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866df66fe4
* First commit while I figure this out * make fixup * Remove unused method * Store prompt attrib * Fix prompt argument for tests * Make same changes in fast tokenizer * Remove global prompts from fast tokenizer too * stash commit * stash commit * Migrate PromptConfig to its True Final Location * Replace Conversation entirely with the new class * Import/dependency fixes * Import/dependency fixes * Change format for lots of default prompts * More default prompt fixups * Revert llama old methods so we can compare * Fix some default configs * Fix some default configs * Fix misspelled kwarg * Fixes for Blenderbot * make fixup * little rebase cleanup * Add basic documentation * Quick doc fix * Truncate docstring for now * Add handling for the case when messages is a single string * Quick llama merges * Update conversational pipeline and tests * Add a couple of legacy properties for backward compatibility * More legacy handling * Add docstring for build_conversation_input_ids * Restructure PromptConfig * Let's start T E M P L A T I N G * Refactor all default configs to use templates instead * Revert changes to the special token properties since we don't need them anymore * More class templates * Make the sandbox even sandier * Everything replaced with pure templating * Remove docs for PromptConfig * Add testing and optional requirement boilerplate * Fix imports and make fixup * Fix LLaMA tests and add Conversation docstring * Finally get LLaMA working with the template system * Finally get LLaMA working with the template system * make fixup * make fixup * fmt-off for the long lists of test tokens * Rename method to apply_chat_template for now * Start on documentation * Make chat_template a property that reads through to the default if it's not set * Expand docs * Expand chat templating doc some more * trim/lstrip blocks by default and update doc * Few doc tweaks * rebase cleanup * Clarify docstring * rebase cleanup * rebase cleanup * make fixup * Quick doc edit * Reformat the standard template to match ChatML * Re-add PEFT check * Update docs/source/en/chat_templating.md Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com> * Add apply_chat_template to the tokenizer doc * make fixup * Add doc links * Fix chat links * Fix chat links * Explain system messages in the doc * Add chat template test * Proper save-loading for chat template attribute * Add test skips for layout models * Remove _build_conversation_input_ids, add default_chat_template to code_llama * Make sure all LLaMA models are using the latest template * Remove default_system_prompt block in code_llama because it has no default prompt * Update ConversationPipeline preprocess * Add correct #Copied from links to the default_chat_templates * Remove unneeded type checking line * Add a dummy mark_processsed method * Reorganize Conversation to have **deprecated_kwargs * Update chat_templating.md * Quick fix to LLAMA tests * Small doc tweaks * Add proper docstrings and "copied from" statements to all default chat templates * Merge use_default_system_prompt support for code_llama too * Improve clarity around self.chat_template * Docstring fix * Fix blenderbot default template * More doctest fix * Break out some tokenizer kwargs * Update doc to explain default templates * Quick tweaks to tokenizer args * Cleanups for tokenizer args * Add note about cacheing * Quick tweak to the chat-templating doc * Update the LLaMA template with error checking and correct system message embedding * make fixup * make fixup * add requires_jinja * Cleanup to expected output formatting * Add cacheing * Fix typo in llama default template * Update LLaMA tests * Update documentation * Improved legacy handling in the Conversation class * Update Jinja template with proper error handling * Quick bugfix * Proper exception raising * Change cacheing behaviour so it doesn't try to pickle an entire Jinja env * make fixup * rebase cleanup --------- Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
446 lines
22 KiB
Python
446 lines
22 KiB
Python
# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import unittest
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from transformers import (
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MODEL_FOR_CAUSAL_LM_MAPPING,
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MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
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TF_MODEL_FOR_CAUSAL_LM_MAPPING,
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TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
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AutoModelForCausalLM,
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AutoModelForSeq2SeqLM,
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AutoTokenizer,
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BlenderbotSmallForConditionalGeneration,
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BlenderbotSmallTokenizer,
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Conversation,
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ConversationalPipeline,
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TFAutoModelForCausalLM,
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pipeline,
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)
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from transformers.testing_utils import (
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is_pipeline_test,
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is_torch_available,
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require_tf,
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require_torch,
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slow,
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torch_device,
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)
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from .test_pipelines_common import ANY
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DEFAULT_DEVICE_NUM = -1 if torch_device == "cpu" else 0
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@is_pipeline_test
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class ConversationalPipelineTests(unittest.TestCase):
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def tearDown(self):
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super().tearDown()
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# clean-up as much as possible GPU memory occupied by PyTorch
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gc.collect()
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if is_torch_available():
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import torch
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torch.cuda.empty_cache()
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model_mapping = dict(
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list(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items())
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if MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
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else [] + list(MODEL_FOR_CAUSAL_LM_MAPPING.items())
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if MODEL_FOR_CAUSAL_LM_MAPPING
