Blog
Harnessing Generative Feedback Loops in AI Systems with Milvus

Harnessing Generative Feedback Loops in AI Systems with Milvus

May 10, 202414 min read

A generative feedback loop is a cyclical process in which the output generated by an AI model is fed back into the system as training data. This allows the model to learn and improve its capabilities continuously over time. This cycle repeats, allowing the AI to optimize its results progressively.

Large Language Models (LLMs) can significantly benefit from generative feedback loops. LLMs are trained on vast amounts of text data, allowing them to understand and generate human-like text. However, their base knowledge is static, and they don't automatically learn from new information. This is where Milvus comes in.

Milvus is an open-source vector database designed to store, index, and search massive amounts of vector data in real-time. In the context of LLMs, Milvus can efficiently store and retrieve the vector representations of text data that encode its semantic meaning. Integrating Milvus with LLMs in a generative feedback loop allows us to create a dynamic system that continually learns and improves.

To better understand how these components work together, let's first see the concept of generative feedback loops in the context of AI and Machine Learning.

Understanding Generative Feedback Loops

In the context of AI and machine learning, a feedback loop is a mechanism that utilizes the model's output data to improve the model itself. This iterative process allows the model to continuously learn and refine its performance. The core elements involved in a generative feedback loop are:

Model Training: The initial stage involves training the AI model using a dataset of labeled examples. This dataset provides the foundation for the model to learn the underlying patterns and relationships within the data.
Model Output: Once trained, the model generates new outputs, which can be predictions, classifications, or creative text formats, for instance.
Evaluation and Feedback: The generated outputs are then evaluated based on a predefined set of criteria. This evaluation might involve human experts assessing their quality and accuracy or comparing them to ground-truth data.
Data Integration: The evaluated outputs and the associated feedback data are then incorporated into the training dataset. This enriched dataset empowers the model to learn from its past performance and refine its output generation process in subsequent iterations.

Generative feedback loops are vital in ensuring the continuous improvement of model outputs in AI systems. Here's how these loops facilitate this ongoing refinement, broken down into specific benefits:

Adaptability to New Data
- The loop incorporates new data inputs and interactions into the training data.
- This allows the model to adjust to evolving patterns and trends, enhancing the relevance and accuracy of future outputs.
Reduced Bias and Errors
- The loop helps identify and mitigate biases in the initial training data.
  - Human evaluation or ground truth data can flag outputs that deviate from desired outcomes.
- This facilitates adjustments to the training data and model parameters, reducing bias and errors over time.
Personalized Model Outputs
- User feedback or preferences can be integrated into the feedback loop.
  - The model personalizes outputs to better cater to specific user needs and contexts.
- For instance, a product description model could be refined based on user click-through rates, resulting in more engaging and compelling descriptions.
Enhanced Creativity and Innovation
- Feedback loops empower models to explore new creative avenues in generative AI applications.
  - The model analyzes successful past outputs and user preferences.
  - This guides the model towards generating more innovative and relevant creative content.
- This continuous learning fosters a cycle of creative exploration and refinement.

Now, let’s see how feedback loops enhance LLMs.

The Role of Feedback Loops in LLMs

Large Language Models (LLMs) are advanced AI systems that understand and generate human-like text. They’re trained on vast datasets, enabling them to perform various language-related tasks, from translation to content creation. Their ability to understand context and nuance allows them to engage in conversations, answer questions, and even mimic certain writing styles.

Feedback loops are crucial for improving the performance of LLMs. They use the outputs generated by the model as inputs for further learning, allowing the model to refine its predictions and text generation. Here’s how feedback loops can enhance LLMs:

Continuous Learning: By incorporating user feedback, LLMs can learn from their interactions. This could be explicit, like corrections or suggestions, or implicit, such as which responses users engage with more.
Adaptation to User Preferences: Over time, feedback loops help LLMs adapt to individual user preferences, tailoring the style and content of the generated text to meet user expectations.
Error Correction: When errors are identified, feedback loops enable LLMs to adjust their internal parameters, reducing the likelihood of repeating the same mistakes.
Predictive Accuracy: By analyzing which predictions were successful and which weren’t, LLMs can improve their accuracy in understanding and anticipating user needs.
Generative Capabilities: Feedback loops can guide LLMs to generate more creative and varied content, whether writing a story, composing a poem, or creating informative articles.

