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Diegi97Diegi97
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Add Agent class for streamlined LLM tool interaction
1 parent f2f26a3 commit b94979e

3 files changed

Lines changed: 81 additions & 71 deletions

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‎sdk/chatfaq_sdk/clients/__init__.py‎

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Original file line numberDiff line numberDiff line change
@@ -1,8 +1,6 @@
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from logging import getLogger
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from typing import Callable, Dict, List, Optional, Union
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from pydantic import BaseModel
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from chatfaq_sdk import ChatFAQSDK
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from chatfaq_sdk.types import CacheConfig
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from chatfaq_sdk.utils import function_to_json

‎sdk/chatfaq_sdk/clients/agent.py‎

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import inspect
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from chatfaq_sdk import ChatFAQSDK
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from chatfaq_sdk.clients import llm_request
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from chatfaq_sdk.layers import Message, ToolUse, ToolResult
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from chatfaq_sdk.utils import convert_mml_to_llm_format
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class Agent:
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"""
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An agent that uses the ChatFAQ SDK to interact with an LLM.
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The agent takes a list of tools and a system instruction, and uses them to generate responses to user messages.
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It streams messages, tool uses, and tool results.
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"""
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def __init__(self, sdk: ChatFAQSDK, model_name: str, tools: list, system_instruction: str = None):
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self.sdk = sdk
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self.tools = tools
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self.system_instruction = system_instruction
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self.model_name = model_name
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async def run(self, ctx: dict):
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messages = convert_mml_to_llm_format(ctx["conv_mml"][1:]) # Skip the greeting message
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if self.system_instruction:
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messages.insert(0, {"role": "system", "content": self.system_instruction})
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while True:
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response = await llm_request(
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self.sdk,
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self.model_name,
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use_conversation_context=False,
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conversation_id=ctx["conversation_id"],
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bot_channel_name=ctx["bot_channel_name"],
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messages=messages,
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tools=self.tools,
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tool_choice="auto",
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stream=False,
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)
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tool_results = []
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for content in response["content"]:
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if content["type"] == "text":
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yield Message(content["text"])
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elif content["type"] == "tool_use":
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tool_use = content["tool_use"]
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yield ToolUse(name=tool_use["name"], id=tool_use["id"], args=tool_use["args"])
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# Find the corresponding tool
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tool = next(t for t in self.tools if t.__name__ == tool_use["name"])
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# Execute the tool
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try:
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if inspect.iscoroutinefunction(tool):
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result = await tool(**tool_use["args"])
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else:
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result = tool(**tool_use["args"])
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except Exception as e:
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result = f"Error executing tool {tool_use['name']}: {str(e)}"
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yield ToolResult(id=tool_use["id"], name=tool_use["name"], result=result)
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tool_results.append({
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"id": tool_use["id"],
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"name": tool_use["name"],
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"result": result
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})
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if not tool_results:
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break
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# Append assistant and user messages for the next iteration
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messages.append({
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"role": "assistant",
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"content": response["content"]
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})
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messages.append({
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"role": "user",
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"content": [{"type": "tool_result", "tool_result": tr} for tr in tool_results]
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})

‎sdk/examples/agent_example/fsm_definition.py‎

Lines changed: 9 additions & 69 deletions
Original file line numberDiff line numberDiff line change
@@ -1,9 +1,7 @@
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from chatfaq_sdk import ChatFAQSDK
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from chatfaq_sdk.clients import llm_request
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from chatfaq_sdk.clients.agent import Agent
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from chatfaq_sdk.fsm import FSMDefinition, State, Transition
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from chatfaq_sdk.layers import Message, StreamingMessage, ToolUse, ToolResult
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from chatfaq_sdk.utils import convert_mml_to_llm_format
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from chatfaq_sdk.layers import Message
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MODEL_NAME = "gemini-2.0-flash"
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@@ -21,77 +19,19 @@ async def send_greeting(sdk: ChatFAQSDK, ctx: dict):
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async def send_answer(sdk: ChatFAQSDK, ctx: dict):
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messages = convert_mml_to_llm_format(ctx["conv_mml"][1:]) # skip the greeting message and get the user message
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messages.insert(0, {"role": "system", "content": "You are a knowledgeable weather assistant. Use provided tools when necessary."})
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response = await llm_request(
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sdk,
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MODEL_NAME,
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use_conversation_context=False,
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conversation_id=ctx["conversation_id"],
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bot_channel_name=ctx["bot_channel_name"],
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messages=messages,
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agent = Agent(
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sdk=sdk,
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tools=[get_weather],
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tool_choice="auto",
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stream=False,
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system_instruction="You are a knowledgeable weather assistant. Use provided tools when necessary."
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)
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print(response)
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tool_results = []
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for content in response["content"]:
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if content["type"] == "text":
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yield Message(content["text"])
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elif content["type"] == "tool_use":
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yield ToolUse(name=content["tool_use"]["name"], id=content["tool_use"]["id"], args=content["tool_use"]["args"])
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if content["tool_use"]["name"] == "get_weather":
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result = get_weather(content["tool_use"]["args"]["location"])
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yield ToolResult(id=content["tool_use"]["id"], name=content["tool_use"]["name"], result=result)
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tool_results.append(
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{
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"id": content["tool_use"]["id"],
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"name": content["tool_use"]["name"],
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"result": result
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}
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)
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if tool_results:
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# If there are tool results it means that the model has called a tool
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# so we need to append the tool results to the messages and ask the model to continue
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messages.append({
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"role": "assistant",
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"content": response["content"]
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})
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messages.append({
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"role": "user",
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"content": [{"type": "tool_result", "tool_result": tool_result} for tool_result in tool_results]
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})
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response = await llm_request(
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sdk,
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MODEL_NAME,
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messages=messages,
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use_conversation_context=False,
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conversation_id=ctx["conversation_id"],
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bot_channel_name=ctx["bot_channel_name"],
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tools=[get_weather],
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tool_choice="auto",
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stream=False,
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)
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print(response)
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yield Message(response["content"][0]["text"])
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async for item in agent.run(ctx):
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yield item
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greeting_state = State(name="Greeting", events=[send_greeting], initial=True)
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answering_state = State(name="Answering", events=[send_answer])
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87-
answering_state = State(
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name="Answering",
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events=[send_answer],
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)
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_to_answer = Transition(
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dest=answering_state,
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)
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_to_answer = Transition(dest=answering_state)
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fsm_definition = FSMDefinition(
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states=[greeting_state, answering_state],

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