Google Gemini LangChain Cheatsheet

April 28, 2025 7 minute read View Code The langchain-google-genai provides access to Google's powerful Gemini models directly via the Gemini API & Google AI Studio. Google AI Studio enables rapid prototyping and experimentation, making it an ideal starting point for individual developers. LangChain is a framework for developing AI applications. The langchain-google-genai package connects LangChain with Google's Gemini models. LangGraph is a library for building stateful, multi-actor applications with LLMs. All examples use the gemini-2.0-flash model. Gemini 2.5 Pro and 2.5 Flash can be used via gemini-2.5-pro-preview-03-25 and gemini-2.5-flash-preview-04-17. All model ids can be found in the Gemini API docs. Start for free and get your API key from Google AI Studio. Install the package langchain-google-genai Python %pip install langchain-google-genai Set your API key Python import getpass import os if "GOOGLE_API_KEY" not in os.environ: os.environ["GOOGLE_API_KEY"] = getpass.getpass("Enter your Google AI API key: ") Get Started with LangChain. Google Gemini with LangChain Chat Models Learn how to use Google Gemini chat models within LangChain for basic text generation and conversation tasks. Python from langchain_google_genai import ChatGoogleGenerativeAI # Initialize model llm = ChatGoogleGenerativeAI( model="gemini-2.0-flash", temperature=0, max_tokens=None, timeout=None, max_retries=2, ) # Simple invocation messages = [ ("system", "You are a helpful assistant that translates English to French."), ("human", "I love programming."), ] response = llm.invoke(messages) print(response.content) # Output: J'adore la programmation. Chain calls with Prompt Template Discover how to chain LangChain prompt templates with Gemini models for flexible and dynamic input processing. Python from langchain_google_genai import ChatGoogleGenerativeAI from langchain_core.prompts import ChatPromptTemplate # Initialize model llm = ChatGoogleGenerativeAI( model="gemini-2.0-flash", temperature=0, ) prompt = ChatPromptTemplate.from_messages([ ("system", "You are a helpful assistant that translates {input_language} to {output_language}."), ("human", "{input}"), ]) chain = prompt | llm result = chain.invoke({ "input_language": "English", "output_language": "German", "input": "I love programming.", }) print(result.content) # Output: Ich liebe Programmieren. Image Input Explore using image inputs (URLs or local files) with multimodal Gemini models in LangChain for vision tasks. Python from langchain_google_genai import ChatGoogleGenerativeAI from langchain_core.messages import HumanMessage import base64 # Initialize model llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash") # Using an image URL message_url = HumanMessage( content=[ {"type": "text", "text": "Describe this image."}, {"type": "image_url", "image_url": "https://picsum.photos/seed/picsum/200/300"}, ] ) result_url = llm.invoke([message_url]) print(result_url.content) # Using a local image local_image_path = "../assets/react.png" with open(local_image_path, "rb") as image_file: encoded_image = base64.b64encode(image_file.read()).decode('utf-8') message_local = HumanMessage( content=[ {"type": "text", "text": "Describe this image."}, {"type": "image_url", "image_url": f"data:image/png;base64,{encoded_image}"} ] ) result_local = llm.invoke([message_local]) print(result_local.content) Audio Input Understand how to provide audio file data to Gemini models via LangChain for audio processing like transcription. Python from langchain_google_genai import ChatGoogleGenerativeAI from langchain_core.messages import HumanMessage import base64 # Initialize model llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash") audio_file_path = "../path/to/your/audio/file.mp3" audio_mime_type = "audio/mpeg" with open(audio_file_path, "rb") as audio_file: encoded_audio = base64.b64encode(audio_file.read()).decode('utf-8') message = HumanMessage( content=[ {"type": "text", "text": "Transcribe this audio."}, {"type": "media", "data": encoded_audio, "mime_type": audio_mime_type} ] ) response = llm.invoke([message]) print(response.content) Video Input See how to utilize video file input with Gemini models in LangChain for video understanding and analysis. Python from langchain_google_genai import ChatGoogleGenerativeAI from langchain_core.messages import HumanMessage import base64 # Initialize model llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash") video_file_path = "../path/to/your/video/file.mp4" video_mime_type = "video/mp4" with open(video_file_path, "rb") as video_file: encoded_video = base64.b64encode(video_file.read()).decode('utf-8') message = HumanMessage( content=[ {"type": "text", "text": "Describe what's happening in this video."