Create app.py

app.py

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+import os
+import gradio as gr
+
+from dotenv import load_dotenv
+from openai import AzureOpenAI
+
+from langchain_openai import AzureOpenAIEmbeddings
+
+from langchain_community.vectorstores import Chroma
+from langchain.retrievers import ContextualCompressionRetriever
+from langchain.retrievers.document_compressors import CrossEncoderReranker
+from langchain_community.cross_encoders import HuggingFaceCrossEncoder
+
+load_dotenv()
+
+client = AzureOpenAI(
+    api_key=os.environ['AZURE_OPENAI_KEY'],
+    azure_endpoint=os.environ['AZURE_OPENAI_ENDPOINT'],
+    api_version='2024-02-01'
+)
+
+model_name = 'gpt-4o-mini'
+
+embedding_model = AzureOpenAIEmbeddings(
+    api_key=os.environ['AZURE_OPENAI_KEY'],
+    azure_endpoint=os.environ['AZURE_OPENAI_ENDPOINT'],
+    api_version='2024-02-01',
+    azure_deployment="text-embedding-ada-002"
+)
+
+tesla_10k_collection = 'tesla-10k-2021-2023'
+
+vectorstore_persisted = Chroma(
+    collection_name=tesla_10k_collection,
+    persist_directory='./tesla_db',
+    embedding_function=embedding_model
+)
+
+retriever = vectorstore_persisted.as_retriever(
+    search_type='similarity',
+    search_kwargs={'k': 20}
+)
+
+cross_encoder_model = HuggingFaceCrossEncoder(model_name="cross-encoder/ms-marco-MiniLM-L-6-v2")
+compressor = CrossEncoderReranker(model=cross_encoder_model, top_n=5)
+compression_retriever = ContextualCompressionRetriever(
+    base_compressor=compressor, base_retriever=retriever
+)
+
+# RAG Q&A
+
+qna_system_message = """
+You are an expert analyst at a financial services firm who answers user queries on annual reports.
+User input will have the context required by you to answer user questions.
+This context will begin with the word: ###Context.
+The context contains documents relevant to the user query.
+It also contains references to the metadata associated with the relevant documents.
+In sum, the context provided to you will be a combination of information and the metadata for the source of information.  
+
+User questions will begin with the word: ###Question.
+
+Please answer user questions only using the context provided in the input and provide citations.
+Remember, you must return both an answer and citations. A citation consists of a VERBATIM quote that
+justifies the answer and the metadata of the quote article. 
+Return a citation for every quote across all articles that justify the answer. 
+Use the following format for your final output:
+
+<cited_answer>
+    <answer></answer>
+    <citations>
+        <citation><source_doc_year></source_doc_year><source_page></source_page><quote></quote></citation>
+        <citation><source_doc_year></source_doc_year><source_page></source_page><quote></quote></citation>
+        ...
+    </citations>
+</cited_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 mentioned below.
+{context}
+
+###Question
+{question}
+"""
+
+
+def predict(user_input: str):
+
+    relevant_document_chunks = retriever.invoke(user_input)
+
+    relevant_document_chunks = compression_retriever.invoke(user_input)
+
+    context_citation_list = [
+        f'Information: {d.page_content}\nMetadata: {d.metadata}'
+        for d in relevant_document_chunks
+    ]
+
+    context_for_query = "\n---\n".join(context_citation_list)
+
+    prompt = [
+        {'role':'system', 'content': qna_system_message},
+        {'role': 'user', 'content': qna_user_message_template.format(
+            context=context_for_query,
+            question=user_input
+            )
+        }
+    ]
+
+    try:
+        response = client.chat.completions.create(
+            model=model_name,
+            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
+
+
+def parse_prediction(user_input: str):
+
+    answer = predict(user_input)
+
+    final_answer = answer[answer.find('<answer>')+len('<answer>'): answer.find('</answer>')]
+    citations = answer[answer.find('<citations>')+len('<citations>'): answer.find('</citations>')].strip().split('</citations>')
+    references = ''
+
+    for i, citation in enumerate(citations):
+        quote = citation[citation.find('<quote>')+len("<quote>"): citation.find('</quote>')]
+        year = citation[citation.find('<source_doc_year>')+len("<source_doc_year>"): citation.find('</source_doc_year>')]
+        page = citation[citation.find('<source_page>')+len("<source_page>"): citation.find('</source_page>')]
+        references += f'{i+1}. Quote: {quote}, Annual Report: {year}, Page: {page}'
+
+    return f'Answer: {final_answer}\n' + f'\nReferences:\n {references}'
+
+# UI
+
+textbox = gr.Textbox(placeholder="Enter your query here", lines=6)
+
+demo = gr.Interface(
+    inputs=textbox, fn=parse_prediction, outputs="text",
+    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 2021 - 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 2023?", ""],
+              ["Summarize 5 key risks identified in the 2023 10k report? Respond with bullet point summaries.", ""],
+              ["What is the view of the management on the future of electric vehicle batteries?",""]
+             ],
+    cache_examples=False,
+    theme=gr.themes.Base(),
+    concurrency_limit=16
+)
+
+demo.queue()
+demo.launch()