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question_answer_tool.py
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question_answer_tool.py
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import json
import logging
import warnings
from ..common.answer import Answer
from ..common.source_document import SourceDocument
from ..helpers.azure_blob_storage_client import AzureBlobStorageClient
from ..helpers.config.config_helper import ConfigHelper
from ..helpers.env_helper import EnvHelper
from ..helpers.llm_helper import LLMHelper
from ..search.search import Search
from .answering_tool_base import AnsweringToolBase
from openai.types.chat import ChatCompletion
logger = logging.getLogger(__name__)
class QuestionAnswerTool(AnsweringToolBase):
def __init__(self) -> None:
self.name = "QuestionAnswer"
self.env_helper = EnvHelper()
self.llm_helper = LLMHelper()
self.search_handler = Search.get_search_handler(env_helper=self.env_helper)
self.verbose = True
self.config = ConfigHelper.get_active_config_or_default()
@staticmethod
def json_remove_whitespace(obj: str) -> str:
"""
Remove whitespace from a JSON string.
"""
try:
return json.dumps(json.loads(obj), separators=(",", ":"))
except json.JSONDecodeError:
return obj
@staticmethod
def clean_chat_history(chat_history: list[dict]) -> list[dict]:
return [
{
"content": message["content"],
"role": message["role"],
}
for message in chat_history
]
def generate_messages(self, question: str, sources: list[SourceDocument]):
sources_text = "\n\n".join(
[f"[doc{i+1}]: {source.content}" for i, source in enumerate(sources)]
)
return [
{
"content": self.config.prompts.answering_user_prompt.format(
question=question, sources=sources_text
),
"role": "user",
},
]
def generate_on_your_data_messages(
self,
question: str,
chat_history: list[dict],
sources: list[SourceDocument],
image_urls: list[str] = [],
) -> list[dict]:
examples = []
few_shot_example = {
"sources": self.config.example.documents.strip(),
"question": self.config.example.user_question.strip(),
"answer": self.config.example.answer.strip(),
}
if few_shot_example["sources"]:
few_shot_example["sources"] = QuestionAnswerTool.json_remove_whitespace(
few_shot_example["sources"]
)
if any(few_shot_example.values()):
if all((few_shot_example.values())):
examples.append(
{
"content": self.config.prompts.answering_user_prompt.format(
sources=few_shot_example["sources"],
question=few_shot_example["question"],
),
"name": "example_user",
"role": "system",
}
)
examples.append(
{
"content": few_shot_example["answer"],
"name": "example_assistant",
"role": "system",
}
)
else:
warnings.warn(
"Not all example fields are set in the config. Skipping few-shot example."
)
documents = json.dumps(
{
"retrieved_documents": [
{f"[doc{i+1}]": {"content": source.content}}
for i, source in enumerate(sources)
],
},
separators=(",", ":"),
)
return [
{
"content": self.config.prompts.answering_system_prompt,
"role": "system",
},
*examples,
{
"content": self.env_helper.AZURE_OPENAI_SYSTEM_MESSAGE,
"role": "system",
},
*QuestionAnswerTool.clean_chat_history(chat_history),
{
"content": [
{
"type": "text",
"text": self.config.prompts.answering_user_prompt.format(
sources=documents,
question=question,
),
},
*(
[
{
"type": "image_url",
"image_url": image_url,
}
for image_url in image_urls
]
),
],
"role": "user",
},
]
def answer_question(self, question: str, chat_history: list[dict], **kwargs):
source_documents = Search.get_source_documents(self.search_handler, question)
if self.env_helper.USE_ADVANCED_IMAGE_PROCESSING:
image_urls = self.create_image_url_list(source_documents)
else:
image_urls = []
model = self.env_helper.AZURE_OPENAI_VISION_MODEL if image_urls else None
if self.config.prompts.use_on_your_data_format:
messages = self.generate_on_your_data_messages(
question, chat_history, source_documents, image_urls
)
else:
warnings.warn(
"Azure OpenAI On Your Data prompt format is recommended and should be enabled in the Admin app.",
)
messages = self.generate_messages(question, source_documents)
llm_helper = LLMHelper()
response = llm_helper.get_chat_completion(messages, model=model, temperature=0)
clean_answer = self.format_answer_from_response(
response, question, source_documents
)
return clean_answer
def create_image_url_list(self, source_documents):
image_types = self.config.get_advanced_image_processing_image_types()
blob_client = AzureBlobStorageClient()
container_sas = blob_client.get_container_sas()
image_urls = [
doc.source.replace("_SAS_TOKEN_PLACEHOLDER_", container_sas)
for doc in source_documents
if doc.title is not None and doc.title.split(".")[-1] in image_types
]
return image_urls
def format_answer_from_response(
self,
response: ChatCompletion,
question: str,
source_documents: list[SourceDocument],
):
answer = response.choices[0].message.content
logger.debug(f"Answer: {answer}")
# Generate Answer Object
clean_answer = Answer(
question=question,
answer=answer,
source_documents=source_documents,
prompt_tokens=response.usage.prompt_tokens,
completion_tokens=response.usage.completion_tokens,
)
return clean_answer