mirror of
https://github.com/hwchase17/langchain
synced 2024-11-18 09:25:54 +00:00
6137894008
This is a template demonstrating how to utilize Google Sensitive Data Protection in conjunction with ChatVertexAI(). Tagging you @efriis as you reviewed my last template. :) Thanks! Proof of successful execution:  --------- Co-authored-by: Erick Friis <erick@langchain.dev>
51 lines
1.4 KiB
Python
51 lines
1.4 KiB
Python
import os
|
|
|
|
from langchain.chat_models import ChatVertexAI
|
|
from langchain.prompts import ChatPromptTemplate
|
|
from langchain.pydantic_v1 import BaseModel
|
|
from langchain.retrievers import GoogleVertexAISearchRetriever
|
|
from langchain.schema.output_parser import StrOutputParser
|
|
from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
|
|
|
|
# Get project, data store, and model type from env variables
|
|
project_id = os.environ.get("GOOGLE_CLOUD_PROJECT_ID")
|
|
data_store_id = os.environ.get("DATA_STORE_ID")
|
|
model_type = os.environ.get("MODEL_TYPE")
|
|
|
|
if not data_store_id:
|
|
raise ValueError(
|
|
"No value provided in env variable 'DATA_STORE_ID'. "
|
|
"A data store is required to run this application."
|
|
)
|
|
|
|
# Set LLM and embeddings
|
|
model = ChatVertexAI(model_name=model_type, temperature=0.0)
|
|
|
|
# Create Vertex AI retriever
|
|
retriever = GoogleVertexAISearchRetriever(
|
|
project_id=project_id, search_engine_id=data_store_id
|
|
)
|
|
|
|
# RAG prompt
|
|
template = """Answer the question based only on the following context:
|
|
{context}
|
|
Question: {question}
|
|
"""
|
|
prompt = ChatPromptTemplate.from_template(template)
|
|
|
|
# RAG
|
|
chain = (
|
|
RunnableParallel({"context": retriever, "question": RunnablePassthrough()})
|
|
| prompt
|
|
| model
|
|
| StrOutputParser()
|
|
)
|
|
|
|
|
|
# Add typing for input
|
|
class Question(BaseModel):
|
|
__root__: str
|
|
|
|
|
|
chain = chain.with_types(input_type=Question)
|