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Add basic function calling example using a llama-cli python wrapper
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46
examples/function-calling/README.md
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examples/function-calling/README.md
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# llama.cpp/examples/function-calling
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This example shows how to do basic function calling using llama-cli and a python wrapper to declare and call functions.
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## Options
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Important options for llama-cli-function-runner.py:
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- `-m FNAME, --model FNAME`: Specify the path to the function calling model (e.g., `-m "$(huggingface-cli download meetkai/functionary-small-v3.2-GGUF functionary-small-v3.2.Q4_0.gguf)"`).
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- `--ctx-size N`: Set the size of the prompt context. The default is 1024
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- `--special`: show special tokens and function calling details
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## Example showing showing function call details
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```
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./examples/function-calling/llama-cli-function-runner.py -m `huggingface-cli download meetkai/functionary-small-v3.2-GGUF functionary-small-v3.2.Q4_0.gguf` -i --special
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What is the weather in Phoenix?
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Sure, I'll look that up for you. Let me just check the current weather conditions in Phoenix.>>>get_weather
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{"location": "Phoenix"}<|eot_id|>
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{"temperature": "30C"}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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The current weather in Phoenix is 30C.<|eot_id|>
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What is 38484 + 323?
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Sure, let's calculate that.>>>calculate
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{"expression": "38484 + 323"}<|eot_id|>
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{"result": 38807}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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The sum of 38484 and 323 is 38807.<|eot_id|>
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What is 67 feet in meters?
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To convert 67 feet into meters, we use the conversion factor: 1 foot is approximately 0.3048 meters. Let's calculate it.>>>calculate
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{"expression": "67 * 0.3048"}<|eot_id|>
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{"result": 20.4216}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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67 feet is approximately 20.4216 meters.<|eot_id|>
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```
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## Function calling example, hiding details
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```
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./examples/function-calling/llama-cli-function-runner.py -m `huggingface-cli download meetkai/functionary-small-v3.2-GGUF functionary-small-v3.2.Q4_0.gguf` -i
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What is the weather in Phoenix?
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To provide you with the current weather in Phoenix, Arizona, I will need to check the weather data for you. Let me get that information.
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The current weather in Phoenix, Arizona is 30°C. If you have any more questions about weather in other locations, feel free to ask!
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Is it colder in Vegas?
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To determine if the current temperature in Las Vegas is colder than in Phoenix, which is currently 30°C, I will need to check the weather data for Las Vegas. Let's find out.
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The current weather in Las Vegas, Nevada is also 30°C. Therefore, there is no difference in temperature between Phoenix and Las Vegas at the moment. If you have any more questions or need further assistance, please let me know!
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What is 37234 times 39?
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To calculate 37234 times 39, I'll perform the multiplication. Let's do that.
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The result of multiplying 37234 by 39 is 1,452,126. If you have any more calculations or questions, feel free to ask!
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```
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examples/function-calling/function_tool.py
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examples/function-calling/function_tool.py
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# Generate function calling definitions function schemas
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import inspect
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import re
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# Extract OpenAI function calling style definitions from functions
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#
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# Generated with: Create a python function to to generate the OpenAI function calling definition from a given function, getting the description, parameter type and parameter description from the function documentation, assuming the function documentation contains sphynx style parameter descriptions, marked with :param.
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def get_function_tool_json(func):
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typemap = { 'str': 'string' };
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def get_type(s):
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return typemap[s] if s in typemap else s
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function_name = func.__name__
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doc_parts = re.split(r'\n\s*:param[^:]*\s+', func.__doc__.rstrip());
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function_description = doc_parts[0]
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params_doc = [ re.split(r'\:\s*', param_doc, maxsplit=1) for param_doc in doc_parts[1:] ]
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params_doc = { param: desc for param, desc in params_doc }
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function_def = {
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'name': function_name,
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'description': function_description,
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'parameters': { 'type': 'object', 'properties': {}, 'required': [] }
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}
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for param_name, param in inspect.signature(func).parameters.items():
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function_def['parameters']['properties'][param_name] = {
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'type' : get_type(param.annotation.__name__) if param.annotation is not param.empty else '',
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'description': params_doc[param_name] if param_name in params_doc else ''
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}
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function_def['parameters']['required'].append(param_name);
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return function_def
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# Generate function definition schema from function definitions
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#
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# This is from llama-cpp-python, llama_chat_format.py
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def generate_schema_from_functions(functions, namespace="functions") -> str:
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schema = (
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"// Supported function definitions that should be called when necessary.\n"
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)
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schema += f"namespace {namespace} {{\n\n"
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for function in functions:
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function_name = function["name"]
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description = function.get("description", "")
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parameters = function.get("parameters", {})
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required_params = parameters.get("required", [])
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schema += f"// {description}\n"
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schema += f"type {function_name} = (_: {{\n"
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for param_name, param in parameters.get("properties", {}).items():
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param_description = param.get("description", "")
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param_type = param.get("type", "any")
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optional_indicator = "" if param_name in required_params else "?"
