templates: redis multi-modal multi-vector rag (#18946)

---------

Co-authored-by: Tyler Hutcherson <tyler.hutcherson@redis.com>

templates/rag-redis-multi-modal-multi-vector/.gitignore

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+__pycache__

templates/rag-redis-multi-modal-multi-vector/LICENSE

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+MIT License
+
+Copyright (c) 2024 LangChain, Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

templates/rag-redis-multi-modal-multi-vector/README.md

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+
+# rag-redis-multi-modal-multi-vector
+
+Multi-modal LLMs enable visual assistants that can perform question-answering about images.
+
+This template create a visual assistant for slide decks, which often contain visuals such as graphs or figures.
+
+It uses GPT-4V to create image summaries for each slide, embeds the summaries, and stores them in Redis.
+
+Given a question, relevant slides are retrieved and passed to GPT-4V for answer synthesis.
+
+![](RAG-architecture.png)
+
+## Input
+
+Supply a slide deck as PDF in the `/docs` directory. 
+
+By default, this template has a slide deck about recent earnings from NVIDIA.
+
+Example questions to ask can be:
+```
+1/ how much can H100 TensorRT improve LLama2 inference performance?
+2/ what is the % change in GPU accelerated applications from 2020 to 2023?
+```
+
+To create an index of the slide deck, run:
+```
+poetry install
+poetry shell
+python ingest.py
+```
+
+## Storage
+
+Here is the process the template will use to create an index of the slides (see [blog](https://blog.langchain.dev/multi-modal-rag-template/)):
+
+* Extract the slides as a collection of images
+* Use GPT-4V to summarize each image
+* Embed the image summaries using text embeddings with a link to the original images
+* Retrieve relevant image based on similarity between the image summary and the user input question
+* Pass those images to GPT-4V for answer synthesis
+
+### Redis
+This template uses [Redis](https://redis.com) to power the [MultiVectorRetriever](https://python.langchain.com/docs/modules/data_connection/retrievers/multi_vector) including:
+- Redis as the [VectorStore](https://python.langchain.com/docs/integrations/vectorstores/redis) (to store + index image summary embeddings)
+- Redis as the [ByteStore](https://python.langchain.com/docs/integrations/stores/redis) (to store images)
+
+Make sure to deploy a Redis instance either in the [cloud](https://redis.com/try-free) (free) or locally with [docker](https://redis.io/docs/install/install-stack/docker/).
+
+This will give you an accessible Redis endpoint that you can use as a URL. If deploying locally, simply use `redis://localhost:6379`.
+
+
+## LLM
+
+The app will retrieve images based on similarity between the text input and the image summary (text), and pass the images to GPT-4V for answer synthesis.
+
+## Environment Setup
+
+Set the `OPENAI_API_KEY` environment variable to access the OpenAI GPT-4V.
+
+Set `REDIS_URL` environment variable to access your Redis database.
+
+## Usage
+
+To use this package, you should first have the LangChain CLI installed:
+
+```shell
+pip install -U langchain-cli
+```
+
+To create a new LangChain project and install this as the only package, you can do:
+
+```shell
+langchain app new my-app --package rag-redis-multi-modal-multi-vector
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add rag-redis-multi-modal-multi-vector
+```
+
+And add the following code to your `server.py` file:
+```python
+from rag_redis_multi_modal_multi_vector import chain as rag_redis_multi_modal_chain_mv
+
+add_routes(app, rag_redis_multi_modal_chain_mv, path="/rag-redis-multi-modal-multi-vector")
+```
+
+(Optional) Let's now configure LangSmith. 
+LangSmith will help us trace, monitor and debug LangChain applications. 
+LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/). 
+If you don't have access, you can skip this section
+
+```shell
+export LANGCHAIN_TRACING_V2=true
+export LANGCHAIN_API_KEY=<your-api-key>
+export LANGCHAIN_PROJECT=<your-project>  # if not specified, defaults to "default"
+```
+
+If you are inside this directory, then you can spin up a LangServe instance directly by:
+
+```shell
+langchain serve
+```
+
+This will start the FastAPI app with a server is running locally at 
+[http://localhost:8000](http://localhost:8000)
+
+We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
+We can access the playground at [http://127.0.0.1:8000/rag-redis-multi-modal-multi-vector/playground](http://127.0.0.1:8000/rag-redis-multi-modal-multi-vector/playground)  
+
+We can access the template from code with:
+
+```python
+from langserve.client import RemoteRunnable
+
+runnable = RemoteRunnable("http://localhost:8000/rag-redis-multi-modal-multi-vector")
+```

