mirror of
https://github.com/trustgraph-ai/trustgraph.git
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285 lines
8.3 KiB
Python
Executable file
285 lines
8.3 KiB
Python
Executable file
"""
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Simple agent infrastructure broadly implements the ReAct flow.
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"""
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import json
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import re
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from pulsar.schema import JsonSchema
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from ... base import ConsumerProducer
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from ... schema import Error
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from ... schema import AgentRequest, AgentResponse, AgentStep
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from ... schema import agent_request_queue, agent_response_queue
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from ... schema import prompt_request_queue as pr_request_queue
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from ... schema import prompt_response_queue as pr_response_queue
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from ... schema import text_completion_request_queue as tc_request_queue
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from ... schema import text_completion_response_queue as tc_response_queue
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from ... schema import graph_rag_request_queue as gr_request_queue
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from ... schema import graph_rag_response_queue as gr_response_queue
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from ... clients.prompt_client import PromptClient
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from ... clients.llm_client import LlmClient
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from ... clients.graph_rag_client import GraphRagClient
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from . tools import CatsKb, ShuttleKb, Compute
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from . agent_manager import AgentManager
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from . types import Final, Action
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module = ".".join(__name__.split(".")[1:-1])
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default_input_queue = agent_request_queue
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default_output_queue = agent_response_queue
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default_subscriber = module
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class Processor(ConsumerProducer):
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def __init__(self, **params):
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input_queue = params.get("input_queue", default_input_queue)
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output_queue = params.get("output_queue", default_output_queue)
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subscriber = params.get("subscriber", default_subscriber)
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prompt_request_queue = params.get(
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"prompt_request_queue", pr_request_queue
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)
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prompt_response_queue = params.get(
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"prompt_response_queue", pr_response_queue
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)
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text_completion_request_queue = params.get(
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"text_completion_request_queue", tc_request_queue
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)
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text_completion_response_queue = params.get(
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"text_completion_response_queue", tc_response_queue
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)
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graph_rag_request_queue = params.get(
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"graph_rag_request_queue", gr_request_queue
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)
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graph_rag_response_queue = params.get(
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"graph_rag_response_queue", gr_response_queue
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)
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super(Processor, self).__init__(
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**params | {
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"input_queue": input_queue,
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"output_queue": output_queue,
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"subscriber": subscriber,
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"input_schema": AgentRequest,
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"output_schema": AgentResponse,
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"prompt_request_queue": prompt_request_queue,
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"prompt_response_queue": prompt_response_queue,
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"text_completion_request_queue": tc_request_queue,
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"text_completion_response_queue": tc_response_queue,
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"graph_rag_request_queue": gr_request_queue,
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"graph_rag_response_queue": gr_response_queue,
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}
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)
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self.prompt = PromptClient(
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subscriber=subscriber,
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input_queue=prompt_request_queue,
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output_queue=prompt_response_queue,
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pulsar_host = self.pulsar_host
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)
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self.llm = LlmClient(
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subscriber=subscriber,
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input_queue=text_completion_request_queue,
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output_queue=text_completion_response_queue,
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pulsar_host = self.pulsar_host
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)
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self.graph_rag = GraphRagClient(
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subscriber=subscriber,
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input_queue=graph_rag_request_queue,
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output_queue=graph_rag_response_queue,
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pulsar_host = self.pulsar_host
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)
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# Need to be able to feed requests to myself
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self.recursive_input = self.client.create_producer(
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topic=input_queue,
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schema=JsonSchema(AgentRequest),
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)
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tools = [
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CatsKb(self), ShuttleKb(self), Compute(self),
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]
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self.agent = AgentManager(self, tools)
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def parse_json(self, text):
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json_match = re.search(r'```(?:json)?(.*?)```', text, re.DOTALL)
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if json_match:
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json_str = json_match.group(1).strip()
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else:
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# If no delimiters, assume the entire output is JSON
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json_str = text.strip()
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return json.loads(json_str)
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def handle(self, msg):
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try:
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v = msg.value()
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# Sender-produced ID
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id = msg.properties()["id"]
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if v.history:
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history = [
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Action(
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thought=h.thought,
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name=h.action,
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arguments=h.arguments,
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observation=h.observation
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)
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for h in v.history
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]
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else:
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history = []
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print(v.question)
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if len(history) > 10:
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raise RuntimeError("Too many agent iterations")
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print("History:", history)
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def think(x):
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print("THINK:", x)
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r = AgentResponse(
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answer=None,
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error=None,
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thought=x,
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observation=None,
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)
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self.producer.send(r, properties={"id": id})
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def observe(x):
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print("OBSERVE:", x)
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r = AgentResponse(
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answer=None,
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error=None,
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thought=None,
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observation=x,
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)
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self.producer.send(r, properties={"id": id})
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act = self.agent.react(v.question, history, think, observe)
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print(act)
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print("Send response...", flush=True)
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if type(act) == Final:
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print("Send response...", flush=True)
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r = AgentResponse(
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answer=act.final,
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error=None,
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thought=None,
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)
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self.producer.send(r, properties={"id": id})
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print("Done.", flush=True)
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return
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history.append(act)
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print(history)
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r = AgentRequest(
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question=v.question,
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plan=v.plan,
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state=v.state,
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history=[
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AgentStep(
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thought=h.thought,
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action=h.name,
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arguments=h.arguments,
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observation=h.observation
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)
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for h in history
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]
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)
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self.recursive_input.send(r, properties={"id": id})
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print("Done.", flush=True)
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return
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except Exception as e:
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print(f"Exception: {e}")
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print("Send error response...", flush=True)
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r = AgentResponse(
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error=Error(
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type = "agent-error",
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message = str(e),
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),
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response=None,
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)
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self.producer.send(r, properties={"id": id})
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@staticmethod
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def add_args(parser):
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ConsumerProducer.add_args(
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parser, default_input_queue, default_subscriber,
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default_output_queue,
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)
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parser.add_argument(
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'--prompt-request-queue',
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default=pr_request_queue,
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help=f'Prompt request queue (default: {pr_request_queue})',
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)
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parser.add_argument(
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'--prompt-response-queue',
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default=pr_response_queue,
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help=f'Prompt response queue (default: {pr_response_queue})',
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)
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parser.add_argument(
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'--text-completion-request-queue',
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default=tc_request_queue,
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help=f'Text completion request queue (default: {tc_request_queue})',
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)
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parser.add_argument(
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'--text-completion-response-queue',
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default=tc_response_queue,
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help=f'Text completion response queue (default: {tc_response_queue})',
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)
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parser.add_argument(
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'--graph-rag-request-queue',
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default=gr_request_queue,
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help=f'Graph RAG request queue (default: {gr_request_queue})',
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)
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parser.add_argument(
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'--graph-rag-response-queue',
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default=gr_response_queue,
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help=f'Graph RAG response queue (default: {gr_response_queue})',
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)
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def run():
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Processor.start(module, __doc__)
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