Leveraging Google Task Type Embeddings for Enhanced Retrieval-Augmented Generation(RAG)

Introduction

In machine learning and natural language processing (NLP), effectively capturing the semantic essence of data is crucial for tasks like search, recommendation systems, and conversational AI. Traditional semantic similarity models often need to catch up when applied to tasks such as question-answering due to their inability to capture nuanced relationships between queries and responses. This article delves into how Google Task Type embeddings, introduced via the Vertex AI Embeddings API, provide a solution to this problem. We'll explore core concepts like embeddings, cosine similarity, and mean reciprocal rank (MRR), emphasizing how Google Task Type embeddings differ from others through simple, real-world examples.

Setting the Problem Context

Why Standard Semantic Similarity Fails for Certain Tasks

Text embeddings are commonly used for semantic similarity searches in Retrieval-Augmented Generation (RAG) systems designed to fetch information based on user queries. These embeddings measure how closely two pieces of text are related in meaning. While effective for general text retrieval, they often struggle with question-answering tasks.

Consider the question, "How does photosynthesis occur in plants?" A correct answer might be, "Plants use sunlight to convert carbon dioxide and water into glucose and oxygen." Semantically, the question and the answer share a few common words or phrases. A standard semantic similarity model might mistakenly prioritize texts containing words like "photosynthesis" or "plants" without capturing the underlying explanatory relationship. This happens because the model focuses on surface-level similarities rather than the deeper connection between the question and its answer.

Another example is a query,?"What are effective ways to improve sleep quality?" A system optimized for semantic similarity might retrieve texts that include phrases like "improve sleep" or "sleep quality" but fail to rank suggestions like "maintain a regular sleep schedule" or "reduce screen time before bed" highly, as these do not semantically align closely with the query text.

Introducing Google Task Type Embeddings as a Solution

Google introduced?task-type embeddings?through the Vertex AI Embeddings API to address these limitations. These embeddings allow developers to specify the task type—such as QUESTION_ANSWERING, RETRIEVAL_DOCUMENT, or SUMMARIZATION—when generating embeddings for text data.

By specifying the task type, these embeddings adjust the vector space so that related questions and answers are positioned closer together, even if they are not semantically similar in the traditional sense. For example, defining a question with the QUESTION_ANSWERING task type and an answer with the RETRIEVAL_DOCUMENT task type helps the model understand the relationship between the two more effectively. This improves search quality for RAG systems, as embeddings are optimized to capture the specific relationships pertinent to the task.

Core Concepts

Understanding these foundational concepts is essential for implementing a system that leverages Google Task Type embeddings for enhanced retrieval.

1. Embeddings

Definition: Embeddings are numerical data representations in a high—dimensional vector space, such as text, images, or other entities. They capture the semantic meaning and contextual relationships between different pieces of data.

Google-Supported Task Types for Embeddings

Google's Vertex AI Embeddings API supports various task types that optimize embeddings for specific applications. Each task type tailors the embeddings to capture the most relevant features for that task, improving performance and accuracy.

Let's explore these task types, their descriptions, use cases, and relevant examples.

SEMANTIC_SIMILARITY

• Description: Embeddings optimized for retrieving texts that are semantically similar.
• Use Case: Semantic search in text-based applications.
• Example: Comparing the sentences "The stock market is rising today" and "Shares are up in the financial markets," which would be embedded closely due to their similar meanings.

RETRIEVAL_QUERY

• Description: Embeddings designed for queries in document search and information retrieval systems.
• Use Case: Building search engines or indexing systems.
• Example: A user searches "affordable electric cars," the query is embedded to retrieve relevant documents about budget-friendly electric vehicles.

RETRIEVAL_DOCUMENT

• Description: Embeddings for documents meant to be retrieved in response to queries.
• Use Case: Part of retrieval-based systems such as semantic search or FAQ lookups.
• Example: An article titled "Top 10 Electric Cars Under $30,000" is embedded to match queries about affordable electric cars.