templates/rag-redis-multi-modal-multi-vector/ingest.py

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+import base64
+import io
+import uuid
+from io import BytesIO
+from pathlib import Path
+
+import pypdfium2 as pdfium
+from langchain_core.documents import Document
+from langchain_core.messages import HumanMessage
+from langchain_openai.chat_models import ChatOpenAI
+from PIL import Image
+from rag_redis_multi_modal_multi_vector.utils import ID_KEY, make_mv_retriever
+
+
+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 = ChatOpenAI(model="gpt-4-vision-preview", max_tokens=1024)
+
+    msg = chat.invoke(
+        [
+            HumanMessage(
+                content=[
+                    {"type": "text", "text": prompt},
+                    {
+                        "type": "image_url",
+                        "image_url": {"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 = """You are an assistant tasked with summarizing images for retrieval. \
+    These summaries will be embedded and used to retrieve the raw image. \
+    Give a concise summary of the image that is well optimized for retrieval."""
+
+    # 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}")  # noqa: T201
+
+    return image_summaries, processed_images
+
+
+def get_images_from_pdf(pdf_path):
+    """
+    Extract images from each page of a PDF document and save as JPEG files.
+
+    :param pdf_path: A string representing the path to the PDF file.
+    """
+    pdf = pdfium.PdfDocument(pdf_path)
+    n_pages = len(pdf)
+    pil_images = []
+    for page_number in range(n_pages):
+        page = pdf.get_page(page_number)
+        bitmap = page.render(scale=1, rotation=0, crop=(0, 0, 0, 0))
+        pil_image = bitmap.to_pil()
+        pil_images.append(pil_image)
+    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=(960, 540))
+    return img_str
+
+
+def load_images(image_summaries, images):
+    """
+    Index image summaries in the db.
+
+    :param image_summaries: Image summaries
+    :param images: Base64 encoded images
+
+    :return: Retriever
+    """
+
+    retriever = make_mv_retriever()
+
+    # 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)
+
+
+if __name__ == "__main__":
+    doc_path = Path(__file__).parent / "docs/nvda-f3q24-investor-presentation-final.pdf"
+    rel_doc_path = doc_path.relative_to(Path.cwd())
+
+    print("Extract slides as images")  # noqa: T201
+    pil_images = get_images_from_pdf(rel_doc_path)
+
+    # Convert to b64
+    images_base_64 = [convert_to_base64(i) for i in pil_images]
+
+    # Generate image summaries
+    print("Generate image summaries")  # noqa: T201
+    image_summaries, images_base_64_processed = generate_img_summaries(images_base_64)
+
+    # Create documents
+    images_base_64_processed_documents = [
+        Document(page_content=i) for i in images_base_64_processed
+    ]
+
+    # Create retriever and load images
+    load_images(image_summaries, images_base_64_processed_documents)

templates/rag-redis-multi-modal-multi-vector/pyproject.toml

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+[tool.poetry]
+name = "rag-redis-multi-modal-multi-vector"
+version = "0.0.1"
+description = "Multi-modal RAG using Redis as the vectorstore and docstore"
+authors = [
+    "Tyler Hutcherson <tyler.hutcherson@redis.com>"
+]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain-core = ">=0.1.5"
+langchain-openai = ">=0.0.1"
+redis = "^5.0.1"
+openai = "<=2.0.0"
+pypdfium2 = "^4.27.0"
+pillow = "^10.2.0"
+
+
+[tool.poetry.group.dev.dependencies]
+langchain-cli = ">=0.0.21"
+fastapi = "^0.104.0"
+sse-starlette = "^1.6.5"
+
+[tool.langserve]
+export_module = "rag_redis_multi_modal_multi_vector"
+export_attr = "chain"
+
+[tool.templates-hub]
+use-case = "rag"
+author = "Redis"
+integrations = ["OpenAI", "Redis"]
+tags = ["vectordbs"]
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

templates/rag-redis-multi-modal-multi-vector/rag_redis_multi_modal_multi_vector/__init__.py

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+from rag_redis_multi_modal_multi_vector.chain import chain
+
+__all__ = ["chain"]

templates/rag-redis-multi-modal-multi-vector/rag_redis_multi_modal_multi_vector/chain.py

