rag

app.py

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+
+
+## Setup
+!pip install -q openai==1.23.2 \
+                tiktoken==0.6.0 \
+                pypdf==4.0.1 \
+                langchain==0.1.1 \
+                langchain-community==0.0.13 \
+                chromadb==0.4.22 \
+                sentence-transformers==2.3.1 \
+                datasets
+
+
+
+
+
+# Import the necessary Libraries
+import os
+import json
+import uuid
+import gradio as gr
+import tiktoken
+from datasets import load_dataset
+
+import pandas as pd
+
+from openai import OpenAI
+
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_core.documents import Document
+
+from langchain_community.embeddings.sentence_transformer import (
+    SentenceTransformerEmbeddings
+)
+from langchain_community.vectorstores import Chroma
+
+from google.colab import userdata, drive
+
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+
+from google.colab import userdata
+
+
+
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+
+
+
+
+# Create Client
+
+
+OpenAI__api_key = userdata.get('CarlosGM')
+client = OpenAI(
+    api_key=OpenAI__api_key
+)
+
+model_name = 'gpt-3.5-turbo'
+
+
+# Define the embedding model and the vectorstore
+
+embedding_model = SentenceTransformerEmbeddings(model_name='thenlper/gte-large')
+
+# Load the persisted vectorDB
+
+## persisted_vectordb_location = '/content/drive/MyDrive/dataset_db'
+
+dataset_10k_collection = 'Dataset-IBM-Meta-aws-google-msft'
+
+vectorstore_persisted = Chroma(
+    collection_name=dataset_10k_collection,
+    persist_directory= './dataset_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="10k-logs",
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+# Define the Q&A system message
+
+qna_system_message = """
+You are an assistant to 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 token: ###Context.
+The context contains references to specific portions of a document relevant to the user query.
+The source for a context will begin with the token ###Source
+
+User questions will begin with the token: ###Question.
+
+
+
+Please answer only using the context provided in the input. Do not mention anything about the context in your final answer.
+
+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 don't know. Please check the docs @ 'Dataset-10k file'"
+- 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 files provided in the sources section of the context and nothing else. You are prohibited from providing other sources.
+
+
+If the answer is not found in the context, respond "I don't know".
+
+Here is an example of how to structure your response:
+
+Answer:
+[Answer]
+
+Source:
+[Source]
+
+"""
+
+
+
+
+# Define the user message template
+
+qna_user_message_template = """
+###Context
+Here are some documents that are relevant to the question.
+{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 = vectorstore_persisted.similarity_search(user_input, k=5, filter={"source":filter})
+
+    # Create context_for_query
+
+
+    context_list = [d.page_content for d in relevant_document_chunks]
+    context_for_query = ". ".join(context_list)
+   
+
+    # 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 = 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}'
+
+
+
+
+
+
+
+
+    # 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
+
+
+
+# Add text box and radio button to the interface
+# The radio button is used to select the company 10k report in which the context needs to be retrieved.
+
+textbox = gr.Textbox(placeholder='Enter your query here', lines=6)
+company = gr.Radio(['aws', 'google', ibm, 'meta', 'msft'], label= "Select Company 10-k Report")
+
+# Create the interface
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=[textbox,company],
+    outputs= 'text'
+    title= '10-k Report Q&A',
+    description = 'This Web API presents an inteface to ask questions about the 10-k reports')
+    
+
+# For the inputs parameter of Interface provide [textbox,company]
+
+
+demo.queue()
+demo.launch()

requirements.txt

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+openai==1.23.2
+chromadb==0.4.22
+langchain==0.1.9
+langchain-community==0.0.32
+sentence-transformers==2.3.1