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app.py

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+# Import the necessary Libraries
+from warnings import filterwarnings
+filterwarnings('ignore')
+import os
+import uuid
+import json
+import gradio as gr
+import pandas as pd
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+from langchain.embeddings import SentenceTransformerEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.llms import OpenAI
+
+# Create Client
+import os
+os.environ['OPENAI_API_KEY']  = "gl-U2FsdGVkX1+0bNWD6YsVLZUYsn0m1WfLxUzrP0xUFbtWFAfk9Z1Cz+mD8u1yqKtV"; # e.g. gl-U2FsdGVkX19oG1mRO+LGAiNeC7nAeU8M65G4I6bfcdI7+9GUEjFFbplKq48J83by
+os.environ["OPENAI_BASE_URL"] =  "https://aibe.mygreatlearning.com/openai/v1" # e.g. "https://aibe.mygreatlearning.com/openai/v1";
+
+llm_client = OpenAI()
+
+# Define the embedding model and the vectorstore
+embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')
+vectorstore_persisted = Chroma(
+    collection_name='10k_reports',
+    persist_directory='10k_reports_db',
+    embedding_function=embedding_model
+)
+
+# Load the persisted vectorDB
+vectorstore_persisted.load()
+
+#
+##
+#
+
+# Prepare the logging functionality
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
+
+scheduler = CommitScheduler(
+    repo_id="eric-green-rag-financial-analyst",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+# Define the Q&A system message
+# Create a system message for the LLM
+qna_system_message = """
+You are an assistant to a tech industry financial analyst. Your task is to provide relevant information about a set of companies AWS, Google, IBM, Meta, Microsoft.
+
+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 source links.
+The source for a context will begin with the token ###Source.
+
+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 to financial report information for the 5 companies, respond with "I am unable to locate relevent information.  I answer questions related to the financial performance of AWS, Google, IBM, Meta and Microsoft."
+
+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 "I am unable to locate a relevent answer."
+- Always quote the source when you use the context. Cite the relevant source at the end of your response under the section - Source:
+- 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]
+
+Source:
+[Source]
+"""
+
+# Define the user message template
+# Create a message template
+qna_user_message_template = """
+###Context
+{context}
+
+###Question
+{question}
+"""
+
+# Define the llm_query function that runs when 'Submit' is clicked or when a API request is made
+def llm_query(user_input,company):
+
+    filter = "dataset/"+company+"-10-k-2023.pdf"
+    
+    relevant_document_chunks = vectorstore_persisted.similarity_search(user_input, k=5, filter={"source":filter})
+
+    # 1 - Create context_for_query
+    context_list = [d.page_content + "\n ###Source: " + str(d.metadata['page']) + "\n\n " for d in relevant_document_chunks]
+
+    context_for_query = ". ".join(context_list)
+
+    # 2 - Create messages
+    prompt = [
+        {'role':'system', 'content': qna_system_message},
+        {'role': 'user', 'content': qna_user_message_template.format(
+            context=context_for_query,
+            question=user_input
+            )
+        }
+    ]
+
+    # Get response from the LLM
+    try:
+        response = llm_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}'
+
+    print(prediction)
+
+    # 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
+
+# Set-up the Gradio UI
+company = gr.Radio(Label='Company:', choices=["aws", "google", "ibm", "meta", "microsoft"]) # Create a radio button for company selection
+textbox = gr.Textbox(Label='Question:') # Create a textbox for user input
+
+# Create Gradio interface
+# For the inputs parameter of Interface provide [textbox,company] with outputs parameter of Interface provide prediction
+demo = gr.Interface(fn=llm_query, inputs=[textbox, company], outputs="text")
+
+demo.queue()
+demo.launch()

requirements.txt

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+    f.write('openai==1.23.2\n')
+    f.write('tiktoken==0.6.0\n')
+    f.write('pypdf==4.0.1\n')
+    f.write('langchain==0.1.1\n')
+    f.write('langchain-community==0.0.13\n')
+    f.write('chromadb==0.4.22\n')
+    f.write('sentence-transformers==2.3.1\n')
+    f.write('gradio==3.23.0\n')
+
+print('requirements.txt created!')