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Update foundational example 33 to work with google-genai

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This commit is contained in:
Mark Backman
2025-05-27 11:06:34 -04:00
parent ebb3d1cfd3
commit 6d94f31ff2
1 changed files with 14 additions and 12 deletions
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26
examples/foundational/33-gemini-rag.py
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@@ -52,8 +52,8 @@ import json
import os import os
import time import time
import google.generativeai as genai
from dotenv import load_dotenv from dotenv import load_dotenv
from google import genai
from loguru import logger from loguru import logger
from pipecat.audio.vad.silero import SileroVADAnalyzer from pipecat.audio.vad.silero import SileroVADAnalyzer
@@ -71,6 +71,9 @@ from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
load_dotenv(override=True) load_dotenv(override=True)
# Initialize the client globally
client = genai.Client(api_key=os.environ["GOOGLE_API_KEY"])
def get_rag_content(): def get_rag_content():
"""Get the RAG content from the file.""" """Get the RAG content from the file."""
@@ -105,20 +108,12 @@ Each request will include:
Here is the knowledge base you have access to: Here is the knowledge base you have access to:
{RAG_CONTENT} {RAG_CONTENT}
""" """
genai.configure(api_key=os.environ["GOOGLE_API_KEY"])
async def query_knowledge_base(params: FunctionCallParams): async def query_knowledge_base(params: FunctionCallParams):
"""Query the knowledge base for the answer to the question.""" """Query the knowledge base for the answer to the question."""
logger.info(f"Querying knowledge base for question: {params.arguments['question']}") logger.info(f"Querying knowledge base for question: {params.arguments['question']}")
client = genai.GenerativeModel(
model_name=RAG_MODEL,
system_instruction=RAG_PROMPT,
generation_config=genai.types.GenerationConfig(
temperature=0.1,
max_output_tokens=64,
),
)
# for our case, the first two messages are the instructions and the user message # for our case, the first two messages are the instructions and the user message
# so we remove them. # so we remove them.
conversation_turns = params.context.messages[2:] conversation_turns = params.context.messages[2:]
@@ -143,8 +138,15 @@ async def query_knowledge_base(params: FunctionCallParams):
logger.info(f"Conversation turns: {messages_json}") logger.info(f"Conversation turns: {messages_json}")
start = time.perf_counter() start = time.perf_counter()
response = client.generate_content( full_prompt = f"System: {RAG_PROMPT}\n\nConversation History: {messages_json}"
contents=[messages_json],
response = await client.aio.models.generate_content(
model=RAG_MODEL,
contents=[full_prompt],
config={
"temperature": 0.1,
"max_output_tokens": 64,
},
) )
end = time.perf_counter() end = time.perf_counter()
logger.info(f"Time taken: {end - start:.2f} seconds") logger.info(f"Time taken: {end - start:.2f} seconds")