Kye
1 year ago
parent
b2fd6250b5
commit
36f026344f
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import requests
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import os
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class Anthropic:
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"""Anthropic large language models."""
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def __init__(self, model="claude-2", max_tokens_to_sample=256, temperature=None, top_k=None, top_p=None, streaming=False, default_request_timeout=None):
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self.model = model
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self.max_tokens_to_sample = max_tokens_to_sample
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self.temperature = temperature
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self.top_k = top_k
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self.top_p = top_p
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self.streaming = streaming
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self.default_request_timeout = default_request_timeout or 600
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self.anthropic_api_url = os.getenv("ANTHROPIC_API_URL", "https://api.anthropic.com")
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self.anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
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def _default_params(self):
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"""Get the default parameters for calling Anthropic API."""
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d = {
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"max_tokens_to_sample": self.max_tokens_to_sample,
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"model": self.model,
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}
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if self.temperature is not None:
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d["temperature"] = self.temperature
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if self.top_k is not None:
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d["top_k"] = self.top_k
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if self.top_p is not None:
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d["top_p"] = self.top_p
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return d
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def _call(self, prompt, stop=None):
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"""Call out to Anthropic's completion endpoint."""
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stop = stop or []
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params = self._default_params()
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headers = {"Authorization": f"Bearer {self.anthropic_api_key}"}
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data = {
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"prompt": prompt,
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"stop_sequences": stop,
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**params
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}
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response = requests.post(f"{self.anthropic_api_url}/completions", headers=headers, json=data, timeout=self.default_request_timeout)
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return response.json().get("completion")
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import json
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from typing import List
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from langchain.tools.base import BaseTool
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FINISH_NAME = "finish"
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class PromptGenerator:
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"""A class for generating custom prompt strings.
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Does this based on constraints, commands, resources, and performance evaluations.
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"""
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def __init__(self) -> None:
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"""Initialize the PromptGenerator object.
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Starts with empty lists of constraints, commands, resources,
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and performance evaluations.
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"""
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self.constraints: List[str] = []
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self.commands: List[BaseTool] = []
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self.resources: List[str] = []
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self.performance_evaluation: List[str] = []
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self.response_format = {
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"thoughts": {
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"text": "thought",
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"reasoning": "reasoning",
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"plan": "- short bulleted\n- list that conveys\n- long-term plan",
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"criticism": "constructive self-criticism",
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"speak": "thoughts summary to say to user",
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},
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"command": {"name": "command name", "args": {"arg name": "value"}},
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}
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def add_constraint(self, constraint: str) -> None:
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"""
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Add a constraint to the constraints list.
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Args:
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constraint (str): The constraint to be added.
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"""
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self.constraints.append(constraint)
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def add_tool(self, tool: BaseTool) -> None:
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self.commands.append(tool)
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def _generate_command_string(self, tool: BaseTool) -> str:
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output = f"{tool.name}: {tool.description}"
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output += f", args json schema: {json.dumps(tool.args)}"
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return output
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def add_resource(self, resource: str) -> None:
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"""
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Add a resource to the resources list.
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Args:
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resource (str): The resource to be added.
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"""
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self.resources.append(resource)
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def add_performance_evaluation(self, evaluation: str) -> None:
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"""
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Add a performance evaluation item to the performance_evaluation list.
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Args:
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evaluation (str): The evaluation item to be added.
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"""
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self.performance_evaluation.append(evaluation)
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def _generate_numbered_list(self, items: list, item_type: str = "list") -> str:
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"""
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Generate a numbered list from given items based on the item_type.
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Args:
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items (list): A list of items to be numbered.
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item_type (str, optional): The type of items in the list.
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Defaults to 'list'.
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Returns:
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str: The formatted numbered list.
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"""
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if item_type == "command":
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command_strings = [
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f"{i + 1}. {self._generate_command_string(item)}"
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for i, item in enumerate(items)
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]
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finish_description = (
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"use this to signal that you have finished all your objectives"
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)
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finish_args = (
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'"response": "final response to let '
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'people know you have finished your objectives"'
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)
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finish_string = (
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f"{len(items) + 1}. {FINISH_NAME}: "
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f"{finish_description}, args: {finish_args}"
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)
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return "\n".join(command_strings + [finish_string])
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else:
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return "\n".join(f"{i+1}. {item}" for i, item in enumerate(items))
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def generate_prompt_string(self) -> str:
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"""Generate a prompt string.