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else []
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)
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tf_model_mapping = dict(
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list(TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items())
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if TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
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else [] + list(TF_MODEL_FOR_CAUSAL_LM_MAPPING.items())
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if TF_MODEL_FOR_CAUSAL_LM_MAPPING
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else []
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)
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def get_test_pipeline(self, model, tokenizer, processor):
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conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
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return conversation_agent, [Conversation("Hi there!")]
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def run_pipeline_test(self, conversation_agent, _):
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# Simple
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outputs = conversation_agent(Conversation("Hi there!"))
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self.assertEqual(
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outputs,
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Conversation([{"role": "user", "content": "Hi there!"}, {"role": "assistant", "content": ANY(str)}]),
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)
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# Single list
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outputs = conversation_agent([Conversation("Hi there!")])
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self.assertEqual(
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outputs,
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Conversation([{"role": "user", "content": "Hi there!"}, {"role": "assistant", "content": ANY(str)}]),
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)
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# Batch
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conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
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conversation_2 = Conversation("What's the last book you have read?")
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self.assertEqual(len(conversation_1), 1)
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self.assertEqual(len(conversation_2), 1)
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outputs = conversation_agent([conversation_1, conversation_2])
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self.assertEqual(outputs, [conversation_1, conversation_2])
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self.assertEqual(
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outputs,
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[
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Conversation(
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[
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{"role": "user", "content": "Going to the movies tonight - any suggestions?"},
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{"role": "assistant", "content": ANY(str)},
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],
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),
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Conversation(
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[
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{"role": "user", "content": "What's the last book you have read?"},
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{"role": "assistant", "content": ANY(str)},
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]
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),
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],
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)
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# One conversation with history
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conversation_2.add_message({"role": "user", "content": "Why do you recommend it?"})
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outputs = conversation_agent(conversation_2)
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self.assertEqual(outputs, conversation_2)
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self.assertEqual(
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outputs,
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Conversation(
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[
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{"role": "user", "content": "What's the last book you have read?"},
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{"role": "assistant", "content": ANY(str)},
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{"role": "user", "content": "Why do you recommend it?"},
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{"role": "assistant", "content": ANY(str)},
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]
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),
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)
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@require_torch
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@slow
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def test_integration_torch_conversation(self):
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# When
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conversation_agent = pipeline(task="conversational", device=DEFAULT_DEVICE_NUM)
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conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
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conversation_2 = Conversation("What's the last book you have read?")
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# Then
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self.assertEqual(len(conversation_1.past_user_inputs), 0)
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self.assertEqual(len(conversation_2.past_user_inputs), 0)
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# When
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result = conversation_agent([conversation_1, conversation_2], do_sample=False, max_length=1000)
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# Then
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self.assertEqual(result, [conversation_1, conversation_2])
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self.assertEqual(len(result[0].past_user_inputs), 1)
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self.assertEqual(len(result[1].past_user_inputs), 1)
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self.assertEqual(len(result[0].generated_responses), 1)
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self.assertEqual(len(result[1].generated_responses), 1)
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self.assertEqual(result[0].past_user_inputs[0], "Going to the movies tonight - any suggestions?")
