Semi-structured Image Retrieval¶

In this notebook we show you how to perform semi-structured retrieval over images.

Given a set of images, we can infer structured outputs from them using Gemini Pro Vision.

We can then index these structured outputs in a vector database. We then take full advantage of semantic search + metadata filter capabilities with auto-retrieval: this allows us to ask both structured and semantic questions over this data!

(An alternative is to put this data into a SQL database, letting you do text-to-SQL. These techniques are quite closely related).

In [ ]:

%pip install llama-index-multi-modal-llms-gemini
%pip install llama-index-vector-stores-qdrant
%pip install llama-index-embeddings-gemini
%pip install llama-index-llms-gemini

%pip install llama-index-multi-modal-llms-gemini %pip install llama-index-vector-stores-qdrant %pip install llama-index-embeddings-gemini %pip install llama-index-llms-gemini

In [ ]:

!pip install llama-index 'google-generativeai>=0.3.0' matplotlib qdrant_client

!pip install llama-index 'google-generativeai>=0.3.0' matplotlib qdrant_client

Setup¶

Get Google API Key¶

In [ ]:

import os

GOOGLE_API_KEY = ""  # add your GOOGLE API key here
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY

import os GOOGLE_API_KEY = "" # add your GOOGLE API key here os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY

Download Images¶

We download the full SROIE v2 dataset from Kaggle here.

This dataset consists of scanned receipt images. We ignore the ground-truth labels for now, and use the test set images to test out Gemini's capabilities for structured output extraction.

Get Image Files¶

Now that the images are downloaded, we can get a list of the file names.

In [ ]:

from pathlib import Path
import random
from typing import Optional

from pathlib import Path import random from typing import Optional

In [ ]:

def get_image_files(
    dir_path, sample: Optional[int] = 10, shuffle: bool = False
):
    dir_path = Path(dir_path)
    image_paths = []
    for image_path in dir_path.glob("*.jpg"):
        image_paths.append(image_path)

    random.shuffle(image_paths)
    if sample:
        return image_paths[:sample]
    else:
        return image_paths

def get_image_files( dir_path, sample: Optional[int] = 10, shuffle: bool = False ): dir_path = Path(dir_path) image_paths = [] for image_path in dir_path.glob("*.jpg"): image_paths.append(image_path) random.shuffle(image_paths) if sample: return image_paths[:sample] else: return image_paths

In [ ]:

image_files = get_image_files("SROIE2019/test/img", sample=100)

image_files = get_image_files("SROIE2019/test/img", sample=100)

Use Gemini to extract structured outputs¶

Here we use Gemini to extract structured outputs.

1. Define a ReceiptInfo pydantic class that captures the structured outputs we want to extract. We extract fields like company, date, total, and also summary.
2. Define a pydantic_gemini function which will convert input documents into a response.

Define a ReceiptInfo pydantic class¶

In [ ]:

from pydantic import BaseModel, Field


class ReceiptInfo(BaseModel):
    company: str = Field(..., description="Company name")
    date: str = Field(..., description="Date field in DD/MM/YYYY format")
    address: str = Field(..., description="Address")
    total: float = Field(..., description="total amount")
    currency: str = Field(
        ..., description="Currency of the country (in abbreviations)"
    )
    summary: str = Field(
        ...,
        description="Extracted text summary of the receipt, including items purchased, the type of store, the location, and any other notable salient features (what does the purchase seem to be for?).",
    )

from pydantic import BaseModel, Field class ReceiptInfo(BaseModel): company: str = Field(..., description="Company name") date: str = Field(..., description="Date field in DD/MM/YYYY format") address: str = Field(..., description="Address") total: float = Field(..., description="total amount") currency: str = Field( ..., description="Currency of the country (in abbreviations)" ) summary: str = Field( ..., description="Extracted text summary of the receipt, including items purchased, the type of store, the location, and any other notable salient features (what does the purchase seem to be for?).", )

