图像标题
默认情况下，加载器使用预训练的 Salesforce BLIP 图像标题生成模型。
本笔记本展示了如何使用 ImageCaptionLoader 生成可查询的图像标题索引。
%pip install -qU transformers langchain_openai langchain_chroma
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
准备一份来自维基媒体的图片网址列表
<!--IMPORTS:[{"imported": "ImageCaptionLoader", "source": "langchain_community.document_loaders", "docs": "https://python.langchain.com/api_reference/community/document_loaders/langchain_community.document_loaders.image_captions.ImageCaptionLoader.html", "title": "Image captions"}]-->
from langchain_community.document_loaders import ImageCaptionLoader
list_image_urls = [
"https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Ara_ararauna_Luc_Viatour.jpg/1554px-Ara_ararauna_Luc_Viatour.jpg",
"https://upload.wikimedia.org/wikipedia/commons/thumb/0/0c/1928_Model_A_Ford.jpg/640px-1928_Model_A_Ford.jpg",
]
创建加载器
loader = ImageCaptionLoader(images=list_image_urls)
list_docs = loader.load()
list_docs
[Document(metadata={'image_path': 'https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Ara_ararauna_Luc_Viatour.jpg/1554px-Ara_ararauna_Luc_Viatour.jpg'}, page_content='an image of a bird flying in the air [SEP]'),
Document(metadata={'image_path': 'https://upload.wikimedia.org/wikipedia/commons/thumb/0/0c/1928_Model_A_Ford.jpg/640px-1928_Model_A_Ford.jpg'}, page_content='an image of a vintage car parked on the street [SEP]')]
import requests
from PIL import Image
Image.open(requests.get(list_image_urls[0], stream=True).raw).convert("RGB")
创建索引
<!--IMPORTS:[{"imported": "Chroma", "source": "langchain_chroma", "docs": "https://python.langchain.com/api_reference/chroma/vectorstores/langchain_chroma.vectorstores.Chroma.html", "title": "Image captions"}, {"imported": "OpenAIEmbeddings", "source": "langchain_openai", "docs": "https://python.langchain.com/api_reference/openai/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html", "title": "Image captions"}, {"imported": "RecursiveCharacterTextSplitter", "source": "langchain_text_splitters", "docs": "https://python.langchain.com/api_reference/text_splitters/character/langchain_text_splitters.character.RecursiveCharacterTextSplitter.html", "title": "Image captions"}]-->
from langchain_chroma import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
splits = text_splitter.split_documents(list_docs)
vectorstore = Chroma.from_documents(documents=splits, embedding=OpenAIEmbeddings())
retriever = vectorstore.as_retriever(k=2)
查询
<!--IMPORTS:[{"imported": "create_retrieval_chain", "source": "langchain.chains", "docs": "https://python.langchain.com/api_reference/langchain/chains/langchain.chains.retrieval.create_retrieval_chain.html", "title": "Image captions"}, {"imported": "create_stuff_documents_chain", "source": "langchain.chains.combine_documents", "docs": "https://python.langchain.com/api_reference/langchain/chains/langchain.chains.combine_documents.stuff.create_stuff_documents_chain.html", "title": "Image captions"}, {"imported": "ChatPromptTemplate", "source": "langchain_core.prompts", "docs": "https://python.langchain.com/api_reference/core/prompts/langchain_core.prompts.chat.ChatPromptTemplate.html", "title": "Image captions"}, {"imported": "ChatOpenAI", "source": "langchain_openai", "docs": "https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.base.ChatOpenAI.html", "title": "Image captions"}]-->
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o", temperature=0)
system_prompt = (
"You are an assistant for question-answering tasks. "
"Use the following pieces of retrieved context to answer "
"the question. If you don't know the answer, say that you "
"don't know. Use three sentences maximum and keep the "
"answer concise."
"\n\n"
"{context}"
)
prompt = ChatPromptTemplate.from_messages(
[
("system", system_prompt),
("human", "{input}"),
]
)
question_answer_chain = create_stuff_documents_chain(model, prompt)
rag_chain = create_retrieval_chain(retriever, question_answer_chain)
response = rag_chain.invoke({"input": "What animals are in the images?"})
print(response["answer"])
The images include a bird.
response = rag_chain.invoke({"input": "What kind of images are there?"})
print(response["answer"])
There are images of a bird flying in the air and a vintage car parked on the street.
