langgraph-chat-google-genai

Instantiation

    from langchain_google_genai import ChatGoogleGenerativeAI

    model = ChatGoogleGenerativeAI(model="gemini-3.1-pro-preview")
    model.invoke("Write me a ballad about LangChain")

File Processing

PDF Input

Chat with model to describe a PDF document

    import base64
    from langchain.messages import HumanMessage

    pdf_bytes = open("/path/to/your/test.pdf", "rb").read()
    pdf_base64 = base64.b64encode(pdf_bytes).decode("utf-8")

    message = HumanMessage(
        content=[
            {"type": "text", "text": "describe the document in a sentence"},
            {
                "type": "file",
                "source_type": "base64",
                "mime_type": "application/pdf",
                "data": pdf_base64,
            },
        ]
    )
    ai_msg = model.invoke([message])

File upload

You can also upload files to Google's servers and reference them by URI. This works for PDFs, images, videos, and audio files.

    import time
    from google import genai
    from langchain.messages import HumanMessage

    client = genai.Client()

    myfile = client.files.upload(file="/path/to/your/sample.pdf")
    while myfile.state.name == "PROCESSING":
    time.sleep(2)
    myfile = client.files.get(name=myfile.name)

    message = HumanMessage(
        content=[
            {"type": "text", "text": "What is in the document?"},
            {
                "type": "media",
                "file_uri": myfile.uri,
                "mime_type": "application/pdf",
            },
        ]
    )
    ai_msg = model.invoke([message])

Context Caching

Context caching allows you to store and reuse content (e.g., PDFs, images) for faster processing. The cached_content parameter accepts a cache name created via the Google Generative AI API.

Single file caching example

    from google import genai
    from google.genai import types
    import time
    from langchain_google_genai import ChatGoogleGenerativeAI
    from langchain.messages import HumanMessage

    client = genai.Client()

    # Upload file
    file = client.files.upload(file="path/to/your/file")
    while file.state.name == "PROCESSING":
        time.sleep(2)
        file = client.files.get(name=file.name)

    # Create cache
    model = "gemini-3.1-pro-preview"
    cache = client.caches.create(
        model=model,
        config=types.CreateCachedContentConfig(
            display_name="Cached Content",
            system_instruction=(
                "You are an expert content analyzer, and your job is to answer "
                "the user's query based on the file you have access to."
            ),
            contents=[file],
            ttl="300s",
        ),
    )

    # Query with LangChain
    llm = ChatGoogleGenerativeAI(
        model=model,
        cached_content=cache.name,
    )
    message = HumanMessage(content="Summarize the main points of the content.")
    llm.invoke([message])

Multiple file caching example

    from google import genai
    from google.genai.types import CreateCachedContentConfig, Content, Part
    import time
    from langchain_google_genai import ChatGoogleGenerativeAI
    from langchain.messages import HumanMessage

    client = genai.Client()

    # Upload files
    file_1 = client.files.upload(file="./file1")
    while file_1.state.name == "PROCESSING":
        time.sleep(2)
        file_1 = client.files.get(name=file_1.name)

    file_2 = client.files.upload(file="./file2")
    while file_2.state.name == "PROCESSING":
        time.sleep(2)
        file_2 = client.files.get(name=file_2.name)

    # Create cache with multiple files
    contents = [
        Content(
            role="user",
            parts=[
                Part.from_uri(file_uri=file_1.uri, mime_type=file_1.mime_type),
                Part.from_uri(file_uri=file_2.uri, mime_type=file_2.mime_type),
            ],
        )
    ]
    model = "gemini-3.1-pro-preview"
    cache = client.caches.create(
        model=model,
        config=CreateCachedContentConfig(
            display_name="Cached Contents",
            system_instruction=(
                "You are an expert content analyzer, and your job is to answer "
                "the user's query based on the files you have access to."
            ),
            contents=contents,
            ttl="300s",
        ),
    )

    # Query with LangChain
    llm = ChatGoogleGenerativeAI(
        model=model,
        cached_content=cache.name,
    )
    message = HumanMessage(
        content="Provide a summary of the key information across both files."
    )
    llm.invoke([message])