# pylint: disable=broad-exception-caught,invalid-name import time from google import genai from google.cloud import bigquery from google.genai.types import FunctionDeclaration, GenerateContentConfig, Part, Tool import streamlit as st BIGQUERY_DATASET_ID = "thelook_ecommerce" MODEL_ID = "gemini-2.0-flash" LOCATION = "us-central1" list_datasets_func = FunctionDeclaration( name="list_datasets", description="Get a list of datasets that will help answer the user's question", parameters={ "type": "object", "properties": {}, }, ) list_tables_func = FunctionDeclaration( name="list_tables", description="List tables in a dataset that will help answer the user's question", parameters={ "type": "object", "properties": { "dataset_id": { "type": "string", "description": "Dataset ID to fetch tables from.", } }, "required": [ "dataset_id", ], }, ) get_table_func = FunctionDeclaration( name="get_table", description="Get information about a table, including the description, schema, and number of rows that will help answer the user's question. Always use the fully qualified dataset and table names.", parameters={ "type": "object", "properties": { "table_id": { "type": "string", "description": "Fully qualified ID of the table to get information about", } }, "required": [ "table_id", ], }, ) sql_query_func = FunctionDeclaration( name="sql_query", description="Get information from data in BigQuery using SQL queries", parameters={ "type": "object", "properties": { "query": { "type": "string", "description": "SQL query on a single line that will help give quantitative answers to the user's question when run on a BigQuery dataset and table. In the SQL query, always use the fully qualified dataset and table names.", } }, "required": [ "query", ], }, ) sql_query_tool = Tool( function_declarations=[ list_datasets_func, list_tables_func, get_table_func, sql_query_func, ], ) client = genai.Client(vertexai=True, location=LOCATION) st.set_page_config( page_title="SQL Talk with BigQuery", page_icon="vertex-ai.png", layout="wide", ) col1, col2 = st.columns([8, 1]) with col1: st.title("SQL Talk with BigQuery") with col2: st.image("vertex-ai.png") st.subheader("Powered by Function Calling in Gemini") st.markdown( "[Source Code](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/gemini/function-calling/sql-talk-app/) • [Documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/multimodal/function-calling) • [Codelab](https://codelabs.developers.google.com/codelabs/gemini-function-calling) • [Sample Notebook](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/function-calling/intro_function_calling.ipynb)" ) with st.expander("Sample prompts", expanded=True): st.write( """ - What kind of information is in this database? - What percentage of orders are returned? - How is inventory distributed across our regional distribution centers? - Do customers typically place more than one order? - Which product categories have the highest profit margins? """ ) if "messages" not in st.session_state: st.session_state.messages = [] for message in st.session_state.messages: with st.chat_message(message["role"]): st.markdown(message["content"].replace("$", r"\$")) # noqa: W605 try: with st.expander("Function calls, parameters, and responses"): st.markdown(message["backend_details"]) except KeyError: pass if prompt := st.chat_input("Ask me about information in the database..."): st.session_state.messages.append({"role": "user", "content": prompt}) with st.chat_message("user"): st.markdown(prompt) with st.chat_message("assistant"): message_placeholder = st.empty() full_response = "" chat = client.chats.create( model=MODEL_ID, config=GenerateContentConfig(temperature=0, tools=[sql_query_tool]), ) client = bigquery.Client() prompt += """ Please give a concise, high-level summary followed by detail in plain language about where the information in your response is coming from in the database. Only use information that you learn from BigQuery, do not make up information. """ try: response = chat.send_message(prompt) response = response.candidates[0].content.parts[0] print(response) api_requests_and_responses = [] backend_details = "" function_calling_in_process = True while function_calling_in_process: try: params = {} for key, value in response.function_call.args.items(): params[key] = value print(response.function_call.name) print(params) if response.function_call.name == "list_datasets": api_response = client.list_datasets() api_response = BIGQUERY_DATASET_ID api_requests_and_responses.append( [response.function_call.name, params, api_response] ) if response.function_call.name == "list_tables": api_response = client.list_tables(params["dataset_id"]) api_response = str([table.table_id for table in api_response]) api_requests_and_responses.append( [response.function_call.name, params, api_response] ) if response.function_call.name == "get_table": api_response = client.get_table(params["table_id"]) api_response = api_response.to_api_repr() api_requests_and_responses.append( [ response.function_call.name, params, [ str(api_response.get("description", "")), str( [ column["name"] for column in api_response["schema"][ "fields" ] ] ), ], ] ) api_response = str(api_response) if response.function_call.name == "sql_query": job_config = bigquery.QueryJobConfig( maximum_bytes_billed=100000000 ) # Data limit per query job try: cleaned_query = ( params["query"] .replace("\\n", " ") .replace("\n", "") .replace("\\", "") ) query_job = client.query( cleaned_query, job_config=job_config ) api_response = query_job.result() api_response = str([dict(row) for row in api_response]) api_response = api_response.replace("\\", "").replace( "\n", "" ) api_requests_and_responses.append( [response.function_call.name, params, api_response] ) except Exception as e: error_message = f""" We're having trouble running this SQL query. This could be due to an invalid query or the structure of the data. Try rephrasing your question to help the model generate a valid query. Details: {str(e)}""" st.error(error_message) api_response = error_message api_requests_and_responses.append( [response.function_call.name, params, api_response] ) st.session_state.messages.append( { "role": "assistant", "content": error_message, } ) print(api_response) response = chat.send_message( Part.from_function_response( name=response.function_call.name, response={ "content": api_response, }, ), ) response = response.candidates[0].content.parts[0] backend_details += "- Function call:\n" backend_details += ( " - Function name: ```" + str(api_requests_and_responses[-1][0]) + "```" ) backend_details += "\n\n" backend_details += ( " - Function parameters: ```" + str(api_requests_and_responses[-1][1]) + "```" ) backend_details += "\n\n" backend_details += ( " - API response: ```" + str(api_requests_and_responses[-1][2]) + "```" ) backend_details += "\n\n" with message_placeholder.container(): st.markdown(backend_details) except AttributeError: function_calling_in_process = False time.sleep(3) full_response = response.text with message_placeholder.container(): st.markdown(full_response.replace("$", r"\$")) # noqa: W605 with st.expander("Function calls, parameters, and responses:"): st.markdown(backend_details) st.session_state.messages.append( { "role": "assistant", "content": full_response, "backend_details": backend_details, } ) except Exception as e: print(e) error_message = f""" Something went wrong! We encountered an unexpected error while trying to process your request. Please try rephrasing your question. Details: {str(e)}""" st.error(error_message) st.session_state.messages.append( { "role": "assistant", "content": error_message, } )