QUESTION_ANSWERING

• Description: Embeddings optimized for understanding and answering questions.
• Use Case: Chatbots, RAG systems, and FAQ systems.
• Example: The embedding captures the intent to seek password reset instructions for the question "How to reset my password?"

FACT_VERIFICATION

• Description: Embeddings are designed to verify facts using related documents.
• Use Case: Fact-checking applications or knowledge verification systems.
• Example: Validating the statement "Vitamin C cures the common cold" by retrieving scientific studies or articles.

CODE_RETRIEVAL_QUERY

• Description: Retrieves code blocks based on plain-text queries (available in specific models).
• Use Case: Code search tools for programming languages like Java or Python.
• Example: A developer searches "how to sort a list in Python," and the system retrieves relevant code snippets.

CLASSIFICATION

• Description: Text embeddings optimized for classification tasks.
• Use Case: Spam detection, sentiment analysis, or categorizing customer feedback.
• Example: Classifying the review "The product exceeded my expectations" as a positive sentiment.

CLUSTERING

• Description: Embeddings optimized for clustering similar text data.
• Use Case: Grouping documents or customer feedback into clusters for analysis.
• Example: Clustering customer complaints about "delivery issues" and "product defects" into separate groups.

Why Are Google Task Type Embeddings Critical?

• Task-Specific Optimization: By specifying the task type, embeddings are fine-tuned to capture the most relevant features for that particular task.
• Enhanced Semantic Relationships: They enable the model to understand relationships that are not immediately apparent through traditional semantic similarity.
• Improved Retrieval Accuracy: Align queries more effectively with relevant documents or responses.

2. Cosine Similarity

Definition: Cosine similarity measures the cosine of the angle between two vectors in a multi-dimensional space. It determines how similar two embeddings are, focusing on their orientation rather than their magnitude.

Range of Values

• 1: Vectors are identical in orientation.
• 0: Vectors are orthogonal (unrelated).
• -1: Vectors are diametrically opposed.

Why Use Cosine Similarity?

• Magnitude-Invariant: It focuses on the direction of the vectors, making it robust to differences in vector lengths.

Real-World Applications

Search Ranking:

Determine the relevance of documents to a given query.

Example: Ranking product reviews in response to a search for "best wireless headphones" by measuring the cosine similarity between the query and review embeddings.

Image and Voice Recognition:

Identify similar images or voices based on embeddings.

Example: Matching a suspect's voice recording to a database of voice prints using cosine similarity.

Plagiarism Detection:

Assess the similarity between documents.

Example: Comparing student assignments to detect copied content by evaluating the cosine similarity of their text embeddings.

Example

Embedding Comparison:

• Text A Embedding: [0.62, 0.48, 0.91] (e.g., "The effects of global warming on sea levels.")
• Text B Embedding: [0.60, 0.50, 0.89] (e.g., "Climate change and its impact on rising oceans.")
• Cosine Similarity: A high value (e.g., 0.998) indicates that the texts are closely related in topic.

3. Mean Reciprocal Rank (MRR)

Definition: MRR is a statistical measure used to evaluate the effectiveness of a retrieval system. It calculates the average reciprocal ranks of the first relevant result for a set of queries.

Why Use MRR?

• Emphasis on Early Retrieval: Rewards systems that retrieve relevant results at higher ranks.
• Interpretability: Provides a clear metric to assess and compare system performance.

Real-World Applications

• Search Engines:
• Chatbots:
• Recommendation Systems:
• Customer Support:

Expanded Example: Understanding Mean Reciprocal Rank (MRR)

Let's delve into an expanded example to make MRR easy to understand.

Scenario: You have a search system, and you want to evaluate its performance in retrieving the correct answers to users' queries. We'll consider three queries.

Query 1: "How to change a flat tire?"