LLMs are more efficient, accurate, personable, and user-friendly by leveraging feedback loops. It’s a journey of continuous improvement, aiming to provide users with an experience that feels both intuitive and distinctly human.

Integrating Milvus for Enhanced Data Handling

Milvus is a highly performant, distributed vector database that is particularly beneficial for managing large-scale vector data, which is essential in feedback loop scenarios for LLMs. Here are some of the critical features of Milvus that make it suitable for such applications:

Vector Embeddings: Milvus specializes in handling vector embeddings, numerical representations derived from machine learning models. These embeddings encapsulate the semantic meaning of unstructured data, allowing for nuanced searches that capture the essence of the data.
Efficient Query Processing: It supports advanced query processing beyond simple vector similarity search. This means it can handle dynamic data for fast updates while ensuring efficient query processing, which is crucial for LLMs that continuously learn and adapt.
Scalability and Availability: Milvus distributes data across multiple nodes to achieve scalability and high availability. This is important for feedback loops where the volume of data can grow rapidly as new information is continuously incorporated.
Heterogeneous Computing: The system optimizes for heterogeneous computing platforms with modern CPUs and GPUs, which benefits the computationally intensive tasks involved in LLM training and response generation.
Easy-to-Use Interfaces: Milvus provides easy-to-use application interfaces, including SDKs and RESTful APIs, facilitating integration with LLMs and other AI applications.

In terms of supporting efficient data indexing and retrieval necessary for dynamic LLM training and response generation, Milvus offers:

Retrieval-Augmented Generation (RAG): Milvus can be used to build RAG systems that combine a retrieval system with a generative model. This allows for generating new text based on retrieved documents, a key component in dynamic LLM training.
Vector Store: It sets up a vector store to save vector embeddings, essential for text-to-text similarity searches. This feature enables the LLM to peek at documents when preparing answers, thus improving the quality of response generation.
Indexing and Search Framework: Milvus integrates with frameworks like NVIDIA Merlin for efficient vector database index and search capabilities. These are critical for recommender workflows and can be applied to LLMs for better prediction and response accuracy.

These features make Milvus an effective tool for enhancing the data-handling capabilities of LLMs, especially in scenarios where feedback loops are used to refine predictive and generative accuracies.

Next, let’s see how Milvus can be used to set up a generative feedback loop system.

Setting Up a Generative Feedback System with Milvus and LLMs

We will start by installing required packages as shown below. We will use Anthropic’s Claude model for text generation. For more information on installing Milvus, please refer to the official documentation: Milvus documentation.

# Install Milvus, pymilvus with model extra, and the specified grpcio version
pip install pymilvus[model] grpcio==1.50.0 milvus

# Install the anthropic package for using Anthropic LLM
pip install anthropic

# Install the sentence-transformers package for text embedding
pip install sentence-transformers

In the next we will start a Milvus server as per the below steps:

Import the default_server module from the milvus package.
Import the connections and utility modules from the pymilvus package.
(Optional) Clean up any previous data using the cleanup() method of default_server.
Start the Milvus server using the start() method of default_server.
Connect to the Milvus server using the connect() method of connections with the specified host and port.
Check if the server is ready by printing the server version using the get_server_version() method of utility.

The code below shows all the steps we performed.

from milvus import default_server
from pymilvus import connections, utility

# (OPTIONAL) Clean up any previous data
default_server.cleanup()

# Start the Milvus server
default_server.start()

# Connect to the Milvus server
connections.connect(host='127.0.0.1', port=default_server.listen_port)

# Check if the server is ready by printing the server version
print(utility.get_server_version())

Next we do the following:

Import the MilvusClient class from the pymilvus package.
Create an instance of the Milvus client named client1 using the MilvusClient() constructor.

from pymilvus import MilvusClient
# Create an instance of the Milvus client
client1 = MilvusClient()

After this, we create a schema and collection along with their index parameters. We name the collection as taverns10. We do that using pymilvus as shown below.

from pymilvus import Collection, FieldSchema, CollectionSchema, DataType

# Define the fields for the "Taverns" collection
fields = [
    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
    FieldSchema(name="name", dtype=DataType.VARCHAR, max_length=255, description="Tavern name"),
    FieldSchema(name="description", dtype=DataType.VARCHAR, max_length=3000, description="Tavern description", is_vector=False),
    FieldSchema(name="location", dtype=DataType.VARCHAR, max_length=255, description="Location in Middle-Earth"),
    FieldSchema(name="famous_visitor", dtype=DataType.VARCHAR, max_length=255, description="Famous visitors in the tavern"),
    FieldSchema(name="vector_embedding", dtype=DataType.FLOAT_VECTOR, dim=384, description="Vector embedding for semantic search")
]

