support workflow engine in devchat

site-packages/devchat/_cli/prompt.py

@@ -1,6 +1,8 @@
 import json
+import sys
 from typing import List, Optional
 import rich_click as click
+from devchat.engine import run_command
 from devchat.assistant import Assistant
 from devchat.openai.openai_chat import OpenAIChat, OpenAIChatConfig
 from devchat.store import Store
@@ -24,10 +26,15 @@ from devchat._cli.utils import handle_errors, init_dir, get_model_config
               help='Path to a JSON file with functions for the prompt.')
 @click.option('-n', '--function-name',
               help='Specify the function name when the content is the output of a function.')
+@click.option('-ns', '--not-store', is_flag=True, default=False, required=False,
+              help='Do not save the conversation to the store.')
+@click.option('-a', '--auto', is_flag=True, default=False, required=False,
+              help='Answer question by function-calling.')
 def prompt(content: Optional[str], parent: Optional[str], reference: Optional[List[str]],
            instruct: Optional[List[str]], context: Optional[List[str]],
            model: Optional[str], config_str: Optional[str] = None,
-           functions: Optional[str] = None, function_name: Optional[str] = None):
+           functions: Optional[str] = None, function_name: Optional[str] = None,
+           not_store: Optional[bool] = False, auto: Optional[bool] = False):
     """
     This command performs interactions with the specified large language model (LLM)
     by sending prompts and receiving responses.
@@ -82,9 +89,9 @@ def prompt(content: Optional[str], parent: Optional[str], reference: Optional[Li
         openai_config = OpenAIChatConfig(model=model, **parameters_data)
 
         chat = OpenAIChat(openai_config)
-        store = Store(repo_chat_dir, chat)
+        chat_store = Store(repo_chat_dir, chat)
 
-        assistant = Assistant(chat, store, config.max_input_tokens)
+        assistant = Assistant(chat, chat_store, config.max_input_tokens, not not_store)
 
         functions_data = None
         if functions is not None:
@@ -94,5 +101,17 @@ def prompt(content: Optional[str], parent: Optional[str], reference: Optional[Li
                               parent=parent, references=reference,
                               function_name=function_name)
 
+        click.echo(assistant.prompt.formatted_header())
+        command_result = run_command(
+            model,
+            assistant.prompt.messages,
+            content,
+            parent,
+            context_contents,
+            auto)
+        if command_result is not None:
+            sys.exit(command_result[0])
+
         for response in assistant.iterate_response():
             click.echo(response, nl=False)
+    sys.exit(0)

site-packages/devchat/assistant.py

@@ -4,6 +4,7 @@ from typing import Optional, List, Iterator
 import openai
 from devchat.message import Message
 from devchat.chat import Chat
+from devchat.openai.openai_prompt import OpenAIPrompt
 from devchat.store import Store
 from devchat.utils import get_logger
 
@@ -12,7 +13,7 @@ logger = get_logger(__name__)
 
 
 class Assistant:
-    def __init__(self, chat: Chat, store: Store, max_prompt_tokens: int):
+    def __init__(self, chat: Chat, store: Store, max_prompt_tokens: int, need_store: bool):
         """
         Initializes an Assistant object.
 
@@ -23,6 +24,11 @@ class Assistant:
         self._store = store
         self._prompt = None
         self.token_limit = max_prompt_tokens
+        self._need_store = need_store
+
+    @property
+    def prompt(self) -> OpenAIPrompt:
+        return self._prompt
 