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self.assertEqual(result[0].generated_responses[0], "The Big Lebowski")
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self.assertEqual(result[1].past_user_inputs[0], "What's the last book you have read?")
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self.assertEqual(result[1].generated_responses[0], "The Last Question")
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# When
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conversation_2.add_user_input("Why do you recommend it?")
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result = conversation_agent(conversation_2, do_sample=False, max_length=1000)
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# Then
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self.assertEqual(result, conversation_2)
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self.assertEqual(len(result.past_user_inputs), 2)
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self.assertEqual(len(result.generated_responses), 2)
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self.assertEqual(result.past_user_inputs[1], "Why do you recommend it?")
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self.assertEqual(result.generated_responses[1], "It's a good book.")
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@require_torch
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@slow
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def test_integration_torch_conversation_truncated_history(self):
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# When
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conversation_agent = pipeline(task="conversational", min_length_for_response=24, device=DEFAULT_DEVICE_NUM)
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conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
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# Then
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self.assertEqual(len(conversation_1.past_user_inputs), 0)
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# When
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result = conversation_agent(conversation_1, do_sample=False, max_length=36)
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# Then
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self.assertEqual(result, conversation_1)
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self.assertEqual(len(result.past_user_inputs), 1)
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self.assertEqual(len(result.generated_responses), 1)
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self.assertEqual(result.past_user_inputs[0], "Going to the movies tonight - any suggestions?")
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self.assertEqual(result.generated_responses[0], "The Big Lebowski")
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# When
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conversation_1.add_user_input("Is it an action movie?")
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result = conversation_agent(conversation_1, do_sample=False, max_length=36)
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# Then
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self.assertEqual(result, conversation_1)
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self.assertEqual(len(result.past_user_inputs), 2)
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self.assertEqual(len(result.generated_responses), 2)
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self.assertEqual(result.past_user_inputs[1], "Is it an action movie?")
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self.assertEqual(result.generated_responses[1], "It's a comedy.")
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@require_torch
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def test_small_model_pt(self):
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tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
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model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
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conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
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conversation = Conversation("hello")
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output = conversation_agent(conversation)
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self.assertEqual(output, Conversation(past_user_inputs=["hello"], generated_responses=["Hi"]))
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@require_tf
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def test_small_model_tf(self):
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tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
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model = TFAutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
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conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
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conversation = Conversation("hello")
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output = conversation_agent(conversation)
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self.assertEqual(output, Conversation(past_user_inputs=["hello"], generated_responses=["Hi"]))
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@require_torch
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@slow
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def test_integration_torch_conversation_dialogpt_input_ids(self):
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tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-small")
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model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-small")
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conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
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conversation_1 = Conversation("hello")
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inputs = conversation_agent.preprocess(conversation_1)
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self.assertEqual(inputs["input_ids"].tolist(), [[31373, 50256]])
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conversation_2 = Conversation("how are you ?", past_user_inputs=["hello"], generated_responses=["Hi there!"])
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inputs = conversation_agent.preprocess(conversation_2)
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self.assertEqual(
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inputs["input_ids"].tolist(), [[31373, 50256, 17250, 612, 0, 50256, 4919, 389, 345, 5633, 50256]]
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)
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@unittest.skip("Model is curently gated")
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@require_torch
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@slow
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def test_integration_torch_conversation_llama2_input_ids(self):
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tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat-hf", use_default_system_prompt=True)
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conversation = Conversation(
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"What is so great about #1?",
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past_user_inputs=["I am going to Paris, what should I see?"],
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generated_responses=[
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"""\
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Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:
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1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.
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2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.
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3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.
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These are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."""
