Template for multi-modal w/ multi-vector (#14618)

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

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+docs/img_*.jpg

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

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+MIT License
+
+Copyright (c) 2023 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-chroma-multi-modal-multi-vector/README.md

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+
+# rag-chroma-multi-modal-multi-vector
+
+Presentations (slide decks, etc) contain visual content that challenges conventional RAG.
+
+Multi-modal LLMs unlock new ways to build apps over visual content like presentations.
+ 
+This template performs multi-modal RAG using Chroma with the multi-vector retriever (see [blog](https://blog.langchain.dev/multi-modal-rag-template/)):
+
+* Extracts the slides as images
+* Uses GPT-4V to summarize each image
+* Embeds the image summaries with a link to the original images
+* Retrieves relevant image based on similarity between the image summary and the user input
+* Finally pass those images to GPT-4V for answer synthesis
+
+## Storage
+
+We will use Upstash to store the images, which offers Redis with a REST API.
+
+Simply login [here](https://upstash.com/) and create a database.
+
+This will give you a REST API with:
+
+* UPSTASH_URL
+* UPSTASH_TOKEN
+
+Set `UPSTASH_URL` and `UPSTASH_TOKEN` as environment variables to access your database.
+
+We will use Chroma to store and index the image summaries, which will be created locally in the template directory.
+
+## Input
+
+Supply a slide deck as pdf in the `/docs` directory. 
+
+Create your vectorstore (Chroma) and populae Upstash with: 
+
+```
+poetry install
+python ingest.py
+```
+
+## LLM
+
+The app will retrieve images using multi-modal embeddings, and pass them to GPT-4V.
+
+## Environment Setup
+
+Set the `OPENAI_API_KEY` environment variable to access the OpenAI GPT-4V.
+
+Set `UPSTASH_URL` and `UPSTASH_TOKEN` as environment variables to access your 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-chroma-multi-modal-multi-vector
+```
+
+If you want to add this to an existing project, you can just run:
+
+```shell
+langchain app add rag-chroma-multi-modal-multi-vector
+```
+
+And add the following code to your `server.py` file:
+```python
+from rag_chroma_multi_modal_multi_vector import chain as rag_chroma_multi_modal_chain_mv
+
+add_routes(app, rag_chroma_multi_modal_chain_mv, path="/rag-chroma-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-chroma-multi-modal-multi-vector/playground](http://127.0.0.1:8000/rag-chroma-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-chroma-multi-modal-multi-vector")
+```

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

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+import base64
+import io
+import os
+import uuid
+from io import BytesIO
+from pathlib import Path
+
+import pypdfium2 as pdfium
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.retrievers.multi_vector import MultiVectorRetriever
+from langchain.schema.document import Document
+from langchain.schema.messages import HumanMessage
+from langchain.storage import UpstashRedisByteStore
+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 = 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}")
+
+    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 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
+    UPSTASH_URL = os.getenv("UPSTASH_URL")
+    UPSTASH_TOKEN = os.getenv("UPSTASH_TOKEN")
+    store = UpstashRedisByteStore(url=UPSTASH_URL, token=UPSTASH_TOKEN)
+    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 PDF
+doc_path = Path(__file__).parent / "docs/DDOG_Q3_earnings_deck.pdf"
+rel_doc_path = doc_path.relative_to(Path.cwd())
+print("Extract slides as images")
+pil_images = get_images_from_pdf(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=OpenAIEmbeddings(),
+)
+
+# 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,
+)

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

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+[tool.poetry]
+name = "rag-chroma-multi-modal-multi-vector"
+version = "0.1.0"
+description = "Multi-modal RAG using Chroma and multi-vector retriever"
+authors = [
+    "Lance Martin <lance@langchain.dev>",
+]
+readme = "README.md"
+
+[tool.poetry.dependencies]
+python = ">=3.8.1,<4.0"
+langchain = ">=0.0.350"
+openai = "<2"
+tiktoken = ">=0.5.1"
+chromadb = ">=0.4.14"
+pypdfium2 = ">=4.20.0"
+langchain-experimental = "^0.0.43"
+upstash-redis = ">=1.0.0"
+pillow = ">=10.1.0"
+
+[tool.poetry.group.dev.dependencies]
+langchain-cli = ">=0.0.15"
+
+[tool.langserve]
+export_module = "rag_chroma_multi_modal_multi_vector"
+export_attr = "chain"
+
+[tool.templates-hub]
+use-case = "rag"
+author = "LangChain"
+integrations = ["OpenAI", "Chroma"]
+tags = ["vectordbs"]
+
+[build-system]
+requires = [
+    "poetry-core",
+]
+build-backend = "poetry.core.masonry.api"

templates/rag-chroma-multi-modal-multi-vector/rag_chroma_multi_modal_multi_vector.ipynb

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+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "681a5d1e",
+   "metadata": {},
+   "source": [
+    "## Run Template\n",
+    "\n",
+    "In `server.py`, set -\n",
+    "```\n",
+    "add_routes(app, chain_rag_conv, path=\"/rag-chroma-multi-modal-multi-vector\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d774be2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langserve.client import RemoteRunnable\n",
+    "\n",
+    "rag_app = RemoteRunnable(\"http://localhost:8001/rag-chroma-multi-modal-multi-vector\")\n",
+    "rag_app.invoke(\"What is the projected TAM for observability expected for each year through 2026?\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

templates/rag-chroma-multi-modal-multi-vector/rag_chroma_multi_modal_multi_vector/__init__.py

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

templates/rag-chroma-multi-modal-multi-vector/rag_chroma_multi_modal_multi_vector/chain.py

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+import base64
+import io
+import os
+from pathlib import Path
+
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.pydantic_v1 import BaseModel
+from langchain.retrievers.multi_vector import MultiVectorRetriever
+from langchain.schema.document import Document
+from langchain.schema.messages import HumanMessage
+from langchain.schema.output_parser import StrOutputParser
+from langchain.schema.runnable import RunnableLambda, RunnablePassthrough
+from langchain.storage import UpstashRedisByteStore
+from langchain.vectorstores import Chroma
+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):
+    """
+    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]:
+            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 = ChatOpenAI(temperature=0, model="gpt-4-vision-preview", max_tokens=1024)
+
+    # Define the RAG pipeline
+    chain = (
+        {
+            "context": retriever | RunnableLambda(get_resized_images),
+            "question": RunnablePassthrough(),
+        }
+        | RunnableLambda(img_prompt_func)
+        | model
+        | StrOutputParser()
+    )
+
+    return chain
+
+
+# Load chroma
+vectorstore_mvr = Chroma(
+    collection_name="image_summaries",
+    persist_directory=str(Path(__file__).parent.parent / "chroma_db_multi_modal"),
+    embedding_function=OpenAIEmbeddings(),
+)
+
+# Load redis
+UPSTASH_URL = os.getenv("UPSTASH_URL")
+UPSTASH_TOKEN = os.getenv("UPSTASH_TOKEN")
+store = UpstashRedisByteStore(url=UPSTASH_URL, token=UPSTASH_TOKEN)
+id_key = "doc_id"
+
+# Create the multi-vector retriever
+retriever = MultiVectorRetriever(
+    vectorstore=vectorstore_mvr,
+    byte_store=store,
+    id_key=id_key,
+)
+
+# Create RAG chain
+chain = multi_modal_rag_chain(retriever)
+
+
+# Add typing for input
+class Question(BaseModel):
+    __root__: str
+
+
+chain = chain.with_types(input_type=Question)