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from pydantic import BaseModel
BaseModel.Config.arbitrary_types_allowed = True
from haystack.document_stores import InMemoryDocumentStore
from haystack.nodes import PromptNode, PromptTemplate, AnswerParser, BM25Retriever
from haystack.pipelines import Pipeline
from haystack.utils import print_answers
import chainlit as cl
from datasets import load_dataset
import os
from dotenv import load_dotenv
#load the dataset
dataset = load_dataset("PKU-Alignment/BeaverTails", split="330k_train")
document_store = InMemoryDocumentStore(use_bm25=True)
#Extract the child abuse prompt in the dataset
child_abuse = [entry['prompt'] for entry in dataset if entry['category']['child_abuse']]
for prompt in child_abuse:
print(prompt)
# Print the filtered cases
for case in child_abuse:
print(case)
prompts_list = []
for prompt in child_abuse:
prompts_list.append(prompt)
from haystack import Document
# Convert each prompt into a Document object
documents = [Document(content=prompt) for prompt in prompts_list]
# Write the documents to the document store
document_store.write_documents(documents)
#initialise the retriever
retriever = BM25Retriever(document_store=document_store, top_k=3)
prompt_template = PromptTemplate(
prompt = """
Answer the question truthfully based solely on the given documents. If the documents do not contain the answer,
say that answering is not possible given the available information.
Documents: {join(documents)}
Question: {query}
Answer:
""",
output_parser= AnswerParser()
)
prompt_node = PromptNode(
model_name_or_path= "mistralai/Mistral-7B-Instruct-v0.2",
default_prompt_template= prompt_template
)
generative_pipeline = Pipeline()
generative_pipeline.add_node(component=retriever, name='retriever', inputs=["Query"])
generative_pipeline.add_node(component=prompt_node, name="prompt_node", inputs=["retriever"])
@cl.on_message
async def main(message: str):
response = await cl.make_async(generative_pipeline.run(message))
sentences = response["answers"][0].answer.split("\n")
if sentences and not sentences[-1].strip().endswith(('.','?','!')):
sentences.pop()
result = "\n".join(sentences[1:])
await cl.Message(author="Bot", content= result).send() |