mirror of https://github.com/hwchase17/langchain
Multi-modal RAG template (#14186)
* OpenCLIP embeddings * GPT-4V --------- Co-authored-by: Erick Friis <erick@langchain.dev>pull/14626/head
Lance Martin
6 months ago
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GitHub
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MIT License
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Copyright (c) 2023 LangChain, Inc.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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# rag-chroma-multi-modal
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Presentations (slide decks, etc) contain visual content that challenges conventional RAG.
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Multi-modal LLMs unlock new ways to build apps over visual content like presentations.
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This template performs multi-modal RAG using Chroma with multi-modal OpenCLIP embeddings and OpenAI GPT-4V.
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## Input
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Supply a slide deck as pdf in the `/docs` directory.
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Create your vectorstore with:
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```
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poetry install
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python ingest.py
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```
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## Embeddings
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This template will use [OpenCLIP](https://github.com/mlfoundations/open_clip) multi-modal embeddings.
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You can select different options (see results [here](https://github.com/mlfoundations/open_clip/blob/main/docs/openclip_results.csv)).
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The first time you run the app, it will automatically download the multimodal embedding model.
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By default, LangChain will use an embedding model with reasonably strong performance, `ViT-H-14`.
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You can choose alternative `OpenCLIPEmbeddings` models in `rag_chroma_multi_modal/ingest.py`:
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```
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vectorstore_mmembd = Chroma(
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collection_name="multi-modal-rag",
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persist_directory=str(re_vectorstore_path),
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embedding_function=OpenCLIPEmbeddings(
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model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k"
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),
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)
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```
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## LLM
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The app will retrieve images using multi-modal embeddings, and pass them to GPT-4V.
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## Environment Setup
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Set the `OPENAI_API_KEY` environment variable to access the OpenAI GPT-4V.
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## Usage
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To use this package, you should first have the LangChain CLI installed:
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```shell
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pip install -U langchain-cli
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```
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To create a new LangChain project and install this as the only package, you can do:
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```shell
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langchain app new my-app --package rag-chroma-multi-modal
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```
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If you want to add this to an existing project, you can just run:
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```shell
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langchain app add rag-chroma-multi-modal
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```
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And add the following code to your `server.py` file:
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```python
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from rag_chroma import chain as rag_chroma_chain
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add_routes(app, rag_chroma_chain, path="/rag-chroma-multi-modal")
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```
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(Optional) Let's now configure LangSmith.
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LangSmith will help us trace, monitor and debug LangChain applications.
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LangSmith is currently in private beta, you can sign up [here](https://smith.langchain.com/).
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If you don't have access, you can skip this section
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```shell
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export LANGCHAIN_TRACING_V2=true
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export LANGCHAIN_API_KEY=<your-api-key>
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export LANGCHAIN_PROJECT=<your-project> # if not specified, defaults to "default"
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```
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If you are inside this directory, then you can spin up a LangServe instance directly by:
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```shell
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langchain serve
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```
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This will start the FastAPI app with a server is running locally at
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[http://localhost:8000](http://localhost:8000)
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We can see all templates at [http://127.0.0.1:8000/docs](http://127.0.0.1:8000/docs)
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We can access the playground at [http://127.0.0.1:8000/rag-chroma-multi-modal/playground](http://127.0.0.1:8000/rag-chroma-multi-modal/playground)
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We can access the template from code with:
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```python
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from langserve.client import RemoteRunnable
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runnable = RemoteRunnable("http://localhost:8000/rag-chroma-multi-modal")
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```
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import os
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from pathlib import Path
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import pypdfium2 as pdfium
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from langchain.vectorstores import Chroma
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from langchain_experimental.open_clip import OpenCLIPEmbeddings
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def get_images_from_pdf(pdf_path, img_dump_path):
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"""
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Extract images from each page of a PDF document and save as JPEG files.
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:param pdf_path: A string representing the path to the PDF file.
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:param img_dump_path: A string representing the path to dummp images.