     @property
     def available_tokens(self) -> int:
@@ -92,7 +98,6 @@ class Assistant:
             Iterator[str]: An iterator over response strings from the chat API.
         """
         if self._chat.config.stream:
-            first_chunk = True
             created_time = int(time.time())
             config_params = self._chat.config.dict(exclude_unset=True)
             for chunk in self._chat.stream_response(self._prompt):
@@ -114,12 +119,10 @@ class Assistant:
                     chunk['choices'][0]['delta']['role']='assistant'
 
                 delta = self._prompt.append_response(json.dumps(chunk))
-                if first_chunk:
-                    first_chunk = False
-                    yield self._prompt.formatted_header()
                 yield delta
             if not self._prompt.responses:
                 raise RuntimeError("No responses returned from the chat API")
+            if self._need_store:
                 self._store.store_prompt(self._prompt)
                 yield self._prompt.formatted_footer(0) + '\n'
             for index in range(1, len(self._prompt.responses)):
@@ -129,6 +132,7 @@ class Assistant:
             self._prompt.set_response(response_str)
             if not self._prompt.responses:
                 raise RuntimeError("No responses returned from the chat API")
+            if self._need_store:
                 self._store.store_prompt(self._prompt)
             for index in range(len(self._prompt.responses)):
                 yield self._prompt.formatted_full_response(index) + '\n'

site-packages/devchat/engine/__init__.py

@@ -1,11 +1,13 @@
 from .command_parser import parse_command, Command, CommandParser
 from .namespace import Namespace
 from .recursive_prompter import RecursivePrompter
+from .router import run_command
 
 __all__ = [
     'parse_command',
     'Command',
     'CommandParser',
     'Namespace',
-    'RecursivePrompter'
+    'RecursivePrompter',
+    'run_command'
 ]