• System's Ranked Results:
• Position of Correct Answer: 2nd
• Reciprocal Rank Calculation:

Query 2: "Symptoms of dehydration in adults"

• System's Ranked Results:
• Position of Correct Answer: 1st
• Reciprocal Rank Calculation:

Query 3: "Recipes for vegan desserts"

• System's Ranked Results:
• Position of Correct Answer: 4th
• Reciprocal Rank Calculation:

Calculating the Mean Reciprocal Rank (MRR):

1. Sum of Reciprocal Ranks:
2. Compute the Mean:

Interpreting the MRR Score:

• An MRR of approximately 0.583 indicates that, on average, the correct answer appears around the 1.71 position in the ranked results (since 1 / 0.583 ≈ 1.71).
• A higher MRR value (closer to 1) signifies better performance, as it means the correct answers appear at the top of the results list.

Why MRR Matters:

• User Experience: Users find what they're looking for more quickly, enhancing satisfaction.
• System Evaluation: Provides a quantitative measure to compare different retrieval models or system configurations.

Building a POC Microservice for Task-Type Embeddings

Google Cloud Project:

• A Google Cloud project with Vertex AI API enabled.
• A service account JSON key for authentication.

Required Libraries:

• Install the following libraries:

pip install fastapi uvicorn pydantic python-dotenv scikit-learn google-cloud-aiplatform
        

Environment Variables: Create a .env file in your project directory with the following contents:

# JSON configuration for the service
CONFIG_JSON="{\"project_id\": \"your_project_id\", \"location\": \"your_location\", \"model_name\": \"text-embedding-005\", \"supported_task_types\": [\"SEMANTIC_SIMILARITY\", \"QUESTION_ANSWERING\", \"RETRIEVAL_QUERY\"]}"

# Path to the Google Cloud service account JSON key file
GOOGLE_APPLICATION_CREDENTIALS=./path_to_your_service_account_key.json

# Port on which the FastAPI server will run
PORT=8080

# Logging level for debugging and monitoring
LOG_LEVEL=INFO
        

Detailed Explanation of Each Parameter

1. CONFIG_JSON:

• A JSON string containing the primary configuration for the service.
• Fields:

project_id: The ID of your Google Cloud project.

Location: The location/region where your Vertex AI resources are hosted (e.g., us-central1, us-east1).

model_name: The Vertex AI model for embeddings (e.g., text-embedding-005).

supported_task_types: List of task types supported by the model (e.g., SEMANTIC_SIMILARITY, QUESTION_ANSWERING, RETRIEVAL_QUERY).

Example:

CONFIG_JSON="{\"project_id\": \"my-gcp-project\", \"location\": \"us-east1\", \"model_name\": \"text-embedding-005\", \"supported_task_types\": [\"SEMANTIC_SIMILARITY\", \"QUESTION_ANSWERING\", \"RETRIEVAL_QUERY\"]}"
        

GOOGLE_APPLICATION_CREDENTIALS:

• Path to the service account key file in JSON format for authentication with Google Cloud.
• Replace ./path_to_your_service_account_key.json with the correct path to your service account key file.

Example:

GOOGLE_APPLICATION_CREDENTIALS=./service-account.json
        

PORT:

• Port on which the FastAPI server will run.
• Default is 8080. You can modify this to avoid conflicts on your machine.

LOG_LEVEL:

• Logging level to control the verbosity of logs.
• Supported levels: DEBUG, INFO, WARNING, ERROR, CRITICAL.

Code Dissection

1. Environment Configuration and Logging Setup

# Load environment variables
load_dotenv()

# Load configuration from environment variables
try:
    config = json.loads(os.getenv("CONFIG_JSON", "{}"))
    if not config:
        raise ValueError("CONFIG_JSON is missing or malformed in the .env file.")
    port = int(os.getenv("PORT", 8080))
    credentials_path = os.getenv("GOOGLE_APPLICATION_CREDENTIALS")
    if not credentials_path or not os.path.exists(credentials_path):
        raise FileNotFoundError("Service account key file not found or not specified in GOOGLE_APPLICATION_CREDENTIALS.")
except Exception as e:
    raise RuntimeError(f"Error loading configuration: {str(e)}")

# Setup logging
log_level = os.getenv("LOG_LEVEL", "INFO").upper()
logging.basicConfig(
    level=log_level,
    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s (%(filename)s:%(lineno)d)",
)
logger = logging.getLogger(__name__)
logger.info("Configuration loaded successfully.")
        

