.

app.py

@@ -1,7 +1,78 @@
+from pymongo import MongoClient
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores import MongoDBAtlasVectorSearch
+from langchain.document_loaders import DirectoryLoader
+from langchain.llms import OpenAI
+from langchain.chains import RetrievalQA
 import gradio as gr
+from gradio.themes.base import Base
+#import key_param
+import os
 
-def greet(name):
-    return "Hola " + name + "!!"
+mongo_uri = os.getenv("MONGO_URI")
+openai_api_key = os.getenv("OPENAI_API_KEY")
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+client = MongoClient(mongo_uri)
+dbName = "langchain_demo"
+collectionName = "collection_of_text_blobs"
+collection = client[dbName][collectionName]
+
+# Define the text embedding model
+embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
+
+# Initialize the Vector Store
+vectorStore = MongoDBAtlasVectorSearch( collection, embeddings, index_name="default" )
+
+def query_data(query):
+    # Convert question to vector using OpenAI embeddings
+    # Perform Atlas Vector Search using Langchain's vectorStore
+    # similarity_search returns MongoDB documents most similar to the query    
+
+    docs = vectorStore.similarity_search(query, K=1)
+    as_output = docs[0].page_content
+
+    # Leveraging Atlas Vector Search paired with Langchain's QARetriever
+
+    # Define the LLM that we want to use -- note that this is the Language Generation Model and NOT an Embedding Model
+    # If it's not specified (for example like in the code below),
+    # then the default OpenAI model used in LangChain is OpenAI GPT-3.5-turbo, as of August 30, 2023
+    
+    llm = OpenAI(openai_api_key=openai_api_key, temperature=0)
+
+
+    # Get VectorStoreRetriever: Specifically, Retriever for MongoDB VectorStore.
+    # Implements _get_relevant_documents which retrieves documents relevant to a query.
+    retriever = vectorStore.as_retriever()
+
+    # Load "stuff" documents chain. Stuff documents chain takes a list of documents,
+    # inserts them all into a prompt and passes that prompt to an LLM.
+
+    qa = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=retriever)
+
+    # Execute the chain
+
+    retriever_output = qa.run(query)
+
+
+    # Return Atlas Vector Search output, and output generated using RAG Architecture
+    return as_output, retriever_output
+
+# Create a web interface for the app, using Gradio
+
+with gr.Blocks(theme=Base(), title="Question Answering App using Vector Search + RAG") as demo:
+    gr.Markdown(
+        """
+        # Question Answering App using Atlas Vector Search + RAG Architecture
+        """)
+    textbox = gr.Textbox(label="Enter your Question:")
+    with gr.Row():
+        button = gr.Button("Submit", variant="primary")
+    with gr.Column():
+        output1 = gr.Textbox(lines=1, max_lines=10, label="Output with just Atlas Vector Search (returns text field as is):")
+        output2 = gr.Textbox(lines=1, max_lines=10, label="Output generated by chaining Atlas Vector Search to Langchain's RetrieverQA + OpenAI LLM:")
+
+# Call query_data function upon clicking the Submit button
+
+    button.click(query_data, textbox, outputs=[output1, output2])
+
+demo.launch()

app_old.py

@@ -0,0 +1,7 @@
+import gradio as gr
+
+def greet(name):
+    return "Hola " + name + "!!"
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()