Create app.py

app.py

@@ -0,0 +1,69 @@
+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 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()