What It Does:

• Loads configuration details and credentials from .env.
• Sets up logging for debugging and error tracking.

Vertex AI Initialization:

# Initialize Vertex AI with service account credentials
try:
    credentials = service_account.Credentials.from_service_account_file(credentials_path)
    vertexai.init(project=config["project_id"], location=config["location"], credentials=credentials)
    logger.info("Vertex AI initialized successfully.")
except Exception as e:
    logger.error(f"Error initializing Vertex AI: {str(e)}")
    raise
        

What It Does:

• Authenticates using the service account.
• Initializes Vertex AI with the project and location specified in the configuration.

FastAPI App and Schema Definitions:

# FastAPI app
app = FastAPI()

# Schema definitions
class QueryData(BaseModel):
    question: str
    answers: List[str]
    correct_answer: str
    question_task_type: TaskType
    answer_task_type: TaskType

class Payload(BaseModel):
    data: List[QueryData]
        

What It Does:

• Creates a FastAPI application.
• Defines input schema for QueryData and payload structure (Payload).

Helper Functions:

def get_embeddings(texts: list[str], task_type: str):
    try:
        model = TextEmbeddingModel.from_pretrained(config["model_name"])
        inputs = [TextEmbeddingInput(text, task_type) for text in texts]
        embeddings = model.get_embeddings(inputs)
        return [emb.values for emb in embeddings]
    except Exception as e:
        logger.error(f"Error fetching embeddings: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error fetching embeddings: {str(e)}")

def get_top100_similar_answers(similarities):
    return sorted(range(len(similarities)), key=lambda i: -similarities[i])

def calculate_mrr(query_ranks):
    reciprocal_ranks = [1 / (i + 1) for ranks in query_ranks for i, rank in enumerate(ranks) if rank == 1]
    return sum(reciprocal_ranks) / len(query_ranks)        

What It Does:

• get_embeddings: Fetches embeddings for texts using the specified task type.
• get_top100_similar_answers: Ranks answers based on cosine similarity scores.
• calculate_mrr: Computes the Mean Reciprocal Rank for ranked queries.

Endpoint Implementation:

@app.post("/embeddings/process/")
async def process_data(payload: Payload):
    logger.info(f"Processing data with {len(payload.data)} queries.")
    output, all_query_ranks = [], []
    try:
        for item in payload.data:
            logger.info(f"Processing question: {item.question}")
            question_embedding = get_embeddings([item.question], item.question_task_type.value)[0]
            answer_embeddings = get_embeddings(item.answers, item.answer_task_type.value)
            similarities = cosine_similarity([question_embedding], answer_embeddings)[0]
            ranked_indices = get_top100_similar_answers(similarities)
            ranked_answers = [item.answers[i] for i in ranked_indices]
            query_ranks = [1 if item.answers[i] == item.correct_answer else 0 for i in ranked_indices]
            all_query_ranks.append(query_ranks)
            output.append({
                "question": item.question,
                "answers": item.answers,
                "cosine_similarities": similarities.tolist(),
                "ranked_answers": ranked_answers,
                "query_ranks": query_ranks
            })
        mrr = calculate_mrr(all_query_ranks)
        return {"results": output, "mean_reciprocal_rank": mrr}
    except Exception as e:
        logger.error(f"Error processing data: {str(e)}")
        raise HTTPException(status_code=500, detail=f"Error processing data: {str(e)}")
        

What It Does:

• Processes incoming payloads.
• For each query, compute embeddings, cosine similarities, and rankings.
• Returns results and the computed MRR.

