Elevating Enterprise LLMs with Retrieval-Augmented Generation (RAG) and Vector Database Integration

Enhancing LLM Applications with Context-Aware Technologies

The applications of Large Language Models have been transformative across various sectors, shaping a new frontier in natural language processing and understanding. LLMs, renowned for generating human-like text, have revolutionized chatbots, content creation, and even complex problem-solving tasks.

However, despite their impressive capabilities, LLMs face notable challenges, particularly in context awareness and maintaining accuracy over extended interactions. A common pitfall is their tendency towards "hallucinations," where the generated content, though fluent, may drift into inaccuracy or irrelevance.

This is where technologies like Retrieval-Augmented Generation (RAG) and vector databases become pivotal. By integrating LLMs with RAG, which dynamically retrieves relevant information from vast datasets, we can significantly mitigate these limitations. The synergy between LLMs and vector databases, capable of efficiently handling and retrieving structured vector data, promises to bring a new level of depth, context, and reliability to LLM applications.

In this blog, readers can expect:

Comprehensive Insights into LLM Challenges: Understanding the limitations of LLMs, such as context awareness and accuracy issues.
Introduction to RAG and Vector Databases: Exploring how these techniques address the drawbacks of traditional LLMs.
Practical Demonstrations and Tutorials: Hands-on examples showcasing the integration of RAG with vector databases to enhance LLM applications.
Real-World Applications: Exploring how these integrations can be applied effectively in enterprise settings.
Actionable Knowledge for Various Audiences: Whether you're a tech enthusiast, an AI practitioner, or a business professional, the blog aims to provide valuable insights and practical knowledge.

Understanding Retrieval-Augmented Generation (RAG)

Retrieval-Augmented Generation (RAG) is an innovative paradigm in the field of AI and natural language processing. It marks a significant shift from conventional language models by integrating information retrieval into the language generation process. This hybrid approach enhances the ability of AI models to generate responses that are not only contextually accurate but also infused with up-to-date knowledge from external data sources.

The inception of RAG can be traced back to the quest for overcoming the limitations of standard language models, such as GPT (Generative Pre-trained Transformer). Traditional models, despite their proficiency in generating coherent text, often struggle with providing accurate, fact-based responses, particularly for queries requiring specific, real-time knowledge.

Here's a description of how Retrieval-Augmented Generation (RAG) works, integrating with a vector database:

Ingestion and Indexing: Workflow begins with a comprehensive Knowledge Base, which is the foundation of the system's intelligence. This Knowledge Base is typically a large corpus of documents containing the information that might be queried by users. These documents could be anything from FAQ sheets and articles to databases of structured information. Before these documents can be utilized, they undergo an ingestion process where they are pre-processed and transformed into embeddings. An Embedding Model—often a sophisticated machine learning algorithm—is employed to convert the textual information into vector embeddings. These embeddings numerically represent the semantic content of the documents in a multi-dimensional space, which is optimal for similarity comparisons.
Customer Interaction: It all begins with a customer interacting with an application and posing a query. This query is a request for information or a question that the customer expects the AI to answer.
Query Embedding: The raw customer query is then processed by an Embedding Model. This model converts the text query into a vector, which is a numeric representation that captures the semantic meaning of the query in a high-dimensional space.
Vector Database Search: The query vector is sent to a Vector Database, a specialized database designed to handle high-dimensional vector data. The database performs a similarity search to retrieve the most relevant document embeddings. These embeddings represent pre-processed knowledge from a Knowledge Base that has been ingested into the system.
Context Retrieval: The retrieved document embeddings are combined with the original query to form a prompt that includes both the query and the relevant context.
Language Model Response Generation: This enriched prompt is then fed into a Language Learning Model (LLM). The LLM uses the context from the retrieved documents to generate a response that is accurate, informative, and contextually relevant to the customer's query.
Generative Response: Finally, the LLM produces a generative response, which is delivered back to the customer via the app. This response is not only based on the model's pre-trained knowledge but also augmented with specific information retrieved from the knowledge base, making it highly relevant to the user's request.

Vector Databases

Vector databases store and manage data that has been converted into numerical vector form, often through processes like embedding models in machine learning. Embeddings are numerical representations of data, often high-dimensional vectors, that capture the semantic or contextual features of the original input. In the case of text data, embeddings convert words, sentences, or entire documents into a form that a computer can process. Machine learning models, particularly neural networks, are used to generate these embeddings so that similar meanings are close in the vector space. These databases are designed to efficiently perform similarity searches, which locate data points that are closest to a given query vector within the vector space.

Here's a deeper look into the process:

Data Storage: When documents are ingested, an embedding model (such as a neural network) transforms the text into a high-dimensional vector. Each vector represents the semantic meaning of the document in a numerical form. These vectors are then stored in the vector database.
Indexing: To facilitate fast retrieval, the database builds an index on these vectors using algorithms suited for high-dimensional spaces, such as Inverted File Index (IVF) or Hierarchical Navigable Small World (HNSW). The choice of index type balances between the speed and accuracy of the search.
Similarity Search: When a query is made, it is also converted into a vector using the same embedding model. The vector database then uses the index to quickly find the vectors most similar to the query vector. Similarity is determined by distance metrics like Euclidean distance or cosine similarity.