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"""
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pdf = pdfium.PdfDocument(pdf_path)
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n_pages = len(pdf)
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for page_number in range(n_pages):
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page = pdf.get_page(page_number)
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bitmap = page.render(scale=1, rotation=0, crop=(0, 0, 0, 0))
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pil_image = bitmap.to_pil()
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pil_image.save(f"{img_dump_path}/img_{page_number + 1}.jpg", format="JPEG")
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# Load PDF
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doc_path = Path(__file__).parent / "docs/DDOG_Q3_earnings_deck.pdf"
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img_dump_path = Path(__file__).parent / "docs/"
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rel_doc_path = doc_path.relative_to(Path.cwd())
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rel_img_dump_path = img_dump_path.relative_to(Path.cwd())
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print("pdf index")
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pil_images = get_images_from_pdf(rel_doc_path, rel_img_dump_path)
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print("done")
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vectorstore = Path(__file__).parent / "chroma_db_multi_modal"
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re_vectorstore_path = vectorstore.relative_to(Path.cwd())
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# Load embedding function
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print("Loading embedding function")
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embedding = OpenCLIPEmbeddings(model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k")
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# Create chroma
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vectorstore_mmembd = Chroma(
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collection_name="multi-modal-rag",
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persist_directory=str(Path(__file__).parent / "chroma_db_multi_modal"),
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embedding_function=embedding,
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)
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# Get image URIs
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image_uris = sorted(
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[
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os.path.join(rel_img_dump_path, image_name)
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for image_name in os.listdir(rel_img_dump_path)
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if image_name.endswith(".jpg")
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]
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)
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# Add images
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print("Embedding images")
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vectorstore_mmembd.add_images(uris=image_uris)
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[tool.poetry]
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name = "rag-chroma-multi-modal"
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version = "0.1.0"
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description = "Multi-modal RAG using Chroma"
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authors = [
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"Lance Martin <lance@langchain.dev>",
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]
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readme = "README.md"
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[tool.poetry.dependencies]
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python = ">=3.8.1,<4.0"
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langchain = ">=0.0.325"
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openai = "<2"
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tiktoken = ">=0.5.1"
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chromadb = ">=0.4.14"
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open-clip-torch = ">=2.23.0"
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torch = ">=2.1.0"
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pypdfium2 = ">=4.20.0"
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langchain-experimental = "^0.0.43"
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[tool.poetry.group.dev.dependencies]
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langchain-cli = ">=0.0.15"
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[tool.langserve]
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export_module = "rag_chroma_multi_modal"
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export_attr = "chain"
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[tool.templates-hub]
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use-case = "rag"
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author = "LangChain"
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integrations = ["OpenAI", "Chroma"]
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tags = ["vectordbs"]
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[build-system]
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requires = [
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"poetry-core",
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]
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build-backend = "poetry.core.masonry.api"
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{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "681a5d1e",
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"metadata": {},
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"source": [
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"## Run Template\n",
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"\n",
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"In `server.py`, set -\n",
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"```\n",
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"add_routes(app, chain_rag_conv, path=\"/rag-chroma-multi-modal\")\n",
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"```"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d774be2a",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langserve.client import RemoteRunnable\n",
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"\n",
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"rag_app = RemoteRunnable(\"http://localhost:8001/rag-chroma-multi-modal\")\n",
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"rag_app.invoke(\"What is the projected TAM for observability expected for each year through 2026?\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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from rag_chroma_multi_modal.chain import chain
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__all__ = ["chain"]
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import base64
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import io
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from pathlib import Path
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from langchain.chat_models import ChatOpenAI
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from langchain.pydantic_v1 import BaseModel
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from langchain.schema.document import Document
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from langchain.schema.messages import HumanMessage
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from langchain.schema.output_parser import StrOutputParser
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from langchain.schema.runnable import RunnableLambda, RunnablePassthrough
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from langchain.vectorstores import Chroma
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from langchain_experimental.open_clip import OpenCLIPEmbeddings
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from PIL import Image
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def resize_base64_image(base64_string, size=(128, 128)):
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"""
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Resize an image encoded as a Base64 string.
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:param base64_string: A Base64 encoded string of the image to be resized.
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:param size: A tuple representing the new size (width, height) for the image.
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:return: A Base64 encoded string of the resized image.
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"""
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img_data = base64.b64decode(base64_string)
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img = Image.open(io.BytesIO(img_data))
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resized_img = img.resize(size, Image.LANCZOS)
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buffered = io.BytesIO()
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resized_img.save(buffered, format=img.format)
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return base64.b64encode(buffered.getvalue()).decode("utf-8")
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def get_resized_images(docs):
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"""
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Resize images from base64-encoded strings.
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:param docs: A list of base64-encoded image to be resized.
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:return: Dict containing a list of resized base64-encoded strings.
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"""
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b64_images = []
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for doc in docs:
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if isinstance(doc, Document):
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doc = doc.page_content
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resized_image = resize_base64_image(doc, size=(1280, 720))
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b64_images.append(resized_image)
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return {"images": b64_images}
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def img_prompt_func(data_dict, num_images=2):
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"""
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GPT-4V prompt for image analysis.
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:param data_dict: A dict with images and a user-provided question.
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:param num_images: Number of images to include in the prompt.
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:return: A list containing message objects for each image and the text prompt.
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"""
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messages = []
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if data_dict["context"]["images"]:
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for image in data_dict["context"]["images"][:num_images]:
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image_message = {
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"type": "image_url",
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"image_url": {"url": f"data:image/jpeg;base64,{image}"},
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}
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messages.append(image_message)
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text_message = {
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"type": "text",
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"text": (
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"You are an analyst tasked with answering questions about visual content.\n"
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"You will be give a set of image(s) from a slide deck / presentation.\n"
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"Use this information to answer the user question. \n"
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f"User-provided question: {data_dict['question']}\n\n"
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),
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}
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messages.append(text_message)
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return [HumanMessage(content=messages)]
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def multi_modal_rag_chain(retriever):
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"""
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Multi-modal RAG chain,
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:param retriever: A function that retrieves the necessary context for the model.
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:return: A chain of functions representing the multi-modal RAG process.
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"""
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# Initialize the multi-modal Large Language Model with specific parameters
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model = ChatOpenAI(temperature=0, model="gpt-4-vision-preview", max_tokens=1024)
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# Define the RAG pipeline
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chain = (
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{
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"context": retriever | RunnableLambda(get_resized_images),
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"question": RunnablePassthrough(),
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}
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| RunnableLambda(img_prompt_func)
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| model
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| StrOutputParser()
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)
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return chain
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# Load chroma
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vectorstore_mmembd = Chroma(
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collection_name="multi-modal-rag",
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persist_directory=str(Path(__file__).parent / "chroma_db_multi_modal"),
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embedding_function=OpenCLIPEmbeddings(
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model_name="ViT-H-14", checkpoint="laion2b_s32b_b79k"
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),
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)
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# Make retriever
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retriever_mmembd = vectorstore_mmembd.as_retriever()
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# Create RAG chain
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chain = multi_modal_rag_chain(retriever_mmembd)
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# Add typing for input
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class Question(BaseModel):
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__root__: str
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chain = chain.with_types(input_type=Question)
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