Create app.py

app.py

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+import os
+
+import gradio as gr
+
+from openai import OpenAI
+
+from langchain_community.embeddings.sentence_transformer import SentenceTransformerEmbeddings
+from langchain_community.vectorstores import Chroma
+
+
+client = OpenAI(
+    base_url="https://api.endpoints.anyscale.com/v1",
+    api_key=os.environ['ANYSCALE_API_KEY']
+)
+
+embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-small')
+
+tesla_10k_collection = 'tesla-10k-2019-to-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': 5}
+)
+
+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(user_input):
+
+    relevant_document_chunks = retriever.get_relevant_documents(user_input)
+    context_list = [d.page_content for d in relevant_document_chunks]
+    context_for_query = ".".join(context_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="mistralai/Mistral-7B-Instruct-v0.1",
+            messages=prompt,
+            temperature=0
+        )
+
+        prediction = response.choices[0].message.content
+
+    except Exception as e:
+        prediction = e
+        
+    return prediction
+
+
+textbox = gr.Textbox(placeholder="Enter your query here", lines=6)
+
+interface = gr.Interface(
+    inputs=textbox, fn=predict, 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 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?", "$ 81.46 Billion"],
+              ["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.", ""]
+             ]
+)
+
+with gr.Blocks() as demo:
+    interface.launch()
+
+demo.queue(concurrency_count=16)
+demo.launch()