site-packages/devchat/engine/command_runner.py

@@ -0,0 +1,198 @@
+"""
+Run Command with a input text.
+"""
+import os
+import sys
+import json
+import threading
+import subprocess
+from typing import List
+import shlex
+
+import openai
+
+from devchat.utils import get_logger
+from .command_parser import Command
+
+
+logger = get_logger(__name__)
+
+
+# Equivalent of CommandRun in Python\which executes subprocesses
+class CommandRunner:
+    def __init__(self, model_name: str):
+        self.process = None
+        self._model_name = model_name
+
+    def _call_function_by_llm(self,
+                           command_name: str,
+                           command: Command,
+                           history_messages: List[dict]):
+        """
+        command needs multi parameters, so we need parse each
+        parameter by LLM from input_text
+        """
+        properties = {}
+        required = []
+        for key, value in command.parameters.items():
+            properties[key] = {}
+            for key1, value1 in value.dict().items():
+                if key1 not in ['type', 'description', 'enum'] or value1 is None:
+                    continue
+                properties[key][key1] = value1
+            required.append(key)
+
+        tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": command_name,
+                    "description": command.description,
+                    "parameters": {
+                        "type": "object",
+                        "properties": properties,
+                        "required": required,
+                    },
+                }
+            }
+        ]
+
+        client = openai.OpenAI(
+            api_key=os.environ.get("OPENAI_API_KEY", None),
+            base_url=os.environ.get("OPENAI_API_BASE", None)
+        )
+
+        connection_error = ''
+        for _1 in range(3):
+            try:
+                response = client.chat.completions.create(
+                    messages=history_messages,
+                    model="gpt-3.5-turbo-16k",
+                    stream=False,
+                    tools=tools,
+                    tool_choice={"type": "function", "function": {"name": command_name}}
+                )
+
+                respose_message = response.dict()["choices"][0]["message"]
+                if not respose_message['tool_calls']:
+                    return None
+                tool_call = respose_message['tool_calls'][0]['function']
+                if tool_call['name'] != command_name:
+                    return None
+                parameters = json.loads(tool_call['arguments'])
+                return parameters
+            except (ConnectionError, openai.APIConnectionError) as err:
+                connection_error = err
+                continue
+            except Exception as err:
+                print("Exception:", err, file=sys.stderr, flush=True)
+                logger.exception("Call command by LLM error: %s", err)
+                return None
+        print("Connect Error:", connection_error, file=sys.stderr, flush=True)
+        return None
+
+
+    def run_command(self,
+                    command_name: str,
+                    command: Command,
+                    history_messages: List[dict],
+                    input_text: str,
+                    parent_hash: str,
+                    context_contents: List[str]):
+        """
+        if command has parameters, then generate command parameters from input by LLM
+        if command.input is "required", and input is null, then return error
+        """
+        if command.parameters and len(command.parameters) > 0:
+            if not self._model_name.startswith("gpt-"):
+                return None
+
+            arguments = self._call_function_by_llm(command_name, command, history_messages)
+            if not arguments:
+                print("No valid parameters generated by LLM", file=sys.stderr, flush=True)
+                return (-1, "")
+            return self.run_command_with_parameters(
+                command,
+                {
+                    "input": input_text.strip().replace(f'/{command_name}', ''),
+                    **arguments
+                },
+                parent_hash,
+                context_contents)
+
+        return self.run_command_with_parameters(
+            command,
+            {
+                "input": input_text.strip().replace(f'/{command_name}', '')
+            },
+            parent_hash,
+            context_contents)
+
+
+    def run_command_with_parameters(self,
+                                 command: Command,
+                                 parameters: dict[str, str],
+                                 parent_hash: str,
+                                 context_contents: List[str]):
+        """
+        replace $xxx in command.steps[0].run with parameters[xxx]
+        then run command.steps[0].run
+        """
+        def pipe_reader(pipe, out_data, out_flag):
+            while pipe:
+                data = pipe.read(1)
+                if data == '':
+                    break
+                out_data['out'] += data
+                print(data, end='', file=out_flag, flush=True)
+
+        try:
+            # add environment variables to parameters
+            if parent_hash:
+                os.environ['PARENT_HASH'] = parent_hash
+            if context_contents:
+                os.environ['CONTEXT_CONTENTS'] = json.dumps(context_contents)
+            for env_var in os.environ:
+                parameters[env_var] = os.environ[env_var]
+            parameters["command_python"] = os.environ['command_python']
+
+            command_run = command.steps[0]["run"]
+            # Replace parameters in command run
+            for parameter in parameters:
+                command_run = command_run.replace('$' + parameter, str(parameters[parameter]))
+
+            # Run command_run
+            env = os.environ.copy()
+            if 'PYTHONPATH' in env:
+                del env['PYTHONPATH']
+            # result = subprocess.run(command_run, shell=True, env=env)
+            # return result
+            with subprocess.Popen(
+                        shlex.split(command_run),
+                        stdout=subprocess.PIPE,
+                        stderr=subprocess.STDOUT,
+                        env=env,
+                        text=True
+                    ) as process:
+
+                stdout_data = {'out': ''}
+                stderr_data = {'out': ''}
+
+                stdout_thread = threading.Thread(
+                    target=pipe_reader,
+                    args=(process.stdout, stdout_data, sys.stdout))
+                stderr_thread = threading.Thread(
+                    target=pipe_reader,
+                    args=(process.stderr, stderr_data, sys.stderr))
+
+                stdout_thread.start()
+                stderr_thread.start()
+
+                stdout_thread.join()
+                stderr_thread.join()
+                exit_code = process.wait()
+                return (exit_code, stdout_data["out"])
+            return (-1, "")
+        except Exception as err:
+            print("Exception:", type(err), err, file=sys.stderr, flush=True)
+            return (-1, "")