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],
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)
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inputs = tokenizer._build_conversation_input_ids(conversation)
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# fmt: off
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EXPECTED_INPUTS_IDS = [ 1, 518, 25580, 29962, 3532, 14816, 29903, 6778, 13, 3492, 526, 263, 8444, 29892, 3390, 1319, 322, 15993, 20255, 29889, 29849, 1234, 408, 1371, 3730, 408, 1950, 29892, 1550, 1641, 9109, 29889, 29871, 3575, 6089, 881, 451, 3160, 738, 10311, 1319, 29892, 443, 621, 936, 29892, 11021, 391, 29892, 7916, 391, 29892, 304, 27375, 29892, 18215, 29892, 470, 27302, 2793, 29889, 3529, 9801, 393, 596, 20890, 526, 5374, 635, 443, 5365, 1463, 322, 6374, 297, 5469, 29889, 13, 13, 3644, 263, 1139, 947, 451, 1207, 738, 4060, 29892, 470, 338, 451, 2114, 1474, 16165, 261, 296, 29892, 5649, 2020, 2012, 310, 22862, 1554, 451, 1959, 29889, 960, 366, 1016, 29915, 29873, 1073, 278, 1234, 304, 263, 1139, 29892, 3113, 1016, 29915, 29873, 6232, 2089, 2472, 29889, 13, 29966, 829, 14816, 29903, 6778, 13, 13, 29902, 626, 2675, 304, 3681, 29892, 825, 881, 306, 1074, 29973, 518, 29914, 25580, 29962, 3681, 29892, 278, 7483, 310, 3444, 29892, 338, 2998, 363, 967, 380, 27389, 11258, 29892, 1616, 19133, 29879, 29892, 15839, 2982, 22848, 29892, 322, 6017, 7716, 25005, 29889, 2266, 526, 777, 310, 278, 2246, 19650, 1953, 304, 1074, 297, 3681, 29901, 13, 13, 29896, 29889, 450, 382, 2593, 295, 23615, 29901, 450, 9849, 293, 382, 2593, 295, 23615, 338, 697, 310, 278, 1556, 5936, 13902, 2982, 22848, 297, 278, 3186, 322, 16688, 2078, 271, 400, 5086, 8386, 310, 278, 4272, 29889, 13, 29906, 29889, 450, 4562, 12675, 6838, 29901, 450, 4562, 12675, 338, 697, 310, 278, 3186, 29915, 29879, 10150, 322, 1556, 13834, 19133, 29879, 29892, 27261, 385, 21210, 573, 4333, 310, 1616, 322, 24238, 29879, 29892, 3704, 278, 2598, 29874, 29420, 29889, 13, 29941, 29889, 24337, 29899, 29928, 420, 315, 21471, 29901, 910, 9560, 274, 21471, 338, 697, 310, 278, 1556, 13834, 2982, 22848, 297, 3681, 322, 338, 2998, 363, 967, 22883, 293, 11258, 322, 380, 27389, 380, 7114, 12917, 5417, 29889, 13, 13, 1349, 968, 526, 925, 263, 2846, 310, 278, 1784, 19650, 1953, 393, 3681, 756, 304, 5957, 29889, 2973, 577, 1568, 304, 1074, 322, 437, 29892, 372, 29915, 29879, 694, 4997, 393, 3681, 338, 697, 310, 278, 1556, 5972, 6282, 391, 15422, 800, 297, 278, 3186, 29889, 29871, 2, 1, 518, 25580, 29962, 1724, 338, 577, 2107, 1048, 396, 29896, 29973, 518, 29914, 25580, 29962]
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# fmt: on
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self.assertEqual(inputs, EXPECTED_INPUTS_IDS)
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model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-chat-hf")
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conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
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EXPECTED_TEXT = "what topic you want to focus on and create content around it. This will help you stand out from other creators and attract a specific audience.\n\nStep 2: Set Up Your Channel\nCreate your YouTube account and customize your channel with your branding and logo. Make sure your channel name and profile picture are consistent with your niche.\n\nStep 3: Plan Your Content\nDevelop a content strategy that includes the type of content you want to create, how often you will post, and when you will post. Consider creating a content calendar to help you stay organized.\n\nStep 4: Invest in Quality Equipment\nInvest in good quality camera and microphone equipment to ensure your videos look and sound professional. You don't need to break the bank, but investing in good equipment will make a big difference in the quality of your videos.\n\nStep 5: Optimize Your Videos for Search\nUse keywords in your video titles, descriptions, and tags to help people find your videos when they search for topics related to your niche"
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conversation = Conversation(
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"<<SYS>>\n Only answer with emojis, and charades\n<</SYS>>\n\nHow can I build a house in 10 steps?"
