feat: add agents/plan_and_execute

src/codeinterpreterapi/agents/__init__.py

@@ -0,0 +1 @@
+

src/codeinterpreterapi/agents/agents.py

@@ -2,14 +2,13 @@
 
 from langchain.agents import AgentExecutor, BaseSingleActionAgent, ConversationalAgent, ConversationalChatAgent
 from langchain.agents.openai_functions_agent.base import OpenAIFunctionsAgent
-from langchain.chat_models.base import BaseChatModel
 from langchain.memory.buffer import ConversationBufferMemory
 from langchain_anthropic import ChatAnthropic
 from langchain_core.prompts.chat import MessagesPlaceholder
-from langchain_experimental.plan_and_execute import PlanAndExecute, load_agent_executor, load_chat_planner
 from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain_openai import AzureChatOpenAI, ChatOpenAI
 
+from codeinterpreterapi.agents.plan_and_execute.agent_executor import load_agent_executor
 from codeinterpreterapi.config import settings
 
 

src/codeinterpreterapi/agents/plan_and_execute/__init__.py

@@ -0,0 +1,2 @@
+# agent_executor.py
+# https://github.com/langchain-ai/langchain/blob/3ee07473821906a29d944866a2ededb41148f234/libs/experimental/langchain_experimental/plan_and_execute/executors/agent_executor.py

src/codeinterpreterapi/agents/plan_and_execute/agent_executor.py

@@ -0,0 +1,74 @@
+from typing import List, Optional
+
+from langchain.agents.agent import AgentExecutor, AgentOutputParser
+from langchain.agents.structured_chat.base import StructuredChatAgent
+from langchain.tools import BaseTool
+from langchain_core.callbacks import BaseCallbackManager
+from langchain_core.language_models import BaseLanguageModel
+from langchain_experimental.plan_and_execute.executors.base import ChainExecutor
+
+from codeinterpreterapi.agents.plan_and_execute.prompts import (
+    FORMAT_INSTRUCTIONS,
+    FORMAT_INSTRUCTIONS_JA,
+    HUMAN_MESSAGE_TEMPLATE,
+    SUFFIX,
+    SUFFIX_JA,
+    TASK_PREFIX,
+    TOOLS_PREFIX,
+    TOOLS_PREFIX_JA,
+)
+
+
+def load_agent_executor(
+    llm: BaseLanguageModel,
+    tools: List[BaseTool],
+    callback_manager: Optional[BaseCallbackManager] = None,
+    output_parser: Optional[AgentOutputParser] = None,
+    verbose: bool = False,
+    include_task_in_prompt: bool = False,
+    is_ja: str = True,
+) -> ChainExecutor:
+    """
+    Load an agent executor.
+
+    Args:
+        llm: BaseLanguageModel
+        tools: List[BaseTool]
+        verbose: bool. Defaults to False.
+        include_task_in_prompt: bool. Defaults to False.
+
+    Returns:
+        ChainExecutor
+    """
+    input_variables = ["previous_steps", "current_step", "agent_scratchpad"]
+
+    # message_template
+    message_template = ""
+    if include_task_in_prompt:
+        input_variables.append("objective")
+        message_template += TASK_PREFIX
+    message_template += HUMAN_MESSAGE_TEMPLATE
+
+    # format_instructions, tools_prefix, suffix
+    if is_ja:
+        format_instructions = FORMAT_INSTRUCTIONS_JA
+        tools_prefix = TOOLS_PREFIX_JA
+        suffix = SUFFIX_JA
+    else:
+        format_instructions = FORMAT_INSTRUCTIONS
+        tools_prefix = TOOLS_PREFIX
+        suffix = SUFFIX
+    agent = StructuredChatAgent.from_llm_and_tools(
+        llm=llm,
+        tools=tools,
+        callback_manager=callback_manager,
+        output_parser=output_parser,
+        prefix=tools_prefix,
+        suffix=suffix,
+        human_message_template=message_template,
+        format_instructions=format_instructions,
+        input_variables=input_variables,
+        # memory_prompts = memory_prompts,
+    )
+    agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=verbose)
+    return ChainExecutor(chain=agent_executor)

src/codeinterpreterapi/agents/plan_and_execute/prompts.py

@@ -0,0 +1,88 @@
+from textwrap import dedent
+
+from langchain_core.prompts import PromptTemplate
+from langchain_experimental.tot.prompts import JSONListOutputParser
+
+HUMAN_MESSAGE_TEMPLATE = """Previous steps: {previous_steps}
+
+Current objective: {current_step}
+
+{agent_scratchpad}"""
+
+TASK_PREFIX = """{objective}
+
+"""
+
+TOOLS_PREFIX = (
+    """Respond to the human as helpfully and accurately as possible. You have access to the following tools:"""
+)
+FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
+
+Valid "action" values: "Final Answer" or {tool_names}
+
+Provide only ONE action per $JSON_BLOB, as shown:
+
+```
+{{{{
+  "action": $TOOL_NAME,
+  "action_input": $INPUT
+}}}}
+```
+
+Follow this format:
+
+Question: input question to answer
+Thought: consider previous and subsequent steps
+Action:
+```
+$JSON_BLOB
+```
+Observation: action result
+... (repeat Thought/Action/Observation N times)
+Thought: I know what to respond
+Action:
+```
+{{{{
+  "action": "Final Answer",
+  "action_input": "Final response to human"
+}}}}
+```"""
+SUFFIX = """Begin! Reminder to ALWAYS respond with a valid json blob of a single action.
+Use tools if necessary. Respond directly if appropriate.
+Format is like this.
+Action:```$JSON_BLOB```
+Observation:
+Thought:"""
+
+TOOLS_PREFIX_JA = """できる限り丁寧かつ正確に人間の質問に答えてください。以下のツールが利用可能です:"""
+
+FORMAT_INSTRUCTIONS_JA = """
+始めましょう！常に単一のアクションの有効なJSONブロブで応答することを忘れないでください。
+必要に応じてツールを使用してください。適切な場合は直接回答してください。
+フォーマットは次のようにしてください。
+Action:```$JSON_BLOB```
+Observation:
+Thought:
+"""
+SUFFIX_JA = """それでは始めましょう。常に単一のアクションの有効なJSON_BLOBで応答してください。
+必要に応じてツールを使用してください。適切な場合は直接回答してください。
+フォーマットは次のようにしてください。
+Action:```$JSON_BLOB```
+Observation:
+Thought:"""
+
+
+def get_tools_prefix_prompt(is_ja: bool) -> str:
+    """Get the prefix prompt for plan_and_execute."""
+    if is_ja:
+        return TOOLS_PREFIX_JA
+    else:
+        return TOOLS_PREFIX
+
+
+def get_plan_and_execute_prompt(is_ja: bool) -> str:
+    """Get the main prompt for plan_and_execute."""
+    if is_ja:
+        return FORMAT_INSTRUCTIONS_JA
+    else:
+        return FORMAT_INSTRUCTIONS

src/codeinterpreterapi/planners/planners.py

@@ -26,5 +26,6 @@ class CodeInterpreterPlanner:
     @staticmethod
     def choose_planner(llm: BaseLanguageModel, is_ja: bool) -> LLMPlanner:
         system_prompt = SYSTEM_PROMPT_PLANNER_JA if is_ja else SYSTEM_PROMPT_PLANNER
+        print("system_prompt(planner)=", system_prompt)
         planner = load_chat_planner(llm, system_prompt=system_prompt)
         return planner