# Define the index parameters for similarity search
index_params = {
    "metric_type": "IP",
    "index_type": "HNSW",
    "params": {"M": 48, "efConstruction": 200}
}

# Create a CollectionSchema object using the defined fields and description
schema = CollectionSchema(fields=fields, description="LOTR Taverns")

# Create a Collection object with the specified name, schema, and index parameters
taverns10 = Collection(name="taverns10", schema=schema, index_params=index_params)

# Create an index on the "vector_embedding" field using the specified index parameters
taverns10.create_index("vector_embedding", index_params)

Now we will add documents to the database along with their vector embeddings. We will create the document descriptions using claude opus and after that, we will create their embeddings using sentence transformers. First, we initialize the Anthropic client and the sentence transformers embedding model as shown below

import anthropic
from pymilvus import model

# Create an instance of the SentenceTransformerEmbeddingFunction for text embedding
sentence_transformer_ef = model.dense.SentenceTransformerEmbeddingFunction(
    model_name='all-MiniLM-L6-v2',
    device='cpu'
)

# Create an instance of the Anthropic client with the provided API key
client = anthropic.Anthropic(
    api_key="your_api_key_here"
)

Next, we create a generate function to generate descriptions using the claude opus model. We then use this function to generate descriptions, and create embeddings and then we will add them to the collection

def generate_description(name, location, famous_visitor):
    # Construct a prompt for generating a vivid description of the tavern
    prompt = f"Write a vivid description for a tavern named {name}, located in {location}, known for a visit by {famous_visitor}."

    # Use the Anthropic client to create a message by sending the prompt to the specified model
    message = client.messages.create(
        model="claude-3-opus-20240229",
        max_tokens=1000,
        temperature=0,
        messages=[{"role": "user", "content": prompt}]
    )

    # Return the generated description from the message content
    return message.content[0].text

# Define a list of example tavern data
tavern_data = [
    {"name": "The Prancing Pony", "location": "Bree", "famous_visitor": "Aragorn"},
    {"name": "Green Dragon", "location": "Bywater", "famous_visitor": "Frodo Baggins"}
]

# Iterate over each tavern in the tavern_data list
for tavern in tavern_data:
    # Generate a description for the tavern using the generate_description function
    description = generate_description(tavern["name"], tavern["location"], tavern["famous_visitor"])

    # Encode the generated description into a vector embedding using the encode_documents method
    vector_embedding = sentence_transformer_ef.encode_documents([description])[0]

    # Insert the tavern data and vector embedding into the taverns10 collection
    taverns10.insert([
        [tavern["name"]],
        [description],
        [tavern["location"]],
        [tavern["famous_visitor"]],
        [vector_embedding]
    ])

We have now created a collection and added vector embeddings to the database. To use this, we need to load the collection using the load_collection method as shown below

# Load the "taverns10" collection into memory using the load_collection method
client1.load_collection(collection_name="taverns10")

# Retrieve the load state of the "taverns10" collection using the get_load_state method
res = client1.get_load_state(collection_name="taverns10")

# Print the load state
print(res)

Output

{'state': <LoadState: Loaded>}

Now, let’s create a function that performs the semantic search using any input query. We do that as shown below

def search_taverns(query):
    # Encode the query string into a vector using the encode_documents method
    vector = sentence_transformer_ef.encode_documents([query])[0]

    # Perform a similarity search on the "taverns10" collection using the search method
    results = taverns10.search(
        data=[vector],
        anns_field="vector_embedding",
        param={"metric_type": "IP", "params": {"nprobe": 10}},
        limit=3,
        output_fields=["name", "description", "location", "famous_visitor"]
    )

    # Return the search results
    return results

Let’s query using the search function and get the first generated response.

