Gemini multi-modal RAG template (#14678)

![Screenshot 2023-12-13 at 12 53 39
PM](https://github.com/langchain-ai/langchain/assets/122662504/a6bc3b0b-f177-4367-b9c8-b8862c847026)

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

@@ -0,0 +1,122 @@
+import base64
+import io
+from pathlib import Path
+
+from langchain.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)