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)
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result = conversation_agent(conversation)
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self.assertEqual(result.generated_responses[-1], EXPECTED_TEXT)
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@require_torch
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@slow
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def test_integration_torch_conversation_blenderbot_400M_input_ids(self):
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tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
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model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-400M-distill")
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conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
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# test1
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conversation_1 = Conversation("hello")
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inputs = conversation_agent.preprocess(conversation_1)
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self.assertEqual(inputs["input_ids"].tolist(), [[1710, 86, 2]])
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# test2
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conversation_1 = Conversation(
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"I like lasagne.",
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past_user_inputs=["hello"],
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generated_responses=[
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" Do you like lasagne? It is a traditional Italian dish consisting of a shepherd's pie."
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],
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)
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inputs = conversation_agent.preprocess(conversation_1)
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self.assertEqual(
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inputs["input_ids"].tolist(),
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[
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# This should be compared with the same conversation on ParlAI `safe_interactive` demo.
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[
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1710, # hello
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86,
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228, # Double space
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228,
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946,
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304,
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398,
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6881,
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558,
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964,
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38,
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452,
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315,
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265,
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6252,
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452,
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322,
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968,
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6884,
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3146,
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278,
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306,
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265,
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617,
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87,
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388,
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75,
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341,
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286,
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521,
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21,
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228, # Double space
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228,
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281, # I like lasagne.
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398,
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6881,
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558,
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964,
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21,
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2, # EOS
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],
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],
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)
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@require_torch
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@slow
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def test_integration_torch_conversation_blenderbot_400M(self):
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tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot-400M-distill")
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model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot-400M-distill")
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conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer)
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conversation_1 = Conversation("hello")
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result = conversation_agent(
|
|
conversation_1,
|
|
)
|
|
self.assertEqual(
|
|
result.generated_responses[0],
|
|
# ParlAI implementation output, we have a different one, but it's our
|
|
# second best, you can check by using num_return_sequences=10
|
|
# " Hello! How are you? I'm just getting ready to go to work, how about you?",
|
|
" Hello! How are you doing today? I just got back from a walk with my dog.",
|
|
)
|
|
|
|
conversation_1 = Conversation("Lasagne hello")
|
|
result = conversation_agent(conversation_1, encoder_no_repeat_ngram_size=3)
|
|
self.assertEqual(
|
|
result.generated_responses[0],
|
|
" Do you like lasagne? It is a traditional Italian dish consisting of a shepherd's pie.",
|
|
)
|
|
|
|
conversation_1 = Conversation(
|
|
"Lasagne hello Lasagne is my favorite Italian dish. Do you like lasagne? I like lasagne."