Define a pydantic_gemini function¶

In [ ]:

from llama_index.multi_modal_llms.gemini import GeminiMultiModal
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser

prompt_template_str = """\
    Can you summarize the image and return a response \
    with the following JSON format: \
"""


async def pydantic_gemini(output_class, image_documents, prompt_template_str):
    gemini_llm = GeminiMultiModal(
        api_key=GOOGLE_API_KEY, model_name="models/gemini-pro-vision"
    )

    llm_program = MultiModalLLMCompletionProgram.from_defaults(
        output_parser=PydanticOutputParser(output_class),
        image_documents=image_documents,
        prompt_template_str=prompt_template_str,
        multi_modal_llm=gemini_llm,
        verbose=True,
    )

    response = await llm_program.acall()
    return response

from llama_index.multi_modal_llms.gemini import GeminiMultiModal from llama_index.core.program import MultiModalLLMCompletionProgram from llama_index.core.output_parsers import PydanticOutputParser prompt_template_str = """\ Can you summarize the image and return a response \ with the following JSON format: \ """ async def pydantic_gemini(output_class, image_documents, prompt_template_str): gemini_llm = GeminiMultiModal( api_key=GOOGLE_API_KEY, model_name="models/gemini-pro-vision" ) llm_program = MultiModalLLMCompletionProgram.from_defaults( output_parser=PydanticOutputParser(output_class), image_documents=image_documents, prompt_template_str=prompt_template_str, multi_modal_llm=gemini_llm, verbose=True, ) response = await llm_program.acall() return response

Run over images¶

In [ ]:

from llama_index.core import SimpleDirectoryReader
from llama_index.core.async_utils import run_jobs


async def aprocess_image_file(image_file):
    # should load one file
    print(f"Image file: {image_file}")
    img_docs = SimpleDirectoryReader(input_files=[image_file]).load_data()
    output = await pydantic_gemini(ReceiptInfo, img_docs, prompt_template_str)
    return output


async def aprocess_image_files(image_files):
    """Process metadata on image files."""

    new_docs = []
    tasks = []
    for image_file in image_files:
        task = aprocess_image_file(image_file)
        tasks.append(task)

    outputs = await run_jobs(tasks, show_progress=True, workers=5)
    return outputs

from llama_index.core import SimpleDirectoryReader from llama_index.core.async_utils import run_jobs async def aprocess_image_file(image_file): # should load one file print(f"Image file: {image_file}") img_docs = SimpleDirectoryReader(input_files=[image_file]).load_data() output = await pydantic_gemini(ReceiptInfo, img_docs, prompt_template_str) return output async def aprocess_image_files(image_files): """Process metadata on image files.""" new_docs = [] tasks = [] for image_file in image_files: task = aprocess_image_file(image_file) tasks.append(task) outputs = await run_jobs(tasks, show_progress=True, workers=5) return outputs

In [ ]:

outputs = await aprocess_image_files(image_files)

outputs = await aprocess_image_files(image_files)

In [ ]:

outputs[4]

outputs[4]

Out[ ]:

ReceiptInfo(company='KEDAI BUKU NEW ACHIEVERS', date='15/09/2017', address='NO. 12 & 14, JALAN HIJAUAN JINANG 27/54 TAMAN ALAM MEGAH, SEKSYEN 27 40400 SHAH ALAM, SELANGOR D. E.', total=48.0, currency='MYR', summary='Purchase of books and school supplies at a bookstore.')

Convert Structured Representation to TextNode objects¶

Node objects are the core units that are indexed in vector stores in LlamaIndex. We define a simple converter function to map the ReceiptInfo objects to TextNode objects.

