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import sys
sys.path.append('../../lutil/')
from aimakerspace.openai_utils.prompts import (
UserRolePrompt,
SystemRolePrompt,
AssistantRolePrompt,
)
from aimakerspace.openai_utils.chatmodel import ChatOpenAI
from aimakerspace.vectordatabase import VectorDatabase
import datetime
from wandb.sdk.data_types.trace_tree import Trace
from wandb import init as winit
RAQA_PROMPT_TEMPLATE = """
Use the provided context to answer the user's query.
You may not answer the user's query unless there is specific context in the following text.
If you do not know the answer, or cannot answer, please respond with "I don't know".
Context:
{context}
"""
raqa_prompt = SystemRolePrompt(RAQA_PROMPT_TEMPLATE)
USER_PROMPT_TEMPLATE = """
User Query:
{user_query}
"""
user_prompt = UserRolePrompt(USER_PROMPT_TEMPLATE)
class RetrievalAugmentedQAPipeline:
def __init__(self, llm: ChatOpenAI(), vector_db_retriever: VectorDatabase, wandb_project = None) -> None:
self.llm = llm
self.vector_db_retriever = vector_db_retriever
self.wandb_project = wandb_project
winit(project="RAQ in pure python HF")
def run_pipeline(self, user_query: str) -> str:
context_list = self.vector_db_retriever.search_by_text(user_query, k=4)
context_prompt = ""
for context in context_list:
context_prompt += context[0] + "\n"
formatted_system_prompt = raqa_prompt.create_message(context=context_prompt)
formatted_user_prompt = user_prompt.create_message(user_query=user_query)
start_time = datetime.datetime.now().timestamp() * 1000
try:
openai_response = self.llm.run([formatted_system_prompt, formatted_user_prompt], text_only=False)
end_time = datetime.datetime.now().timestamp() * 1000
status = "success"
status_message = (None, )
response_text = openai_response.choices[0].message.content
token_usage = openai_response["usage"].to_dict()
model = openai_response["model"]
except Exception as e:
end_time = datetime.datetime.now().timestamp() * 1000
status = "error"
status_message = str(e)
response_text = ""
token_usage = {}
model = ""
if self.wandb_project:
root_span = Trace(
name="root_span",
kind="llm",
status_code=status,
status_message=status_message,
start_time_ms=start_time,
end_time_ms=end_time,
metadata={
"token_usage" : token_usage,
"model_name" : model
},
inputs= {"system_prompt" : formatted_system_prompt, "user_prompt" : formatted_user_prompt},
outputs= {"response" : response_text}
)
root_span.log(name="openai_trace")
return response_text if response_text else "We ran into an error. Please try again later. Full Error Message: " + status_message |