Upload 2 files

app.py

@@ -0,0 +1,147 @@
+# Import the necessary Libraries
+import json
+import uuid
+import os
+
+from openai import OpenAI
+import gradio as gr
+
+from langchain_community.embeddings.sentence_transformer import (
+    SentenceTransformerEmbeddings
+)
+from langchain_community.vectorstores import Chroma
+from huggingface_hub import CommitScheduler
+
+
+client = OpenAI(
+    base_url="https://api.endpoints.anyscale.com/v1",
+    api_key=os.environ['ANYSCALE_API_KEY']
+)
+
+embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')
+
+streamlit_collection = 'ai_collection'
+
+vectorstore_persisted = Chroma(
+    collection_name=streamlit_collection,
+    persist_directory='./reports_db',
+    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="reports-qna",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+qna_system_message = """
+You are an assistant to a Financial Analyst. Your task is to summarize and provide relevant information to the financial analyst's question based on the provided context.
+
+User input will include the necessary context for you to answer their questions. This context will begin with the token: ###Context.
+The context contains references to specific portions of documents relevant to the user's query, along with page number from the report.
+The source for the context will begin with the token ###Page
+
+When crafting your response:
+1. Select only context relevant to answer the question.
+2. Include the source links in your response.
+3. User questions will begin with the token: ###Question.
+4. If the question is irrelevant or if you do not have the information to  respond with - "Sorry, this is out of my knowledge base"
+
+Please adhere to the following guidelines:
+- Your response should only be about the question asked and nothing else.
+- Answer only using the context provided.
+- Do not mention anything about the context in your final answer.
+- If the answer is not found in the context, it is very very important for you to respond with "Sorry, this is out of my knowledge base"
+- Always quote the page number when you use the context. Cite the relevant page number at the end of your response under the section - Page:
+- Do not make up sources Use the links provided in the sources section of the context and nothing else. You are prohibited from providing other links/sources.
+
+Here is an example of how to structure your response:
+
+Answer:
+[Answer]
+
+Page:
+[Page number]
+"""
+
+qna_user_message_template = """
+###Context
+Here are some documents and their page number that are relevant to the question mentioned below.
+{context}
+
+###Question
+{question}
+"""
+
+# Define the predict function that runs when 'Submit' is clicked or when a API request is made
+def predict(user_input,company):
+
+    filter = "dataset/"+company+"-10-k-2023.pdf"
+    relevant_document_chunks = retriever.invoke(user_input)
+    context_list = [d.page_content + "\n ###Page: " + str(d.metadata['page']) + "\n\n " 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/Mixtral-8x7B-Instruct-v0.1',
+            messages=prompt,
+            temperature=0
+        )
+
+        prediction = response.choices[0].message.content
+
+    except Exception as e:
+        prediction = 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
+
+
+textbox = gr.Textbox(placeholder="Enter your query here", lines=6)
+company = gr.Radio(choices=["google", "msft", "aws", "ibm", "meta"], label="Select an option")
+
+# Create the interface
+demo = gr.Interface(
+    inputs={"textbox": textbox, "options": company}, fn=predict, outputs="text",
+    title="!0-k Reports Q&A System",
+    description="This web API presents an interface to ask questions on 10-k reports ",
+    concurrency_limit=16
+)
+
+demo.queue()
+demo.launch()

requirements.txt

@@ -0,0 +1,5 @@
+openai==1.23.2
+chromadb==0.4.22
+langchain==0.1.9
+langchain-community==0.0.32
+sentence-transformers==2.3.1