site-packages/devchat/engine/router.py

@@ -0,0 +1,237 @@
+import os
+import json
+from typing import List, Iterable
+import openai
+from devchat._cli.utils import init_dir
+from .namespace import Namespace
+from .command_parser import CommandParser, Command
+from .command_runner import CommandRunner
+
+
+def _load_command(command: str):
+    _, user_chat_dir = init_dir()
+    workflows_dir = os.path.join(user_chat_dir, 'workflows')
+    if not os.path.exists(workflows_dir):
+        return None
+    if not os.path.isdir(workflows_dir):
+        return None
+
+    namespace = Namespace(workflows_dir)
+    commander = CommandParser(namespace)
+
+    cmd = commander.parse(command)
+    if not cmd:
+        return None
+    return cmd
+
+
+def _load_commands() -> List[Command]:
+    _, user_chat_dir = init_dir()
+    workflows_dir = os.path.join(user_chat_dir, 'workflows')
+    if not os.path.exists(workflows_dir):
+        return None
+    if not os.path.isdir(workflows_dir):
+        return None
+
+    namespace = Namespace(workflows_dir)
+    commander = CommandParser(namespace)
+    command_names = namespace.list_names("", True)
+
+    commands = []
+    for name in command_names:
+        cmd = commander.parse(name)
+        if not cmd:
+            continue
+        commands.append((name, cmd))
+
+    return commands
+
+
+def _create_tool(command_name:str, command: Command) -> dict:
+    properties = {}
+    required = []
+    if command.parameters:
+        for key, value in command.parameters.items():
+            properties[key] = {}
+            for key1, value1 in value.dict().items():
+                if key1 not in ['type', 'description', 'enum'] or value1 is None:
+                    continue
+                properties[key][key1] = value1
+            required.append(key)
+    elif command.steps[0]['run'].find('$input') > 0:
+        properties['input'] = {
+            "type": "string",
+            "description": "input text"
+        }
+        required.append('input')
+
+    return {
+        "type": "function",
+        "function": {
+            "name": command_name,
+            "description": command.description,
+            "parameters": {
+                "type": "object",
+                "properties": properties,
+                "required": required,
+            },
+        }
+    }
+
+
+def _create_tools() -> List[dict]:
+    commands = _load_commands()
+    return [_create_tool(command[0], command[1]) for command in commands if command[1].steps]
+
+
+def _call_gpt(messages: List[dict],  # messages passed to GPT
+              model_name: str,       # GPT model name
+              use_function_calling: bool) -> dict: # whether to use function calling
+    client = openai.OpenAI(
+        api_key=os.environ.get("OPENAI_API_KEY", None),
+        base_url=os.environ.get("OPENAI_API_BASE", None)
+    )
+
+    tools = [] if not use_function_calling else _create_tools()
+
+    for try_times in range(3):
+        try:
+            response: Iterable = client.chat.completions.create(
+                messages=messages,
+                model=model_name,
+                stream=True,
+                tools=tools
+            )
+
+            response_result = {'content': None, 'function_name': None, 'parameters': ""}
+            for chunk in response: # pylint: disable=E1133
+                chunk = chunk.dict()
+                delta = chunk["choices"][0]["delta"]
+                if 'tool_calls' in delta and delta['tool_calls']:
+                    tool_call = delta['tool_calls'][0]['function']
+                    if tool_call.get('name', None):
+                        response_result["function_name"] = tool_call["name"]
+                    if tool_call.get("arguments", None):
+                        response_result["parameters"] += tool_call["arguments"]
+                if delta.get('content', None):
+                    if response_result["content"]:
+                        response_result["content"] += delta["content"]
+                    else:
+                        response_result["content"] = delta["content"]
+                    print(delta["content"], end='', flush=True)
+            if response_result["function_name"]:
+                print("``` command_run")
+                function_call = {
+                    'name': response_result["function_name"],
+                    'arguments': response_result["parameters"]}
+                print(json.dumps(function_call, indent=4))
+                print("```", flush=True)
+            return response_result
+        except (ConnectionError, openai.APIConnectionError) as err:
+            if try_times == 2:
+                print("Connect Exception:", err)
+                print(err.strerror)
+                return {'content': None, 'function_name': None, 'parameters': ""}
+            continue
+        except Exception as err:
+            print("Exception Error:", err)
+            return {'content': None, 'function_name': None, 'parameters': ""}
+    return {'content': None, 'function_name': None, 'parameters': ""}
+
+
+def _create_messages():
+    return []
+
+
+def _call_function(function_name: str, parameters: str, model_name: str):
+    """
+    call function by function_name and parameters
+    """
+    parameters = json.loads(parameters)
+    command_obj = _load_command(function_name)
+    runner = CommandRunner(model_name)
+    return runner.run_command_with_parameters(command_obj, parameters, "", [])
+
+
+def _auto_function_calling(history_messages: List[dict], model_name:str):
+    """
+    通过function calling方式来回答当前问题。
+    function最多被调用4次，必须进行最终答复。
+    """
+    function_call_times = 0
+
+    response = _call_gpt(history_messages, model_name, True)
+    while True:
+        if response['function_name']:
+            # run function
+            function_call_times += 1
+            print("do function calling", end='\n\n', flush=True)
+            function_result = _call_function(
+                response['function_name'],
+                response['parameters'],
+                model_name)
+            history_messages.append({
+                'role': 'function',
+                'content': f'exit code: {function_result[0]} stdout: {function_result[1]}',
+                'name': response['function_name']})
+            print("after functon call.", end='\n\n', flush=True)
+
+            # send function result to gpt
+            if function_call_times < 5:
+                response = _call_gpt(history_messages, model_name, True)
+            else:
+                response = _call_gpt(history_messages, model_name, False)
+        else:
+            return response
+
+
+def _auto_route(history_messages, model_name:str):
+    """
+    select which command to run
+    """
+    response = _call_gpt(history_messages, model_name, True)
+    if response['function_name']:
+        return _call_function(
+            response['function_name'],
+            response['parameters'],
+            model_name)
+    if response['content']:
+        return (0, response['content'])
+    return (-1, "")
+
+
+def run_command(
+        model_name: str,
+        history_messages: List[dict],
+        input_text: str,
+        parent_hash: str,
+        context_contents: List[str],
+        auto_fun: bool):
+    """
+    load command config, and then run Command
+    """
+    # split input_text by ' ','\n','\t'
+    if len(input_text.strip()) == 0:
+        return None
+    if input_text.strip()[:1] != '/':
+        if not (auto_fun and model_name.startswith('gpt-')):
+            return None
+
+        # response = _auto_function_calling(history_messages, model_name)
+        # return response['content']
+        return _auto_route(history_messages, model_name)
+    commands = input_text.split()
+    command = commands[0][1:]
+
+    command_obj = _load_command(command)
+    if not command_obj or not command_obj.steps:
+        return None
+
+    runner = CommandRunner(model_name)
+    return runner.run_command(
+        command,
+        command_obj,
+        history_messages,
+        input_text,
+        parent_hash,
+        context_contents)