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+import base64
+import io
+
+from langchain.pydantic_v1 import BaseModel
+from langchain_core.documents import Document
+from langchain_core.messages import HumanMessage
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnableLambda, RunnablePassthrough
+from langchain_openai import ChatOpenAI
+from PIL import Image
+
+from rag_redis_multi_modal_multi_vector.utils import make_mv_retriever
+
+
+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):
+    """
+    GPT-4V 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]:
+            messages.append(
+                {
+                    "type": "image_url",
+                    "image_url": {"url": f"data:image/jpeg;base64,{image}"},
+                }
+            )
+
+    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():
+    """
+    Multi-modal RAG chain,
+
+    :return: A chain of functions representing the multi-modal RAG process.
+    """
+    # Initialize the multi-modal Large Language Model with specific parameters
+    model = ChatOpenAI(temperature=0, model="gpt-4-vision-preview", max_tokens=1024)
+    # Initialize the retriever
+    retriever = make_mv_retriever()
+    # Define the RAG pipeline
+    return (
+        {
+            "context": retriever | RunnableLambda(get_resized_images),
+            "question": RunnablePassthrough(),
+        }
+        | RunnableLambda(img_prompt_func)
+        | model
+        | StrOutputParser()
+    )
+
+
+# Create RAG chain
+chain = multi_modal_rag_chain()
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)

templates/rag-redis-multi-modal-multi-vector/rag_redis_multi_modal_multi_vector/schema.yml

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+text:
+- name: content
+tag:
+- name: doc_id
+vector:
+- name: content_vector
+  algorithm: FLAT
+  datatype: FLOAT32
+  dims: 1536
+  distance_metric: COSINE

templates/rag-redis-multi-modal-multi-vector/rag_redis_multi_modal_multi_vector/utils.py

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+import os
+
+from langchain.retrievers.multi_vector import MultiVectorRetriever
+from langchain_community.storage import RedisStore
+from langchain_community.vectorstores import Redis as RedisVectorDB
+from langchain_openai.embeddings import OpenAIEmbeddings
+
+ID_KEY = "doc_id"
+
+
+def get_boolean_env_var(var_name, default_value=False):
+    """Retrieve the boolean value of an environment variable.
+
+    Args:
+    var_name (str): The name of the environment variable to retrieve.
+    default_value (bool): The default value to return if the variable
+    is not found.
+
+    Returns:
+    bool: The value of the environment variable, interpreted as a boolean.
+    """
+    true_values = {"true", "1", "t", "y", "yes"}
+    false_values = {"false", "0", "f", "n", "no"}
+
+    # Retrieve the environment variable's value
+    value = os.getenv(var_name, "").lower()
+
+    # Decide the boolean value based on the content of the string
+    if value in true_values:
+        return True
+    elif value in false_values:
+        return False
+    else:
+        return default_value
+
+
+# Check for openai API key
+if "OPENAI_API_KEY" not in os.environ:
+    raise Exception("Must provide an OPENAI_API_KEY as an env var.")
+
+
+def format_redis_conn_from_env() -> str:
+    redis_url = os.getenv("REDIS_URL", None)
+    if redis_url:
+        return redis_url
+    else:
+        using_ssl = get_boolean_env_var("REDIS_SSL", False)
+        start = "rediss://" if using_ssl else "redis://"
+
+        # if using RBAC
+        password = os.getenv("REDIS_PASSWORD", None)
+        username = os.getenv("REDIS_USERNAME", "default")
+        if password is not None:
+            start += f"{username}:{password}@"
+
+        host = os.getenv("REDIS_HOST", "localhost")
+        port = int(os.getenv("REDIS_PORT", 6379))
+
+        return start + f"{host}:{port}"
+
+
+REDIS_URL = format_redis_conn_from_env()
+
+current_file_path = os.path.abspath(__file__)
+parent_dir = os.path.dirname(current_file_path)
+schema_path = os.path.join(parent_dir, "schema.yml")
+INDEX_SCHEMA = schema_path
+
+
+def make_mv_retriever():
+    """Create the multi-vector retriever"""
+    # Load Redis
+    REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379")
+    vectorstore = RedisVectorDB(
+        redis_url=REDIS_URL,
+        index_name="image_summaries",
+        key_prefix="summary",
+        index_schema=INDEX_SCHEMA,
+        embedding=OpenAIEmbeddings(),
+    )
+    store = RedisStore(redis_url=REDIS_URL, namespace="image")
+
+    # Create the multi-vector retriever
+    return MultiVectorRetriever(
+        vectorstore=vectorstore,
+        byte_store=store,
+        id_key=ID_KEY,
+    )