Bases: object
A bot to take in some text and perform multi-label classification on it
init the bot
name: name of the bot
cred: credentials object
platform: name of the platform backend to use
default to openai platform for now
will be extended in the future to suuport other platforms
Source code in llmsdk/agents/mltclassifier.py
| def __init__(self,
name,
cred={},
platform="openai"):
"""
init the bot
name: name of the bot
cred: credentials object
platform: name of the platform backend to use
default to openai platform for now
will be extended in the future to suuport other platforms
"""
start_time = time.time()
# logging
self.logger = get_logger()
# defaults
self.metadata = {}
self.sanity_entries = {}
self.max_llm_tokens = 512
# name
self.agent_name = name
self.agent_type = "query-router"
# creds
self.cred = cred
# LLM params
self.platform = platform
# init the llm and embeddings objects
self.llm = self._get_llm_objs(platform=self.platform,
cred=self.cred)
# note metadata for this agent
self.metadata = {
"agent": {
"name": self.agent_name,
"platform": self.platform,
},
"events": []
}
# log that the agent is ready
duration = time.time() - start_time
event = self._log_event(agent_events._EVNT_READY, duration)
|
get_metadata()
return metadata collected by the agent
Source code in llmsdk/agents/mltclassifier.py
| def get_metadata(self):
"""
return metadata collected by the agent
"""
return self.metadata
|
get_prompt(query, label_spec)
generate a prompt for querying the LLM
Source code in llmsdk/agents/mltclassifier.py
| def get_prompt(self, query, label_spec):
"""
generate a prompt for querying the LLM
"""
# construct the prompt template
defualt_persona = """You are a highly advanced, AI-enabled bot, that performs multi-label text classification"""
# get the bot's persona
persona = label_spec.get("persona", defualt_persona)
# get the bot's specified instruction set
instructions = label_spec.get("instructions", "")
# get the synonymns
syns = []
for term, synonymns in label_spec.get('synonymns', {}).items():
syn = f" - {term}: {', '.join(synonymns)}"
syns.append(syn)
synonymns = "\n".join(syns)
if len(synonymns) > 0:
synonymns = f"""Here is a set of comma-separated synonymns for various phrases that may be asked about:
{synonymns}"""
# get the output format
output_format = label_spec.get('output_format', {})
output_type = output_format.get('type', 'json')
output_sample = output_format.get('sample', None)
if output_sample:
output_format = f"""Always respond by formatting your response as a {output_type} object EXACTLY as follows:
{output_sample}"""
else:
output_format = f"""Always respond by formatting your response as a {output_type} object"""
# construct the system message
sys_msg = "\n\n".join([persona, instructions, synonymns, output_format])
# construct the human message
human_msg = f"""
Here is the question:
------ BEGIN QUESTION ------
{query}
------- END QUESTION -------
Your response:
"""
messages = [
SystemMessage(content=sys_msg),
HumanMessage(content=human_msg),
]
return messages
|
label(query, label_spec={})
run a prompt against the LLM using the routing spec
Source code in llmsdk/agents/mltclassifier.py
| def label(self, query, label_spec={}):
"""
run a prompt against the LLM using the routing spec
"""
start_time = time.time()
success = True
response = None
# construct the prompt to the policy bot
prompt = self.get_prompt(query, label_spec)
# run the query
try:
if self.platform in ['openai', 'azure']:
with get_openai_callback() as cb:
response = self.llm(prompt)
stats = {
"total_tokens": cb.total_tokens,
"prompt_tokens": cb.prompt_tokens,
"completion_tokens": cb.completion_tokens,
"total_cost": round(cb.total_cost, 4)
}
else:
response = self.llm(prompt)
stats = {}
response = json.loads(response.content)
except:
success = False
# log the event
params = {
"query": query,
"mode": "internal",
"result": response.copy() if response is not None else None,
"stats": stats,
}
duration = time.time() - start_time
event = self._log_event(agent_events._EVNT_QUERY, duration, params=params)
return success, response
|