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schema += f"// {param_description}\n"
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schema += f"{param_name}{optional_indicator}: {param_type},\n"
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schema += "}) => any;\n\n"
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schema += "}} // namespace {}".format(namespace)
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return schema
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examples/function-calling/functions.py
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examples/function-calling/functions.py
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def calculate(expression: str):
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"""Evaluate a mathematical expression
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:param expression: The mathematical expression to evaluate
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"""
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try:
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result = eval(expression)
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return {"result": result}
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except:
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return {"error": "Invalid expression"}
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def get_weather(location: str):
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"""get the weather of a location
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:param location: where to get weather.
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"""
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return {"temperature": "30C"}
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def _run_python(code):
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allowed_globals = { '__builtins__': None, '_': None }
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allowed_locals = {}
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code = code.splitlines()
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code[-1] = f"_ = {code[-1]}"
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code = '\n'.join(code)
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try:
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exec(code, allowed_globals, allowed_locals)
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except Exception as e:
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return None
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return {'result': allowed_locals.get('_', None)}
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examples/function-calling/llama-cli-function-runner.py
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examples/function-calling/llama-cli-function-runner.py
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#!/usr/bin/env python3
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# function calling using llama-cli
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import subprocess
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import sys
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import select
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import os
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import re
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import json
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import functions
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from function_tool import get_function_tool_json, generate_schema_from_functions
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function_name_list = [ name for name in dir(functions) if not name.startswith('_') ]
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function_lookup = { name: getattr(functions, name) for name in function_name_list }
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tools = [ get_function_tool_json(f) for (n, f) in function_lookup.items() ]
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function_schema = generate_schema_from_functions(tools)
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prompt = """<|start_header_id|>system<|end_header_id|>
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You are capable of executing available function(s) if required.
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Execute function(s) as needed.
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The function calls are not shown in the conversation and should be called covertly to answer questions.
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Ask for the required input to:recipient==all
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Use JSON for function arguments.
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Respond in this format:
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>>>${recipient}
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${content}
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Available functions:
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""" + function_schema + """<|eot_id|><|start_header_id|>system<|end_header_id|>
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When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 60.0 seconds. The drive at '/mnt/data' can be used to save and persist user files.<|eot_id|><|start_header_id|>user<|end_header_id|>
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"""
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def main():
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import argparse
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parser = argparse.ArgumentParser(epilog='For more options: llama-cli --help')
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parser.add_argument('--display-prompt', action=argparse.BooleanOptionalAction, default=False)
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parser.add_argument('--special', action=argparse.BooleanOptionalAction, default=False)
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parser.add_argument('--reverse-prompt', type=str, default='<|start_header_id|>user<|end_header_id|>\n')
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parser.add_argument('--ctx-size', type=int, default=1024)
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args, other_args = parser.parse_known_args()
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if args.display_prompt: print(prompt)
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command = [ './llama-cli', '-i', '-p', prompt, '--reverse-prompt', args.reverse_prompt, '--escape', '--special', '--no-display-prompt', '--log-disable', '--simple-io', '--ctx-size', str(args.ctx_size), *other_args]
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process = subprocess.Popen(
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command,
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stdin=subprocess.PIPE,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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text=True,
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)
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if process.stdout is not None: os.set_blocking(process.stdout.fileno(), False)
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try:
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run_loop(process, args)
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except KeyboardInterrupt:
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print("\nInterrupted by user.")
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finally:
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process.terminate()
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process.wait()
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def run_loop(process, args):
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pbuffer = ''
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skip_output_until_result = False
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while True:
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readable, _, _ = select.select([process.stdout, process.stderr, sys.stdin], [], [])
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for stream in readable:
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if stream == process.stdout:
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pdata = process.stdout.read()
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if not pdata: continue
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pbuffer += pdata
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if(match := re.search(r'>>>([^\n]*)\n(.*)<\|eot_id\|>', pbuffer, re.S)):
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if not args.special:
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pdata = pdata[:match.pos]
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pbuffer = ''
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skip_output_until_result = False
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tool_name = match.group(1)
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tool_args = match.group(2)
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if tool_name == 'python':
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result = functions._run_python(tool_args);
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else:
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try:
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tool_args = json.loads(tool_args)
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result = function_lookup[tool_name](**tool_args)
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except ValueError as e:
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result = {'error': 'unknown'}
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result = json.dumps(result) + '<|eot_id|><|start_header_id|>assistant<|end_header_id|>'
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process.stdin.write(result + '\n')
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process.stdin.flush()
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if(args.special): pdata += '\n' + result
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elif (n := pdata.find('>>>')) >= 0:
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if not args.special:
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pdata = pdata[:n]
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skip_output_until_result = True
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elif skip_output_until_result:
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pdata = ''
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if not args.special:
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pdata = re.sub(r'<\|[^\|>]*\|>', '', pdata)
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sys.stdout.write(pdata)
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sys.stdout.flush()
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elif stream == sys.stdin:
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user_input = sys.stdin.readline()
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if user_input:
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user_input = user_input.rstrip()
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process.stdin.write(user_input + '<|eot_id|><|start_header_id|>assistant<|end_header_id|>' + '\n')
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process.stdin.flush()
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if __name__ == '__main__':
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main()
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