Upload app.py

app.py

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+import os
+import chromadb
+
+import gradio as gr
+
+from dotenv import load_dotenv
+from openai import OpenAI
+
+from langchain_community.embeddings import AnyscaleEmbeddings
+from langchain_community.vectorstores import Chroma
+
+qna_system_message = """
+You are an assistant to an insurance firm who answers user queries on policy documents.
+User input will have the context required by you to answer user questions.
+This context will begin with the word: ###Context.
+The context contains references to specific portions of a document relevant to the user query.
+
+User questions will begin with the word: ###Question.
+
+Please answer user questions only using the context provided in the input.
+Do not mention anything about the context in your final answer. Your response should only contain the answer to the question.
+
+If the answer is not found in the context, respond "Sorry, I cannot answer your question. Please contact our representative on the hotline 1-800-AWESOMEINSURER".
+"""
+
+qna_user_message_template = """
+###Context
+Here are some documents that are relevant to the question mentioned below.
+{context}
+
+###Question
+{question}
+"""
+
+load_dotenv()
+
+anyscale_api_key = os.environ['ANYSCALE_API_KEY']
+
+client = OpenAI(
+    base_url="https://api.endpoints.anyscale.com/v1",
+    api_key=anyscale_api_key
+)
+ 
+qna_model = 'mlabonne/NeuralHermes-2.5-Mistral-7B'
+
+embedding_model = AnyscaleEmbeddings(
+    client=client,
+    model='thenlper/gte-large'
+)
+
+chromadb_client = chromadb.PersistentClient(path='./policy_db')
+
+vectorstore_persisted = Chroma(
+    client=chromadb_client,
+    collection_name="policy-text",
+    embedding_function=embedding_model
+)
+
+retriever = vectorstore_persisted.as_retriever(
+    search_type='similarity',
+    search_kwargs={'k': 5}
+)
+
+def predict(question):
+
+    relevant_document_chunks = retriever.invoke(question)
+    context_list = [d.page_content for d in relevant_document_chunks]
+    context_for_query = "\n".join(context_list)
+
+    prompt = [
+        {'role':'system', 'content': qna_system_message},
+        {'role': 'user', 'content': qna_user_message_template.format(
+            context=context_for_query,
+            question=question
+        )
+        }
+    ]
+
+    try:
+        response = client.chat.completions.create(
+              model=qna_model,
+              messages=prompt,
+              temperature=0
+        )
+
+        prediction = response.choices[0].message.content.strip()
+    except Exception as e:
+        prediction = f'Sorry, I encountered the following error: \n {e}'
+
+    return prediction
+
+textbox = gr.Textbox(placeholder="Enter your query here", lines=6)
+
+demo = gr.Interface(
+    inputs=textbox, fn=predict, outputs="text",
+    title="AMA your insurance policy document",
+    description="This web API presents an interface to ask questions on contents of your health insurance policy",
+    article="Note that questions that are not relevant to the policy will not be answered.",
+    examples=[["My trip was delayed and I paid 45, how much am I covered for?", ""],
+              ["I just had a baby, is baby food covered?", ""],
+              ["How is the gauze used in my operation covered?", ""]
+             ],
+    concurrency_limit=16
+)
+
+demo.queue()
+demo.launch(auth=("demouser", os.getenv('PASSWD')))