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app.py

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+from pymongo import MongoClient
+import os
+import time
+import gradio as gr
+import requests
+import traceback
+import google.generativeai as genai
+
+
+
+
+genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
+try:
+    # Initialize MongoDB python client
+    MONGODB_URI = os.getenv("MONGODB_ATLAS_URI")
+    client = MongoClient(MONGODB_URI, appname="devrel.content.python")
+
+    DB_NAME = "google-ai"
+    COLLECTION_NAME = "embedded_docs"
+    ATLAS_VECTOR_SEARCH_INDEX_NAME = "vector_index"
+    collection = client[DB_NAME][COLLECTION_NAME]
+
+    ### Insert data about 5 individual employees
+    collection.delete_many({})
+    collection.insert_many([
+        {
+            '_id' : '54633',
+            'content' : 'Employee number 54633, name  John Doe, department  Sales, location  New York, salary  100000'
+        },
+        {
+            '_id' : '54634',
+            'content' : 'Employee number 54634, name  Jane Doe, department  Marketing, location  Los Angeles, salary  120000',
+
+        },
+        {
+            '_id' : '54635',
+            'content' : 'Employee number 54635, name  John Smith, department  Engineering, location  San Francisco, salary  150000'
+        },
+        {
+            '_id' : '54636',
+            'content' : 'Employee number 54636, name  Jane Smith, department  Finance, location  Chicago, salary  130000'
+        },
+        {
+            '_id' : '54637',
+            'content' : 'Employee number 54637, name  John Johnson, department  HR, location  Miami, salary  110000'
+        },
+        {
+            '_id' : '54638',
+            'content' : 'Employee number 54638, name  Jane Johnson, department  Operations, location  Seattle, salary  140000'
+        }
+    ])
+
+
+    # Exception handling to catch and display errors during the pipeline execution.
+except Exception as erorr_message:
+    print("An error occurred: \n" + erorr_message)
+
+gemini_pro = genai.GenerativeModel('gemini-pro')
+
+def embed_text(text):
+    result = genai.embed_content(
+    model="models/embedding-001",
+    content=text,
+    task_type="retrieval_document",
+    title="Embedding of single string")
+
+    return result['embedding']
+
+def get_rag_output(context, question):
+   
+    template = f""" You are an hr assistant, answer in detail. Answer the question based only on the following context:
+        ```
+        {context}
+        ```
+        Question: {question}
+        """
+    response = gemini_pro.generate_content([template], stream=False)
+    return response.text
+
+def mongodb_vector_query(message):
+    docs = collection.aggregate([
+        {
+            '$vectorSearch' : {
+                'index' : 'vector_index',
+                'queryVector' : embed_text(message),
+                'path' : 'embedding',
+                'numCandidates' : 10,
+                'limit' : 5
+            }
+        },
+        {
+            '$project': {
+                'embedding': 0
+            }
+        }
+    ])
+
+    return list(docs)
+
+
+
+
+def get_rag(message, history):
+
+    try:
+        context = mongodb_vector_query(message)
+        result = get_rag_output(context, message)
+        
+        # print(result)
+        print_llm_text = result
+        for i in range(len(print_llm_text)):
+            time.sleep(0.03)
+            yield print_llm_text[: i+1]
+        
+    
+    except Exception as e:
+        error_message = traceback.format_exc()
+        print("An error occurred: \n" + error_message)
+        yield error_message
+        
+
+
+
+def fetch_url_data(url):
+    try:
+        response = requests.get(url)
+        response.raise_for_status()  # Raises an HTTPError if the HTTP request returned an unsuccessful status code
+        return response.text
+    except requests.RequestException as e:
+        return f"Error: {e}"
+        
+# Setup Gradio interface
+with gr.Blocks() as demo:
+    with gr.Tab("Demo"):
+       
+        ## value=[(None, "Hi, I'm a MongoDB and Heystack based question and answer bot 🤖, I can help you answer on the knowledge base above…")]
+        gr.ChatInterface(get_rag,examples=["List all employees", "Where does jane work?", "Who has the highest salary? List it"], title="Atlas Vector Search Chat",description="This small chat uses a similarity search to find relevant plots as listed above, it uses MongoDB Atlas and Google Gemini.",submit_btn="Search").queue()
+
+    with gr.Tab("Code"):
+        gr.Code(label="Code", language="python", value=fetch_url_data('https://huggingface.co/spaces/MongoDB/Haystack-MongoDB-Integration-Chat/raw/main/app.py'))
+
+        
+
+    
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+google-generativeai
+pymongo
+requests