# Call the search_taverns function with a sample query and assign the results to search_results
search_results = search_taverns("A cozy place frequented by famous hobbits")
search_results[0][0]

Output

id: 449415822381617057, distance: 0.4701901376247406, entity: {'location': 'Bree', 'famous_visitor': 'Aragorn', 'name': 'The Prancing Pony', 'description': "The Prancing Pony is a cozy, rustic tavern nestled in the heart of the bustling village of Bree. As you approach the weathered wooden door, the warm glow of flickering lanterns and the merry sounds of laughter and clinking mugs beckon you inside.\n\nStepping over the threshold, you're greeted by the inviting aroma of hearty stew simmering in a cauldron over the crackling fireplace. The tavern's interior is a tapestry of rough-hewn wooden beams, well-worn tables, and benches polished smooth by countless patrons over the years. The walls are adorned with colorful banners and curious trinkets, each hinting at a tale waiting to be told.\n\nThe Prancing Pony's patrons are a lively and eclectic bunch, from local hobbits and farmers to wandering dwarves and mysterious rangers. Amidst the chatter and revelry, whispers of a recent visit by the enigmatic Aragorn, a man of legend, add an air of intrigue to the already enchanting atmosphere.\n\nAs you settle into a cozy nook, the attentive barkeep, a jovial fellow with a twinkle in his eye, brings you a frothing mug of the tavern's renowned ale and a plate of piping hot bread, fresh from the oven. The Prancing Pony is more than just a place to rest your weary feet; it's a sanctuary where stories are shared, bonds are forged, and the spirit of adventure thrives, making it a true gem in the heart of Bree."}

We got a response that is nearer to our expectations. We can use this response to refine further responses thus triggering a feedback loop. This can effectively increase the accuracy of the outputs.

Practical Applications and Benefits

Milvus enhances AI applications like personalized recommendations by enabling similarity searches for user-item matching, ensuring tailored content delivery. Adaptive learning systems facilitate the quick retrieval of educational resources and adapt to individual learning paces. For real-time content generation, Milvus’s efficient indexing supports dynamic content creation.

The benefits of using Milvus include scalability to handle growing data volumes, speed for instant query responses, and precision in finding the most relevant data points, making it a robust backbone for AI-driven applications.

Challenges and Considerations

Implementing generative feedback loops with LLMs and Milvus presents several challenges:

Computational Demands: LLMs require significant computational resources, which can be costly and complex to manage.
Data Privacy: Ensuring the confidentiality and integrity of data within these systems is paramount, especially when handling sensitive information.

To address these challenges, consider the following solutions and best practices:

Optimize Resource Usage: Utilize cloud services with scalable infrastructure to handle computational loads efficiently.
Data Anonymization: Implement robust data anonymization techniques to protect user privacy.
Regular Audits: Conduct frequent security and privacy audits to identify and mitigate potential vulnerabilities.
Best Practices: To streamline processes and maintain system integrity, apply best practices in machine learning operations (MLOps).

Future of AI with Generative Feedback Loops

The future of AI is poised for growth with advancements in generative feedback loops and their integration with Large Language Models (LLMs) and vector databases like Milvus. Here’s what to expect:

Feedback Loop Methodologies: Enhanced feedback loops will enable AI to learn and adapt more dynamically, fostering continuous improvement in real-time applications.
Vector Databases: Innovations in vector databases, particularly Milvus, will facilitate the efficient handling of complex data, bolstering AI’s ability to perform similarity searches and content-based retrieval.
LLM Architectures: Improvements in LLM architectures will lead to a more nuanced understanding and generation of language, opening new avenues for AI interaction and creativity.

Key Points:

Generative feedback loops will significantly enhance AI’s learning capabilities.
Milvus and similar vector databases will provide the backbone for scalable, precise AI applications.
LLM advancements will drive AI towards more sophisticated, context-aware operations.

Experimentation with these technologies is crucial. By leveraging generative feedback loops, Milvus, and improved LLMs, developers and researchers can unlock new potentials in AI-driven applications, from personalized digital assistants to advanced data analysis tools. The synergy of these technologies will refine existing applications and pave the way for previously unimagined innovative solutions.

If you are interested in exploring the capabilities of Milvus and Large Language Models (LLMs), Zilliz offers a wealth of resources and community forums. Here’s how you can get involved:

Explore Resources: Zilliz provides a variety of whitepapers, webinars, and training to help you understand vector search and AI technologies.
Join the Community: Engage with a growing community of developers and users on the Zilliz Cloud Developer Hub.
Try Milvus: Start building with Milvus and experience its power in your AI applications. Sign up for a free account and experiment with the SDKs that are available.

Whether you’re a developer or just starting, these resources and forums are a great way to connect, learn, and contribute to the future of AI with Milvus and LLMs.

Uppu Rajesh Kumar

Content

Start Free, Scale Easily

Try the fully-managed vector database built for your GenAI applications.

Try Zilliz Cloud for Free