site-packages/devchat/openai/openai_prompt.py

@@ -239,8 +239,7 @@ class OpenAIPrompt(Prompt):
         if not self._timestamp:
             self._timestamp = response_data['created']
         elif self._timestamp != response_data['created']:
-            raise ValueError(f"Time mismatch: expected {self._timestamp}, "
+            self._timestamp = response_data['created']
-                             f"got {response_data['created']}")
 
     def _id_from_dict(self, response_data: dict):
         if self._id is None:

site-packages/devchat/prompt.py

@@ -1,6 +1,7 @@
 from abc import ABC, abstractmethod
 from dataclasses import dataclass, field, asdict
 import hashlib
+from datetime import datetime
 import sys
 from typing import Dict, List
 from devchat.message import Message
@@ -224,7 +225,7 @@ class Prompt(ABC):
         formatted_str = f"User: {user_id(self.user_name, self.user_email)[0]}\n"
 
         if not self._timestamp:
-            raise ValueError(f"Prompt lacks timestamp for formatting header: {self.request}")
+            self._timestamp = datetime.timestamp(datetime.now())
 
         local_time = unix_to_local_datetime(self._timestamp)
         formatted_str += f"Date: {local_time.strftime('%a %b %d %H:%M:%S %Y %z')}\n\n"
@@ -267,7 +268,7 @@ class Prompt(ABC):
                          index, self.request, self.responses)
             return None
 
-        formatted_str = self.formatted_header()
+        formatted_str = ""
 
         if self.responses[index].content:
             formatted_str += self.responses[index].content