}, {"type": "media", "data": encoded_video, "mime_type": video_mime_type} ] ) response = llm.invoke([message]) print(response.content) Image Generation Generate images from text prompts using specialized Gemini models integrated with LangChain. Python from langchain_google_genai import ChatGoogleGenerativeAI import base64 from IPython.display import Image, display # Initialize model for image generation llm = ChatGoogleGenerativeAI(model="models/gemini-2.0-flash-exp-image-generation") message = { "role": "user", "content": "Generate an image of a cat wearing a hat.", } response = llm.invoke( [message], generation_config=dict(response_modalities=["TEXT", "IMAGE"]), ) # Display the generated image image_base64 = response.content[0].get("image_url").get("url").split(",")[-1] image_data = base64.b64decode(image_base64) display(Image(data=image_data, width=300)) Tool Calling/Function Calling Learn to implement tool calling (function calling) with Gemini models in LangChain to execute custom functions. Python from langchain_google_genai import ChatGoogleGenerativeAI from langchain_core.tools import tool from langchain_core.messages import ToolMessage # Define a tool @tool(description="Get the current weather in a given location") def get_weather(location: str) -> str: return "It's sunny." # Initialize model and bind the tool llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash") llm_with_tools = llm.bind_tools([get_weather]) # Invoke with a query that should trigger the tool query = "What's the weather in San Francisco?" ai_msg = llm_with_tools.invoke(query) # Access tool calls in the response print(ai_msg.tool_calls) # Pass tool results back to the model tool_message = ToolMessage( content=get_weather(*ai_msg.tool_calls[0]['args']), tool_call_id=ai_msg.tool_calls[0]['id'] ) final_response = llm_with_tools.invoke([ai_msg, tool_message]) print(final_response.content) Built-in Tools (Google Search, Code Execution) Leverage Gemini's built-in tools like Google Search and Code Execution directly within your LangChain applications. Python from langchain_google_genai import ChatGoogleGenerativeAI from google.ai.generativelanguage_v1beta.types import Tool as GenAITool # Initialize model llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash") # Google Search search_resp = llm.invoke( "When is the next total solar eclipse in US?", tools=[GenAITool(google_search={})], ) print(search_resp.content) # Code Execution code_resp = llm.invoke( "What is 2*2, use python", tools=[GenAITool(code_execution={})], ) for c in code_resp.content: if isinstance(c, dict): if c["type"] == 'code_execution_result': print(f"Code execution result: {c['code_execution_result']}") elif c["type"] == 'executable_code': print(f"Executable code: {c['executable_code']}") else: print(c) Structured Output Control Gemini model output to conform to a specific Pydantic schema for reliable structured data extraction in LangChain. Python from langchain_core.pydantic_v1 import BaseModel, Field from langchain_google_genai import ChatGoogleGenerativeAI # Define the desired structure class Person(BaseModel): '''Information about a person.''' name: str = Field(..., description="The person's name") height_m: float = Field(..., description="The person's height in meters") # Initialize the model llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0) structured_llm = llm.with_structured_output(Person) # Invoke the model with a query asking for structured information result = structured_llm.invoke("Who was the 16th president of the USA, and how tall was he in meters?") print(result) # Output: name='Abraham Lincoln' height_m=1.93 Token Usage Tracking Track token usage for Gemini model calls within LangChain to monitor costs and API consumption. Python from langchain_google_genai import ChatGoogleGenerativeAI # Initialize model llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash") result = llm.invoke("Explain the concept of prompt engineering in one sentence.") print(result.content) print("\nUsage Metadata:") print(result.usage_metadata) Google Gemini Embeddings with LangChain Generate powerful text embeddings using Google Gemini models within the LangChain framework for semantic understanding. Python from langchain_google_genai import GoogleGenerativeAIEmbeddings embeddings = GoogleGenerativeAIEmbeddings(model="models/gemini-embedding-exp-03-07") # Embed a single query vector = embeddings.embed_query("hello, world!") # Embed multiple documents vectors = embeddings.embed_documents([ "Today is Monday", "Today is Tuesday", "Today is April Fools day", ]) Using with Vector Store Integrate Gemini text embeddings with LangChain vector stores for efficient similarity search and information retrieval. Python from langchain_google_genai import GoogleGenerativeAIEmbeddings from langchain_core.vectorstores import InMemoryVectorStore # Initialize embeddings embeddings = GoogleGenerativeAIEmbeddings(model="models/gemini-embedding-exp-03-07") text = "LangChain is the framework for building context-aware reasoning applications" # Create vector store and retriever vectorstore = InMemoryVectorStore.from_texts([text], embedding=embeddings) retriever = vectorstore.as_retriever() # Retrieve similar documents retrieved_documents = retriever.invoke("What is LangChain?") print(retrieved_documents[0].page_content) Task Types Optimize Gemini embedding performance by selecting the appropriate task type (e.g., retrieval, classification) in LangChain. Python %pip install scikit-learn Python from langchain_google_genai import GoogleGenerativeAIEmbeddings from sklearn.metrics.pairwise import cosine_similarity # Different task types for different use cases query_embeddings = GoogleGenerativeAIEmbeddings( model="models/gemini-embedding-exp-03-07", task_type="RETRIEVAL_QUERY" # For queries ) doc_embeddings = GoogleGenerativeAIEmbeddings( model="models/gemini-embedding-exp-03-07", task_type="RETRIEVAL_DOCUMENT" # For documents ) # Compare similarity q_embed = query_embeddings.embed_query("What is the capital of France?") d_embed = doc_embeddings.embed_documents(["The capital of France is Paris.", "Philipp likes to eat pizza."]) for i, d in enumerate(d_embed): similarity = cosine_similarity([q_embed], [d])[0][0] print(f"Document {i+1} similarity: {similarity}") Thanks for reading! If you have any questions or feedback, please let me know on Twitter or LinkedIn.