Create chat_interface.py

chat_interface.py

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+import os
+import uuid
+import json
+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
+
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+
+
+load_dotenv()
+
+tesla_10k_collection = 'tesla-10k-2019-to-2023'
+
+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 = 'meta-llama/Meta-Llama-3-8B-Instruct'
+
+embedding_model = AnyscaleEmbeddings(
+    client=client,
+    model='thenlper/gte-large'
+)
+
+chromadb_client = chromadb.PersistentClient(path='./tesla_db')
+
+vectorstore_persisted = Chroma(
+    client=chromadb_client,
+    collection_name=tesla_10k_collection,
+    embedding_function=embedding_model
+)
+
+retriever = vectorstore_persisted.as_retriever(
+    search_type='similarity',
+    search_kwargs={'k': 5}
+)
+
+# Prepare the logging functionality
+
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
+
+scheduler = CommitScheduler(
+    repo_id="document-qna-chroma-anyscale-logs",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+qna_system_message = """
+You are an assistant to a financial services firm who answers user queries on annual reports.
+Users will ask questions delimited by triple backticks, that is, ```.
+User input will have the context required by you to answer user questions.
+This context will begin with the token: ###Context.
+The context contains references to specific portions of a document relevant to the user query.
+Please answer only using the context provided in the input. However, do not mention anything about the context in your answer. 
+If the answer is not found in the context, respond "I don't know".
+"""
+
+qna_user_message_template = """
+###Context
+Here are some documents that are relevant to the question.
+{context}
+```
+{question}
+```
+"""
+
+def predict(input: str, history):
+
+    """
+    Predict the response of the chatbot and complete a running list of chat history.
+    """
+
+    relevant_document_chunks = retriever.invoke(input)
+    context_list = [d.page_content for d in relevant_document_chunks]
+    context_for_query = "\n".join(context_list)
+
+    user_message = [{
+        'role': 'user', 
+        'content': qna_user_message_template.format(
+            context=context_for_query,
+            question=input
+        )
+    }]
+
+    prompt = [{'role':'system', 'content': qna_system_message}]
+
+    for entry in history:
+        prompt += (
+            [{'role': 'user', 'content': entry[0]}] +
+            [{'role': 'assistant', 'content': entry[1]}]
+        )
+    
+    final_prompt = prompt + user_message
+
+    try:
+        
+        response = client.chat.completions.create(
+              model=qna_model,
+              messages=final_prompt,
+              temperature=0
+        )
+
+        prediction = response.choices[0].message.content.strip()
+    except Exception as e:
+        prediction = f"Sorry, I cannot answer your question at this point. {e}"
+
+    # While the prediction is made, log both the inputs and outputs to a local log file
+    # While writing to the log file, ensure that the commit scheduler is locked to avoid parallel
+    # access
+    
+    with scheduler.lock:
+        with log_file.open("a") as f:
+            f.write(json.dumps(
+                {
+                    'user_input': user_input,
+                    'retrieved_context': context_for_query,
+                    'model_response': prediction
+                }
+            ))
+            f.write("\n")
+
+    return prediction
+
+demo = gr.ChatInterface(
+    fn=predict,
+    title="AMA on Tesla 10-K statements",
+    description="This web API presents an interface to ask questions on contents of the Tesla 10-K reports for the period 2019 - 2023.",
+    article="Note that questions that are not relevant to the Tesla 10-K report will not be answered.",
+    examples=[["What was the total revenue of the company in 2022?"],
+              ["Summarize the Management Discussion and Analysis section of the 2021 report in 50 words."],
+              ["What was the company's debt level in 2020?"],
+              ["Identify 5 key risks identified in the 2019 10k report? Respond with bullet point summaries."],
+              ["What is the view of the management on the future of electric vehicle batteries?"]
+             ],
+    cache_examples=False,
+    concurrency_limit=8,
+    show_progress="full"
+)
+
+demo.launch(auth=("demouser", os.getenv('PASSWD')))