Advantages of Embeddings:

Semantic Similarity: Embeddings are designed so that semantically similar items are closer in the vector space, enabling systems to understand context and meaning. For example, in the field of genomics, gene expression data can be encoded as embeddings to reveal patterns that indicate relationships between different genes and phenotypes. This can assist in identifying biomarkers for diseases that may not be apparent through traditional analysis.
Complex Relationships: They can capture complex relationships and nuances in the data that might be missed with traditional representations. A practical application is seen in recommendation systems, such as those used by streaming services like Netflix or Spotify. These platforms use embeddings to understand user preferences and content features, thereby recommending movies or songs that share similarities with a user's previous choices. Despite the diversity in content, embeddings allow for nuanced recommendations that go beyond genre or artist, considering deeper patterns in user consumption.
Uniformity: Embeddings convert varied data types into a uniform vector format, simplifying operations like comparison and retrieval.

Getting Started with Vector DB

Creating a local development environment for RAG and Vector DB (Milvus) involves several key steps.

Here's a structured guide:

Prerequisites:
- Ensure Python 3.6+ is installed on your system.
- Docker is required for running Milvus.

Virtual Environment:

Create a new virtual environment and use that:

python3 -m venv rag-milvus-env
source rag-milvus-env/bin/activate

# Install supporting dependencies
pip install transformers datasets faiss-cpu torch sentence-transformers pymilvus

Milvus using docker:

Pull and run the Milvus Docker image: (you can also use other vector db’s)

You can use the below steps or follow the getting started guide provided here.

docker pull milvusdb/milvus:latest
docker run -d --name milvus_cpu -p 19530:19530 -p 19121:19121 milvusdb/milvus:latest

Setup Data:

Now let’s try to download some sample data, create embeddings, and insert them into a collection.

import requests
import csv

url = "http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv"

# Download the file
response = requests.get(url)
response.raise_for_status()  # This will raise an error if the download failed

# Decode the content and split into lines
lines = response.content.decode('utf-8').splitlines()

questions = []

# Process the lines
reader = csv.reader(lines, delimiter='\t')
next(reader)  # Skip the header row
for row in reader:
    questions.extend([row[1], row[4]])  # Assuming the questions are in the 2nd and 3rd columns


questions = questions[:10000]

Create Embeddings


from sentence_transformers import SentenceTransformer

# transformer to create embeddings
model = SentenceTransformer('all-MiniLM-L6-v2')

embeddings = model.encode(questions)

Insert into vector DB.

# connect to db
connections.connect()

embedding_size = 384

# Prepare the collection schema
fields = [
    FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
    FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=embedding_size)
]


schema = CollectionSchema(fields, "questions")
collection = Collection("questions", schema)

# Insert the document embeddings
mr = collection.insert([embeddings])

# to get document, in practice this would be some external DB.
id_to_question = {str(mr.primary_keys[i]): questions[i] for i in range(len(questions))}


# List all collections
collections = list_collections()
print(collections)

Index the collection.

from pymilvus import Collection

index_params = {
    "metric_type": "L2",
    "index_type": "HNSW", # Index of type HNSW, refer to docs for other types.
    "params": {
        "M": 16,  # Example value, adjust as needed
        "efConstruction": 200  
    }
}
collection = Collection("questions")    
collection.create_index(
  field_name="embedding", 
  index_params=index_params
)

Query Documents

query = "What is artificial intelligence?"
query_embedding = model.encode(query)

collection.load()

# Define search parameters
search_params = {"metric_type": "L2", "params": {"nprobe": 10}}

# Perform the search
results = collection.search(
    data=[query_vector], 
    anns_field="embedding", 
    param=search_params, 
    limit=10, 
    expr=None,
    consistency_level="Strong"
)

# Process results
for result in results:
    milvus_id = str(result.id)  # Convert ID to string for dictionary lookup
    original_question = id_to_question[milvus_id]
    print(f"Milvus ID: {milvus_id}, Similar Question: {original_question}")

Once we get semantically similar documents from vector db., we could pass this context + input query to LLMs which will have much better results as LLMs have context now.

Conclusion

In conclusion, the integration of RAG with vector databases like Milvus offers a potent solution to some of the most pressing challenges in LLM applications—particularly those requiring deep contextual understanding and dynamic information retrieval. By combining the generative prowess of LLMs with the precision and efficiency of vector databases, enterprises can vastly improve the relevance and accuracy of AI-driven responses, providing users with valuable and contextually rich interactions.

As AI continues to advance, the fusion of these technologies represents not just a step, but a leap forward, heralding a future where AI can support more sophisticated, varied, and nuanced applications across all sectors. This blog has set the stage for innovators and practitioners to begin experimenting with these tools, pushing the boundaries of what's possible in the realm of enterprise AI applications.