common.py", line 296, in run_in_loop
result = loop.run_until_complete(f)
File "uvloop/loop.pyx", line 1456, in uvloop.loop.Loop.run_until_complete
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/model_training.py", line 108, in train_async
return await _train_async_internal(
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/model_training.py", line 289, in _train_async_internal
await _do_training(
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/model_training.py", line 335, in _do_training
model_path = await _train_nlu_with_validated_data(
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/model_training.py", line 759, in _train_nlu_with_validated_data
await rasa.nlu.train.train(
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/nlu/train.py", line 97, in train
trainer = Trainer(
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/nlu/model.py", line 167, in init
self.pipeline = self._build_pipeline(cfg, component_builder)
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/nlu/model.py", line 178, in _build_pipeline
component = component_builder.create_component(component_cfg, cfg)
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/nlu/components.py", line 938, in create_component
component = registry.create_component_by_config(component_config, cfg)
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/nlu/registry.py", line 193, in create_component_by_config
return component_class.create(component_config, config)
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/nlu/featurizers/dense_featurizer/lm_featurizer.py", line 105, in create
return cls(component_config, hf_transformers_loaded=hf_transformers_loaded)
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/rasa/nlu/featurizers/dense_featurizer/lm_featurizer.py", line 88, in init
self._load_model_instance(skip_model_load)
File "/Users/wenhk/Downloads/rasa_ch_faq/compoments/nlu/featurizer/lm_featurizer.py", line 83, in _load_model_instance
self.model = model_class_dict[self.model_name].from_pretrained(
File "/Users/anaconda3/envs/rasa/lib/python3.8/site-packages/transformers/modeling_tf_utils.py", line 1234, in from_pretrained
raise OSError(
OSError: Unable to load weights from h5 file. If you tried to load a TF 2.0 model from a PyTorch checkpoint, please set from_pt=True.

请问这是什么问题？

训练时间过长

当nlu的语料>20000条，训练的时候，如果不用ONNX模型花费的时间为15分钟，使用ONNX模型需要花费2个多小时，即使用GPU也是这样，这个有什么优化方法吗？

运行时报了Noreturn

python train.py 报错，麻烦帮看下啥问题

Some layers from the model checkpoint at pre_models were not used when initializing TFBertModel: ['mlm___cls', 'nsp___cls']

• This IS expected if you are initializing TFBertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
• This IS NOT expected if you are initializing TFBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
  All the layers of TFBertModel were initialized from the model checkpoint at pre_models.
  If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertModel for predictions without further training.
  2021-01-29 19:33:57.271548: W tensorflow/core/framework/op_kernel.cc:1767] OP_REQUIRES failed at einsum_op_impl.h:714 : Resource exhausted: OOM when allocating tensor with shape[64,12,218,218] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu
  Traceback (most recent call last):
  File "train.py", line 6, in
  training_files="data",
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/rasa/train.py", line 109, in train
  loop,
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/rasa/utils/common.py", line 308, in run_in_loop
  result = loop.run_until_complete(f)
  File "uvloop/loop.pyx", line 1456, in uvloop.loop.Loop.run_until_complete
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/rasa/train.py", line 174, in train_async
  finetuning_epoch_fraction=finetuning_epoch_fraction,
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/rasa/train.py", line 353, in _train_async_internal
  finetuning_epoch_fraction=finetuning_epoch_fraction,
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/rasa/train.py", line 396, in _do_training
  finetuning_epoch_fraction=finetuning_epoch_fraction,
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/rasa/train.py", line 818, in _train_nlu_with_validated_data
  **additional_arguments,
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/rasa/nlu/train.py", line 116, in train
  interpreter = trainer.train(training_data, **kwargs)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/rasa/nlu/model.py", line 209, in train
  updates = component.train(working_data, self.config, **context)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/rasa/nlu/featurizers/dense_featurizer/lm_featurizer.py", line 814, in train
  batch_docs = self._get_docs_for_batch(batch_messages, attribute)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/rasa/nlu/featurizers/dense_featurizer/lm_featurizer.py", line 767, in _get_docs_for_batch
  batch_token_ids, batch_tokens, batch_examples, attribute, inference_mode
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/rasa/nlu/featurizers/dense_featurizer/lm_featurizer.py", line 685, in _get_model_features_for_batch
  batch_attention_mask, padded_token_ids
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/rasa/nlu/featurizers/dense_featurizer/lm_featurizer.py", line 537, in _compute_batch_sequence_features
  np.array(padded_token_ids), attention_mask=np.array(batch_attention_mask)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py", line 985, in call
  outputs = call_fn(inputs, *args, **kwargs)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/transformers/models/bert/modeling_tf_bert.py", line 901, in call
  training=inputs["training"],
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py", line 985, in call
  outputs = call_fn(inputs, *args, **kwargs)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/transformers/models/bert/modeling_tf_bert.py", line 694, in call
  training=inputs["training"],
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py", line 985, in call
  outputs = call_fn(inputs, *args, **kwargs)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/transformers/models/bert/modeling_tf_bert.py", line 452, in call
  hidden_states, attention_mask, head_mask[i], output_attentions, training=training
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py", line 985, in call
  outputs = call_fn(inputs, *args, **kwargs)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/transformers/models/bert/modeling_tf_bert.py", line 418, in call
  hidden_states, attention_mask, head_mask, output_attentions, training=training
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py", line 985, in call
  outputs = call_fn(inputs, *args, **kwargs)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/transformers/models/bert/modeling_tf_bert.py", line 354, in call
  input_tensor, attention_mask, head_mask, output_attentions, training=training
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/tensorflow/python/keras/engine/base_layer.py", line 985, in call
  outputs = call_fn(inputs, *args, **kwargs)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/transformers/models/bert/modeling_tf_bert.py", line 288, in call
  attention_scores = tf.einsum("aecd,abcd->acbe", key_layer, query_layer)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/tensorflow/python/util/dispatch.py", line 201, in wrapper
  return target(*args, **kwargs)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/tensorflow/python/ops/special_math_ops.py", line 684, in einsum
  return _einsum_v2(equation, *inputs, **kwargs)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/tensorflow/python/ops/special_math_ops.py", line 1113, in _einsum_v2
  return gen_linalg_ops.einsum(inputs, resolved_equation)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/tensorflow/python/ops/gen_linalg_ops.py", line 1088, in einsum
  _ops.raise_from_not_ok_status(e, name)
  File "/root/anaconda3/envs/rasa/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 6843, in raise_from_not_ok_status
  six.raise_from(core._status_to_exception(e.code, message), None)
  File "", line 3, in raise_from
  tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[64,12,218,218] and type float on /job:localhost/replica:0/task:0/device:CPU:0 by allocator cpu [Op:Einsum]

单独 python bert_tokenizer.py 报错，是不是huggingface加载模型方式有变化？

rasa train 和rasa shell 都没问题。

执行rasa命令提示命未找到命令

[root@localhost rasa_ch_faq]# rasa train
-bash: rasa: 未找到命令

大佬 执行rasa命令提示这个是啥情况