Seamlessly Integrate Gemini with LangChain and LlamaIndex: Build a Private RAG Knowledge Base
How do you use the most cost-effective Gemini in mainstream large model frameworks? With only two lines of configuration, this guide shows you how to seamlessly switch your RAG knowledge base and agent foundation to the high-value Gemini 1.5.
1. Replace the Framework's Underlying Layer with Two Lines of Code
In enterprise RAG development, we often don't need to rewrite logic; we can directly use the official packaged provider integration packages.
2. Minimal LangChain Integration Code
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.messages import HumanMessage
llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro", temperature=0.3)
message = HumanMessage(content="Answer the user's question using the retrieved knowledge base passages below...")
response = llm.invoke([message])
print(response.content)
It works out of the box with the full PromptTemplate, OutputParser, and LCEL expression call chains, so migration costs are almost zero!