In [ ]:

from llama_index.core.schema import TextNode
from typing import List


def get_nodes_from_objs(
    objs: List[ReceiptInfo], image_files: List[str]
) -> TextNode:
    """Get nodes from objects."""
    nodes = []
    for image_file, obj in zip(image_files, objs):
        node = TextNode(
            text=obj.summary,
            metadata={
                "company": obj.company,
                "date": obj.date,
                "address": obj.address,
                "total": obj.total,
                "currency": obj.currency,
                "image_file": str(image_file),
            },
            excluded_embed_metadata_keys=["image_file"],
            excluded_llm_metadata_keys=["image_file"],
        )
        nodes.append(node)
    return nodes

from llama_index.core.schema import TextNode from typing import List def get_nodes_from_objs( objs: List[ReceiptInfo], image_files: List[str] ) -> TextNode: """Get nodes from objects.""" nodes = [] for image_file, obj in zip(image_files, objs): node = TextNode( text=obj.summary, metadata={ "company": obj.company, "date": obj.date, "address": obj.address, "total": obj.total, "currency": obj.currency, "image_file": str(image_file), }, excluded_embed_metadata_keys=["image_file"], excluded_llm_metadata_keys=["image_file"], ) nodes.append(node) return nodes

In [ ]:

nodes = get_nodes_from_objs(outputs, image_files)

nodes = get_nodes_from_objs(outputs, image_files)

In [ ]:

print(nodes[0].get_content(metadata_mode="all"))

print(nodes[0].get_content(metadata_mode="all"))

company: UNIHAIKKA INTERNATIONAL SDN BHD
date: 13/09/2018
address: 12, Jalan Tampoi 7/4, Kawasan Perindustrian Tampoi, 81200 Johor Bahru, Johor
total: 8.85
currency: MYR
image_file: SROIE2019/test/img/X51007846371.jpg

The receipt is from a restaurant called Bar Wang Rice. The total amount is 8.85 MYR. The items purchased include chicken, vegetables, and a drink.

Index these nodes in vector stores¶

In [ ]:

import qdrant_client
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.embeddings.gemini import GeminiEmbedding
from llama_index.llms.gemini import Gemini
from llama_index.core import Settings

# Create a local Qdrant vector store
client = qdrant_client.QdrantClient(path="qdrant_gemini")

vector_store = QdrantVectorStore(client=client, collection_name="collection")

# global settings
Settings.embed_model = GeminiEmbedding(
    model_name="models/embedding-001", api_key=GOOGLE_API_KEY
)
Settings.llm = (Gemini(api_key=GOOGLE_API_KEY),)

storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = VectorStoreIndex(
    nodes=nodes,
    storage_context=storage_context,
)

import qdrant_client from llama_index.vector_stores.qdrant import QdrantVectorStore from llama_index.core import StorageContext from llama_index.core import VectorStoreIndex from llama_index.embeddings.gemini import GeminiEmbedding from llama_index.llms.gemini import Gemini from llama_index.core import Settings # Create a local Qdrant vector store client = qdrant_client.QdrantClient(path="qdrant_gemini") vector_store = QdrantVectorStore(client=client, collection_name="collection") # global settings Settings.embed_model = GeminiEmbedding( model_name="models/embedding-001", api_key=GOOGLE_API_KEY ) Settings.llm = (Gemini(api_key=GOOGLE_API_KEY),) storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex( nodes=nodes, storage_context=storage_context, )

Define Auto-Retriever¶

Now we can setup our auto-retriever, which can perform semi-structured queries: structured queries through inferring metadata filters, along with semantic search.

We setup our schema definition capturing the receipt info which is fed into the prompt.

In [ ]:

from llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo


vector_store_info = VectorStoreInfo(
    content_info="Receipts",
    metadata_info=[
        MetadataInfo(
            name="company",
            description="The name of the store",
            type="string",
        ),
        MetadataInfo(
            name="address",
            description="The address of the store",
            type="string",
        ),
        MetadataInfo(
            name="date",
            description="The date of the purchase (in DD/MM/YYYY format)",
            type="string",
        ),
        MetadataInfo(
            name="total",
            description="The final amount",
            type="float",
        ),
        MetadataInfo(
            name="currency",
            description="The currency of the country the purchase was made (abbreviation)",
            type="string",
        ),
    ],
)

from llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo vector_store_info = VectorStoreInfo( content_info="Receipts", metadata_info=[ MetadataInfo( name="company", description="The name of the store", type="string", ), MetadataInfo( name="address", description="The address of the store", type="string", ), MetadataInfo( name="date", description="The date of the purchase (in DD/MM/YYYY format)", type="string", ), MetadataInfo( name="total", description="The final amount", type="float", ), MetadataInfo( name="currency", description="The currency of the country the purchase was made (abbreviation)", type="string", ), ], )

