Update prompts.py

prompts.py

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+PREFIX = """You are a Live RSS Feed Reader with a set of tools.
+Your duty is to use the provided tools to complete the users request.
+Complete your purpose and return the result to the user as quickly as possible.
+Make sure your information is current
+Current Date and Time is:
+{timestamp}
+You have access to the following tools:
+- action: UPDATE-TASK action_input=NEW_TASK
+- action: READ-RSS action_input=URL
+- action: COMPLETE
+Purpose:
+{purpose}
+"""
+
+FINDER = """
+Instructions
+- Select an RSS FEED URL from the provided list of RSS FEEDS
+- Use the tool provided tool to read the RSS FEED URL
+- Compile a report that includes all relevant data points like, Title, Description, Link
+- When the have complete the user request, end with:\naction: COMPLETE
+Use the following format:
+task: the input task you must complete
+action: the action to take (should be one of [UPDATE-TASK, READ-RSS, COMPLETE]) action_input=XXX
+You are attempting to complete the task
+task: {task}
+{history}"""
+
+FIND_FEEDS = """
+You are attempting to complete the task
+task: {task}
+RSS FEEDS:
+{rss_feeds}
+{history}"""
+
+MODEL_FINDER_PRE = """
+You have access to the following tools:
+- action: UPDATE-TASK action_input=NEW_TASK
+- action: SEARCH action_input=SEARCH_QUERY
+- action: COMPLETE
+Instructions
+- Generate a search query for the requested model from these options: 
+>{TASKS}
+- Return the search query using the search tool
+- Wait for the search to return a result
+- After observing the search result, choose a model
+- Return the name of the repo and model ("repo/model")
+- When you are finished, return with  action: COMPLETE
+Use the following format:
+task: the input task you must complete
+thought: you should always think about what to do
+action: the action to take (should be one of [UPDATE-TASK, SEARCH, COMPLETE]) action_input=XXX
+observation: the result of the action
+thought: you should always think after an observation
+action: SEARCH action_input='text-generation'
+... (thought/action/observation/thought can repeat N times)
+Example:
+***************************
+User command: Find me a text generation model with less than 50M parameters.
+thought: I will use the option 'text-generation'
+action: SEARCH action_input=text-generation
+--- pause and wait for data to be returned ---
+Response:
+Assistant: I found the 'distilgpt2' model which has around 82M parameters. It is a distilled version of the GPT-2 model from OpenAI, trained by Hugging Face. Here's how to load it:
+action: COMPLETE
+***************************
+You are attempting to complete the task
+task: {task}
+{history}"""
+
+
+ACTION_PROMPT = """
+You have access to the following tools:
+- action: UPDATE-TASK action_input=NEW_TASK
+- action: SEARCH action_input=SEARCH_QUERY
+- action: COMPLETE
+Instructions
+- Generate a search query for the requested model
+- Return the search query using the search tool
+- Wait for the search to return a result
+- After observing the search result, choose a model
+- Return the name of the repo and model ("repo/model")
+Use the following format:
+task: the input task you must complete
+action: the action to take (should be one of [UPDATE-TASK, SEARCH, COMPLETE]) action_input=XXX
+observation: the result of the action
+action: SEARCH action_input='text generation'
+You are attempting to complete the task
+task: {task}
+{history}"""
+
+ACTION_PROMPT_PRE = """
+You have access to the following tools:
+- action: UPDATE-TASK action_input=NEW_TASK
+- action: SEARCH action_input=SEARCH_QUERY
+- action: COMPLETE
+Instructions
+- Generate a search query for the requested model
+- Return the search query using the search tool
+- Wait for the search to return a result
+- After observing the search result, choose a model
+- Return the name of the repo and model ("repo/model")
+Use the following format:
+task: the input task you must complete
+thought: you should always think about what to do
+action: the action to take (should be one of [UPDATE-TASK, SEARCH, COMPLETE]) action_input=XXX
+observation: the result of the action
+thought: you should always think after an observation
+action: SEARCH action_input='text generation'
+... (thought/action/observation/thought can repeat N times)
+You are attempting to complete the task
+task: {task}
+{history}"""
+
+TASK_PROMPT = """
+You are attempting to complete the task
+task: {task}
+Progress:
+{history}
+Tasks should be small, isolated, and independent
+To start a search use the format:
+action: RSS-FEEDS action_input=RSS_FEED_URL
+What should the task be for us to achieve the purpose?
+task: """
+
+
+COMPRESS_DATA_PROMPT_SMALL = """
+You are attempting to complete the task
+task: {task}
+Current data:
+{knowledge}
+New data:
+{history}
+Compress the data above into a concise data presentation of relevant data
+Include datapoints that will provide greater accuracy in completing the task
+Return the data in JSON format to save space
+"""
+
+
+
+
+COMPRESS_DATA_PROMPT = """
+You are attempting to complete the task
+task: {task}
+Current data:
+{knowledge}
+New data:
+{history}
+Compress the data above into a concise data presentation of relevant data
+Include a datapoints and source urls that will provide greater accuracy in completing the task
+"""
+
+COMPRESS_HISTORY_PROMPT = """
+You are attempting to complete the task
+task: {task}
+Progress:
+{history}
+Compress the timeline of progress above into a single summary (as a paragraph)
+Include all important milestones, the current challenges, and implementation details necessary to proceed
+"""
+
+LOG_PROMPT = """
+PROMPT
+**************************************
+{}
+**************************************
+"""
+
+LOG_RESPONSE = """
+RESPONSE
+**************************************
+{}
+**************************************
+"""
+
+
+FINDER1 = """
+Example Response 1:
+User command: Find me a text generation model with less than 50M parameters.
+Query: text generation
+--- pause and wait for data to be returned ---
+Assistant: I found the 'distilgpt2' model which has around 82M parameters. It is a distilled version of the GPT-2 model from OpenAI, trained by Hugging Face. Here's how to load it:
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+tokenizer = AutoTokenizer.from_pretrained("distilgpt2")
+model = AutoModelForMaskedLM.from_pretrained("distilgpt2")
+```
+Example Response 2:
+User command: Help me locate a multilingual Named Entity Recognition model.
+Query: named entity recognition
+--- pause and wait for data to be returned ---
+Assistant: I discovered the 'dbmdz/bert-base-multilingual-cased' model, which supports named entity recognition across multiple languages. Here's how to load it:
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+tokenizer = AutoTokenizer.from_pretrained("dbmdz/bert-base-multilingual-cased")
+model = AutoModelForTokenClassification.from_pretrained("dbmdz/bert-base-multilingual-cased")
+```
+Example Response 3:
+User command: Search for a question-answering model fine-tuned on the SQuAD v2 dataset with more than 90% accuracy.
+action: SEARCH action_input=named entity recognition
+--- pause and wait for data to be returned ---
+Assistant: I found the 'pranavkv/roberta-base-squad2' model, which was fine-tuned on the SQuAD v2 dataset and achieves approximately 91% accuracy. Below is the way to load it:
+```python
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+tokenizer = AutoTokenizer.from_pretrained("pranavkv/roberta-base-squad2")
+model = AutoModelForQuestionAnswering.from_pretrained("pranavkv/roberta-base-squad2")
+```
+"""