|
|
)
|
|
result = conversation_agent(
|
|
conversation_1,
|
|
encoder_no_repeat_ngram_size=3,
|
|
)
|
|
self.assertEqual(
|
|
result.generated_responses[0],
|
|
" Me too. I like how it can be topped with vegetables, meats, and condiments.",
|
|
)
|
|
|
|
@require_torch
|
|
@slow
|
|
def test_integration_torch_conversation_encoder_decoder(self):
|
|
# When
|
|
tokenizer = AutoTokenizer.from_pretrained("facebook/blenderbot_small-90M")
|
|
model = AutoModelForSeq2SeqLM.from_pretrained("facebook/blenderbot_small-90M")
|
|
conversation_agent = ConversationalPipeline(model=model, tokenizer=tokenizer, device=DEFAULT_DEVICE_NUM)
|
|
|
|
conversation_1 = Conversation("My name is Sarah and I live in London")
|
|
conversation_2 = Conversation("Going to the movies tonight, What movie would you recommend? ")
|
|
# Then
|
|
self.assertEqual(len(conversation_1.past_user_inputs), 0)
|
|
self.assertEqual(len(conversation_2.past_user_inputs), 0)
|
|
# When
|
|
result = conversation_agent([conversation_1, conversation_2], do_sample=False, max_length=1000)
|
|
# Then
|
|
self.assertEqual(result, [conversation_1, conversation_2])
|
|
self.assertEqual(len(result[0].past_user_inputs), 1)
|
|
self.assertEqual(len(result[1].past_user_inputs), 1)
|
|
self.assertEqual(len(result[0].generated_responses), 1)
|
|
self.assertEqual(len(result[1].generated_responses), 1)
|
|
self.assertEqual(result[0].past_user_inputs[0], "My name is Sarah and I live in London")
|
|
self.assertEqual(
|
|
result[0].generated_responses[0],
|
|
"hi sarah, i live in london as well. do you have any plans for the weekend?",
|
|
)
|
|
self.assertEqual(
|
|
result[1].past_user_inputs[0], "Going to the movies tonight, What movie would you recommend? "
|
|
)
|
|
self.assertEqual(
|
|
result[1].generated_responses[0], "i don't know... i'm not really sure. what movie are you going to see?"
|
|
)
|
|
# When
|
|
conversation_1.add_user_input("Not yet, what about you?")
|
|
conversation_2.add_user_input("What's your name?")
|
|
result = conversation_agent([conversation_1, conversation_2], do_sample=False, max_length=1000)
|
|
# Then
|
|
self.assertEqual(result, [conversation_1, conversation_2])
|
|
self.assertEqual(len(result[0].past_user_inputs), 2)
|
|
self.assertEqual(len(result[1].past_user_inputs), 2)
|
|
self.assertEqual(len(result[0].generated_responses), 2)
|
|
self.assertEqual(len(result[1].generated_responses), 2)
|
|
self.assertEqual(result[0].past_user_inputs[1], "Not yet, what about you?")
|
|
self.assertEqual(result[0].generated_responses[1], "i don't have any plans yet. i'm not sure what to do yet.")
|
|
self.assertEqual(result[1].past_user_inputs[1], "What's your name?")
|
|
self.assertEqual(result[1].generated_responses[1], "i don't have a name, but i'm going to see a horror movie.")
|
|
|
|
@require_torch
|
|
@slow
|
|
def test_from_pipeline_conversation(self):
|
|
model_id = "facebook/blenderbot_small-90M"
|
|
|
|
# from model id
|
|
conversation_agent_from_model_id = pipeline("conversational", model=model_id, tokenizer=model_id)
|
|
|
|
# from model object
|
|
model = BlenderbotSmallForConditionalGeneration.from_pretrained(model_id)
|
|
tokenizer = BlenderbotSmallTokenizer.from_pretrained(model_id)
|
|
conversation_agent_from_model = pipeline("conversational", model=model, tokenizer=tokenizer)
|
|
|
|
conversation = Conversation("My name is Sarah and I live in London")
|
|
conversation_copy = Conversation("My name is Sarah and I live in London")
|
|
|
|
result_model_id = conversation_agent_from_model_id([conversation])
|
|
result_model = conversation_agent_from_model([conversation_copy])
|
|
|
|
# check for equality
|
|
self.assertEqual(
|
|
result_model_id.generated_responses[0],
|
|
"hi sarah, i live in london as well. do you have any plans for the weekend?",
|
|
)
|
|
self.assertEqual(
|
|
result_model_id.generated_responses[0],
|
|
result_model.generated_responses[0],
|
|
)
|