In [ ]:

from llama_index.core.retrievers import VectorIndexAutoRetriever

retriever = VectorIndexAutoRetriever(
    index,
    vector_store_info=vector_store_info,
    similarity_top_k=2,
    empty_query_top_k=10,  # if only metadata filters are specified, this is the limit
    verbose=True,
)

from llama_index.core.retrievers import VectorIndexAutoRetriever retriever = VectorIndexAutoRetriever( index, vector_store_info=vector_store_info, similarity_top_k=2, empty_query_top_k=10, # if only metadata filters are specified, this is the limit verbose=True, )

In [ ]:

# from PIL import Image
import requests
from io import BytesIO
import matplotlib.pyplot as plt
from IPython.display import Image


def display_response(nodes: List[TextNode]):
    """Display response."""
    for node in nodes:
        print(node.get_content(metadata_mode="all"))
        # img = Image.open(open(node.metadata["image_file"], 'rb'))
        display(Image(filename=node.metadata["image_file"], width=200))

# from PIL import Image import requests from io import BytesIO import matplotlib.pyplot as plt from IPython.display import Image def display_response(nodes: List[TextNode]): """Display response.""" for node in nodes: print(node.get_content(metadata_mode="all")) # img = Image.open(open(node.metadata["image_file"], 'rb')) display(Image(filename=node.metadata["image_file"], width=200))

Run Some Queries¶

Let's try out different types of queries!

In [ ]:

nodes = retriever.retrieve(
    "Tell me about some restaurant orders of noodles with total < 25"
)
display_response(nodes)

nodes = retriever.retrieve( "Tell me about some restaurant orders of noodles with total < 25" ) display_response(nodes)

Using query str: restaurant orders of noodles
Using filters: [('total', '<', 25)]
company: Restoran Wan Sheng
date: 23-03-2018
address: No. 2, Jalan Temenggung 19/9, Seksyen 9, Bandar Mahkota Cheras, 43200 Cheras, Selangor
total: 6.7
currency: MYR
image_file: SROIE2019/test/img/X51005711443.jpg

Teh (B), Cham (B), Bunga Kekwa, Take Away

company: UNIHAIKKA INTERNATIONAL SDN BHD
date: 19/06/2018
address: 12, Jalan Tampoi 7/4, Kawasan Perindustrian Tampoi 81200 Johor Bahru, Johor
total: 8.45
currency: MYR
image_file: SROIE2019/test/img/X51007846392.jpg

The receipt is from a restaurant called Bar Wang Rice. The total amount is 8.45 MYR. The items purchased include 1 plate of fried noodles, 1 plate of chicken, and 1 plate of vegetables.

In [ ]:

nodes = retriever.retrieve("Tell me about some grocery purchases")
display_response(nodes)

nodes = retriever.retrieve("Tell me about some grocery purchases") display_response(nodes)

Using query str: grocery purchases
Using filters: []
company: GARDENIA BAKERIES (KL) SDN BHD
date: 24/09/2017
address: LOT 3, JALAN PELABUR 23/1, 40300 SHAH ALAM, SELANGOR
total: 38.55
currency: RM
image_file: SROIE2019/test/img/X51006556829.jpg

Purchase of groceries from a supermarket.

company: Segi Cash & Carry Sdn. Bhd
date: 02/02/2017
address: PT17920, SEKSYEN U9,
40150 SHAH ALAM,
SELANGOR DARUL EHSAN
total: 27.0
currency: RM
image_file: SROIE2019/test/img/X51006335818.jpg

Purchase of groceries at Segi Cash & Carry Sdn. Bhd. on 02/02/2017. The total amount of the purchase is RM27.