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Returns:
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str: The generated prompt string.
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"""
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formatted_response_format = json.dumps(self.response_format, indent=4)
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prompt_string = (
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f"Constraints:\n{self._generate_numbered_list(self.constraints)}\n\n"
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f"Commands:\n"
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f"{self._generate_numbered_list(self.commands, item_type='command')}\n\n"
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f"Resources:\n{self._generate_numbered_list(self.resources)}\n\n"
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f"Performance Evaluation:\n"
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f"{self._generate_numbered_list(self.performance_evaluation)}\n\n"
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f"You should only respond in JSON format as described below "
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f"\nResponse Format: \n{formatted_response_format} "
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f"\nEnsure the response can be parsed by Python json.loads"
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)
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return prompt_string
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def get_prompt(tools: List[BaseTool]) -> str:
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"""Generates a prompt string.
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It includes various constraints, commands, resources, and performance evaluations.
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Returns:
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str: The generated prompt string.
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"""
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# Initialize the PromptGenerator object
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prompt_generator = PromptGenerator()
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# Add constraints to the PromptGenerator object
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prompt_generator.add_constraint(
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"~4000 word limit for short term memory. "
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"Your short term memory is short, "
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"so immediately save important information to files."
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)
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prompt_generator.add_constraint(
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"If you are unsure how you previously did something "
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"or want to recall past events, "
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"thinking about similar events will help you remember."
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)
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prompt_generator.add_constraint("No user assistance")
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prompt_generator.add_constraint(
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'Exclusively use the commands listed in double quotes e.g. "command name"'
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)
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# Add commands to the PromptGenerator object
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for tool in tools:
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prompt_generator.add_tool(tool)
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# Add resources to the PromptGenerator object
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prompt_generator.add_resource(
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"Internet access for searches and information gathering."
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)
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prompt_generator.add_resource("Long Term memory management.")
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prompt_generator.add_resource(
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"GPT-3.5 powered Agents for delegation of simple tasks."
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)
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prompt_generator.add_resource("File output.")
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# Add performance evaluations to the PromptGenerator object
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prompt_generator.add_performance_evaluation(
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"Continuously review and analyze your actions "
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"to ensure you are performing to the best of your abilities."
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)
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prompt_generator.add_performance_evaluation(
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"Constructively self-criticize your big-picture behavior constantly."
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)
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prompt_generator.add_performance_evaluation(
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"Reflect on past decisions and strategies to refine your approach."
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)
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prompt_generator.add_performance_evaluation(
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"Every command has a cost, so be smart and efficient. "
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"Aim to complete tasks in the least number of steps."
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)
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# Generate the prompt string
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prompt_string = prompt_generator.generate_prompt_string()
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return prompt_string
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@ -1,35 +1,24 @@
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"""Tool for asking human input."""
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from typing import Callable, Optional
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from pydantic import Field
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from langchain.callbacks.manager import CallbackManagerForToolRun
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from langchain.tools.base import BaseTool
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def _print_func(text: str) -> None:
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print("\n")
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print(text)
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class HumanInputRun(BaseTool):
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class HumanInputRun:
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"""Tool that asks user for input."""
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name = "human"
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description = (
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"You can ask a human for guidance when you think you "
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"got stuck or you are not sure what to do next. "
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"The input should be a question for the human."
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)
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prompt_func: Callable[[str], None] = Field(default_factory=lambda: _print_func)
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input_func: Callable = Field(default_factory=lambda: input)
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def _run(
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self,
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query: str,
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run_manager: Optional[CallbackManagerForToolRun] = None,
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) -> str:
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def __init__(self, prompt_func=None, input_func=None):
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self.name = "human"
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self.description = (
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"You can ask a human for guidance when you think you "
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"got stuck or you are not sure what to do next. "
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"The input should be a question for the human."
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)
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self.prompt_func = prompt_func if prompt_func else self._print_func
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self.input_func = input_func if input_func else input
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def _print_func(self, text: str) -> None:
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print("\n")
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print(text)
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def run(self, query: str) -> str:
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"""Use the Human input tool."""
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self.prompt_func(query)
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return self.input_func()
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