templates: Add Ollama multi-modal templates (#14868)

Templates for [local multi-modal
LLMs](https://llava-vl.github.io/llava-interactive/) using -
* Image summaries
* Multi-modal embeddings

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

templates/rag-multi-modal-mv-local/ingest.py

@@ -0,0 +1,192 @@
+import base64
+import io
+import os
+import uuid
+from io import BytesIO
+from pathlib import Path
+
+from langchain.chat_models import ChatOllama
+from langchain.embeddings import OllamaEmbeddings
+from langchain.retrievers.multi_vector import MultiVectorRetriever
+from langchain.schema.document import Document
+from langchain.schema.messages import HumanMessage
+from langchain.storage import LocalFileStore
+from langchain.vectorstores import Chroma
+from PIL import Image
+
+
+def image_summarize(img_base64, prompt):
+    """
+    Make image summary
+
+    :param img_base64: Base64 encoded string for image
+    :param prompt: Text prompt for summarizatiomn
+    :return: Image summarization prompt
+
+    """
+    chat = ChatOllama(model="bakllava", temperature=0)
+
+    msg = chat.invoke(
+        [
+            HumanMessage(
+                content=[
+                    {"type": "text", "text": prompt},
+                    {
+                        "type": "image_url",
+                        "image_url": f"data:image/jpeg;base64,{img_base64}",
+                    },
+                ]
+            )
+        ]
+    )
+    return msg.content
+
+
+def generate_img_summaries(img_base64_list):
+    """
+    Generate summaries for images
+
+    :param img_base64_list: Base64 encoded images
+    :return: List of image summaries and processed images
+    """
+
+    # Store image summaries
+    image_summaries = []
+    processed_images = []
+
+    # Prompt
+    prompt = """Give a detailed summary of the image."""
+
+    # Apply summarization to images
+    for i, base64_image in enumerate(img_base64_list):
+        try:
+            image_summaries.append(image_summarize(base64_image, prompt))
+            processed_images.append(base64_image)
+        except Exception as e:
+            print(f"Error with image {i+1}: {e}")
+
+    return image_summaries, processed_images
+
+
+def get_images(img_path):
+    """
+    Extract images.
+
+    :param img_path: A string representing the path to the images.
+    """
+    # Get image URIs
+    pil_images = [
+        Image.open(os.path.join(img_path, image_name))
+        for image_name in os.listdir(img_path)
+        if image_name.endswith(".jpg")
+    ]
+    return pil_images
+
+
+def resize_base64_image(base64_string, size=(128, 128)):
+    """
+    Resize an image encoded as a Base64 string
+
+    :param base64_string: Base64 string
+    :param size: Image size
+    :return: Re-sized Base64 string
+    """
+    # Decode the Base64 string
+    img_data = base64.b64decode(base64_string)
+    img = Image.open(io.BytesIO(img_data))
+
+    # Resize the image
+    resized_img = img.resize(size, Image.LANCZOS)
+
+    # Save the resized image to a bytes buffer
+    buffered = io.BytesIO()
+    resized_img.save(buffered, format=img.format)
+
+    # Encode the resized image to Base64
+    return base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+
+def convert_to_base64(pil_image):
+    """
+    Convert PIL images to Base64 encoded strings
+
+    :param pil_image: PIL image
+    :return: Re-sized Base64 string
+    """
+
+    buffered = BytesIO()
+    pil_image.save(buffered, format="JPEG")  # You can change the format if needed
+    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+    # img_str = resize_base64_image(img_str, size=(831,623))
+    return img_str
+
+
+def create_multi_vector_retriever(vectorstore, image_summaries, images):
+    """
+    Create retriever that indexes summaries, but returns raw images or texts
+
+    :param vectorstore: Vectorstore to store embedded image sumamries
+    :param image_summaries: Image summaries
+    :param images: Base64 encoded images
+    :return: Retriever
+    """
+
+    # Initialize the storage layer for images
+    store = LocalFileStore(
+        str(Path(__file__).parent / "multi_vector_retriever_metadata")
+    )
+    id_key = "doc_id"
+
+    # Create the multi-vector retriever
+    retriever = MultiVectorRetriever(
+        vectorstore=vectorstore,
+        byte_store=store,
+        id_key=id_key,
+    )
+
+    # Helper function to add documents to the vectorstore and docstore
+    def add_documents(retriever, doc_summaries, doc_contents):
+        doc_ids = [str(uuid.uuid4()) for _ in doc_contents]
+        summary_docs = [
+            Document(page_content=s, metadata={id_key: doc_ids[i]})
+            for i, s in enumerate(doc_summaries)
+        ]
+        retriever.vectorstore.add_documents(summary_docs)
+        retriever.docstore.mset(list(zip(doc_ids, doc_contents)))
+
+    add_documents(retriever, image_summaries, images)
+
+    return retriever
+
+
+# Load images
+doc_path = Path(__file__).parent / "docs/"
+rel_doc_path = doc_path.relative_to(Path.cwd())
+print("Read images")
+pil_images = get_images(rel_doc_path)
+
+# Convert to b64
+images_base_64 = [convert_to_base64(i) for i in pil_images]
+
+# Image summaries
+print("Generate image summaries")
+image_summaries, images_base_64_processed = generate_img_summaries(images_base_64)
+
+# The vectorstore to use to index the images summaries
+vectorstore_mvr = Chroma(
+    collection_name="image_summaries",
+    persist_directory=str(Path(__file__).parent / "chroma_db_multi_modal"),
+    embedding_function=OllamaEmbeddings(model="llama2:7b"),
+)
+
+# Create documents
+images_base_64_processed_documents = [
+    Document(page_content=i) for i in images_base_64_processed
+]
+
+# Create retriever
+retriever_multi_vector_img = create_multi_vector_retriever(
+    vectorstore_mvr,
+    image_summaries,
+    images_base_64_processed_documents,
+)