langchain/templates/rag-gemini-multi-modal/rag_gemini_multi_modal/chain.py

import base64
import io
from pathlib import Path

from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
from langchain_core.messages import HumanMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
from langchain_experimental.open_clip import OpenCLIPEmbeddings
from langchain_google_genai import ChatGoogleGenerativeAI
from PIL import Image


def resize_base64_image(base64_string, size=(128, 128)):
    """
    Resize an image encoded as a Base64 string.

    :param base64_string: A Base64 encoded string of the image to be resized.
    :param size: A tuple representing the new size (width, height) for the image.
    :return: A Base64 encoded string of the resized image.
    """
    img_data = base64.b64decode(base64_string)
    img = Image.open(io.BytesIO(img_data))
    resized_img = img.resize(size, Image.LANCZOS)
    buffered = io.BytesIO()
    resized_img.save(buffered, format=img.format)
    return base64.b64encode(buffered.getvalue()).decode("utf-8")


def get_resized_images(docs):
    """
    Resize images from base64-encoded strings.

    :param docs: A list of base64-encoded image to be resized.
    :return: Dict containing a list of resized base64-encoded strings.
    """
    b64_images = []
    for doc in docs:
        if isinstance(doc, Document):
            doc = doc.page_content
        resized_image = resize_base64_image(doc, size=(1280, 720))
        b64_images.append(resized_image)
    return {"images": b64_images}


def img_prompt_func(data_dict, num_images=2):
    """
    Gemini prompt for image analysis.

    :param data_dict: A dict with images and a user-provided question.
    :param num_images: Number of images to include in the prompt.
    :return: A list containing message objects for each image and the text prompt.
    """
    messages = []
    if data_dict["context"]["images"]:
        for image in data_dict["context"]["images"][:num_images]:
            image_message = {
                "type": "image_url",
                "image_url": {"url": f"data:image/jpeg;base64,{image}"},
            }
            messages.append(image_message)
    text_message = {
        "type": "text",
        "text": (
            "You are an analyst tasked with answering questions about visual content.\n"
            "You will be give a set of image(s) from a slide deck / presentation.\n"
            "Use this information to answer the user question. \n"
            f"User-provided question: {data_dict['question']}\n\n"
        ),
    }
    messages.append(text_message)
    return [HumanMessage(content=messages)]


def multi_modal_rag_chain(retriever):
    """
    Multi-modal RAG chain,

    :param retriever: A function that retrieves the necessary context for the model.
    :return: A chain of functions representing the multi-modal RAG process.
    """
    # Initialize the multi-modal Large Language Model with specific parameters
    model = ChatGoogleGenerativeAI(model="gemini-pro-vision")

    # Define the RAG pipeline
    chain = (
        {
            "context": retriever | RunnableLambda(get_resized_images),
            "question": RunnablePassthrough(),
        }
        | RunnableLambda(img_prompt_func)
        | model
        | StrOutputParser()
    )

    return chain


# Load chroma
vectorstore_mmembd = Chroma(
    collection_name="multi-modal-rag",
    persist_directory=str(Path(__file__).parent.parent / "chroma_db_multi_modal"),
    embedding_function=OpenCLIPEmbeddings(
        model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k"
    ),
)

# Make retriever
retriever_mmembd = vectorstore_mmembd.as_retriever()

# Create RAG chain
chain = multi_modal_rag_chain(retriever_mmembd)


# Add typing for input
class Question(BaseModel):
    __root__: str


chain = chain.with_types(input_type=Question)