Sample Input Payload

Here’s a new example payload to demonstrate the microservice with a different use case:

Input Payload:

{
  "data": [
    {
      "question": "What is the tallest mountain in the world?",
      "answers": ["Mount Everest", "K2", "Kangchenjunga", "Lhotse"],
      "correct_answer": "Mount Everest",
      "question_task_type": "QUESTION_ANSWERING",
      "answer_task_type": "QUESTION_ANSWERING"
    },
    {
      "question": "Which planet is known as the Red Planet?",
      "answers": ["Earth", "Mars", "Jupiter", "Venus"],
      "correct_answer": "Mars",
      "question_task_type": "QUESTION_ANSWERING",
      "answer_task_type": "QUESTION_ANSWERING"
    }
  ]
}
        

Expected Output

The service will process the above input and return cosine similarity scores, ranked answers, and the Mean Reciprocal Rank (MRR):

Output:

{
  "results": [
    {
      "question": "What is the tallest mountain in the world?",
      "answers": ["Mount Everest", "K2", "Kangchenjunga", "Lhotse"],
      "cosine_similarities": [0.95, 0.30, 0.25, 0.20],
      "ranked_answers": ["Mount Everest", "K2", "Kangchenjunga", "Lhotse"],
      "query_ranks": [1, 0, 0, 0]
    },
    {
      "question": "Which planet is known as the Red Planet?",
      "answers": ["Earth", "Mars", "Jupiter", "Venus"],
      "cosine_similarities": [0.10, 0.98, 0.15, 0.12],
      "ranked_answers": ["Mars", "Jupiter", "Venus", "Earth"],
      "query_ranks": [1, 0, 0, 0]
    }
  ],
  "mean_reciprocal_rank": 1.0
}
        

Conclusion

By leveraging Google Task Type embeddings, we can significantly enhance the performance of retrieval systems, particularly for tasks like question-answering where traditional semantic similarity measures fall short. Task-specific embeddings allow models to capture the nuanced relationships between queries and relevant responses, leading to more accurate retrieval and improved user experiences. Understanding the various task types supported by Google and how they can be applied with relevant examples highlights their potential to revolutionize applications across industries.

#googleTaskTypeEmbeddings #Embeddings #NLP #MachineLearning #SemanticSimilarity #RetrievalSystems #VertexAI #QuestionAnswering #CosineSimilarity #MeanReciprocalRank #MRR #ArtificialIntelligence #DataScience #TaskTypeEmbeddings #GoogleAI

Pulling the Code from GitHub

We have hosted the complete source code in a GitHub repository to make it easier for developers to start using the microservice. Follow the steps below to clone the repository and get started:

GitHub Repository

The code is available on GitHub under the following repository: ?? AI Microservices Repository

This repository contains multiple microservices, including the TaskType embedding service described in this article.

Step 1: Clone the Repository

To clone the repository, follow these steps:

1. Open your terminal or command prompt.
2. Use the git clone command to pull the repository. For example:
3. Navigate into the repository:

Step 2: Navigate to the Microservice

In the repository, please find the GCPTaskTypeEmbeddings folder, which contains all the necessary files for the microservice.

cd GCPTaskTypeEmbeddings        

This folder includes:

• main.py – The microservice code.
• .env – Environment configuration file(SampleEnv.txt).
• requirements.txt – Dependencies for the microservice.
• Sample Service account credentials placeholder (gcp_service_account_placeholder.json).

Step 3: Install Dependencies

To install the dependencies, use the following command inside the GCPTaskTypeEmbeddings directory:

pip install -r requirements.txt        

This will install all required Python libraries, such as fast API, google-cloud-platform, sci-kit-learn, etc.

Step 4: Configure the .env File

Rename the SampleEnv.txt to.env file, and update the .env file with the necessary parameters (e.g., CONFIG_JSON and GOOGLE_APPLICATION_CREDENTIALS) as described earlier in the article.

Example .env file template:

CONFIG_JSON="{\"project_id\": \"<your-project-id>\", \"location\": \"<region>\", \"model_name\": \"text-embedding-005\", \"supported_task_types\": [\"SEMANTIC_SIMILARITY\", \"QUESTION_ANSWERING\", \"RETRIEVAL_QUERY\"]}" GOOGLE_APPLICATION_CREDENTIALS=./<your-service-account-key>.json        

Step 5: Run the Microservice

Finally, start the FastAPI server by running the following command:

python main.py        

This will launch the microservice at https://0.0.0.0:8080 (or the port specified in the .env file). You can test the service using a tool like Postman or cURL.