Tutorial on Vector Search and RAG: Enhancing Data with Large Language Models
Vector Search RAG Tutorial – Combining Your Data with LLMs for Advanced Search
Welcome to this comprehensive tutorial on integrating vector search and embeddings with large language models like GPT-4. We’ll explore how to leverage these technologies to build advanced search applications that understand the semantic meaning of your data.
Understanding Vector Embeddings
Vector embeddings are numerical representations of data that capture the semantic meaning of words, sentences, or even entire documents. By converting text into vectors, we can measure the similarity between different pieces of text based on their contextual meaning.
- Embeddings allow us to perform semantic searches rather than simple keyword matches.
- They are essential for tasks like recommendation engines, natural language processing, and information retrieval.
Building Vector Embeddings for Semantic Similarity Searches
To perform semantic similarity searches, we first need to convert our textual data into vector embeddings. Here’s how you can do it using Python and the Hugging Face API:
import torch
from transformers import AutoTokenizer, AutoModel
# Load pre-trained model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
# Function to convert text to embeddings
def get_embedding(text):
encoded_input = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = model_output.last_hidden_state.mean(dim=1)
return embeddings.numpy()
Storing and Querying Vector Embeddings in MongoDB with Atlas Vector Search
MongoDB Atlas now offers Vector Search capabilities, allowing you to store and query vector embeddings efficiently. Here’s how you can set it up:
Setting Up MongoDB Atlas
- Sign up for a free MongoDB Atlas account at MongoDB Atlas.
- Create a new cluster and database.
- Enable Vector Search in your cluster settings.
Creating Vector Search Indices
After setting up your database, create a vector search index:
- Navigate to the Indexes tab in your cluster.
- Create a new index and select Vector as the index type.
- Specify the field in your documents where the embedding vectors will be stored.
Practical Implementation: Building a Semantic Search Application
Let’s apply what we’ve learned to build a simple semantic search application that searches movie plots:
Preparing the Data
- Collect a dataset of movie plots.
- Generate embeddings for each plot using the function we defined earlier.
- Store the plots and their embeddings in MongoDB.
Performing a Semantic Search
// Example using Node.js and the MongoDB driver
const { MongoClient } = require('mongodb');
async function semanticSearch(query) {
const client = new MongoClient('your_mongodb_connection_string');
await client.connect();
const database = client.db('movieDB');
const collection = database.collection('movies');
const queryEmbedding = getEmbedding(query);
const results = await collection.find({
embedding: {
$near: {
$vector: queryEmbedding,
$maxDistance: 0.5
}
}
}).toArray();
console.log(results);
await client.close();
}
In this example, we retrieve movies with plots semantically similar to the user’s query.
Retrieval Augmented Generation (RAG) with GPT-4
The RAG architecture combines retrieval mechanisms with generative models to provide context-specific responses. Here’s how you can modify a ChatGPT clone to use RAG:
Integrating with LangChain
LangChain is a framework that facilitates the development of applications powered by language models:
- Install LangChain using
pip install langchain. - Set up your OpenAI API key.
- Configure LangChain to use MongoDB Atlas as a vector store.
Implementing RAG
from langchain import OpenAI, VectorDBQA
from langchain.vectorstores import MongoDBAtlasVectorSearch
# Set up the vector store
vector_store = MongoDBAtlasVectorSearch(
mongodb_atlas_cluster_uri=\"your_mongodb_connection_string\",
index_name=\"your_vector_index\"
)
# Initialize the QA system
qa = VectorDBQA(
llm=OpenAI(model_name='gpt-4'),
vectorstore=vector_store
)
# Ask a question
response = qa(\"What is the plot of a movie where dreams are within dreams?\")
print(response)
This system retrieves relevant information from your data and uses GPT-4 to generate accurate, context-specific answers.
Limitations and Capabilities of Large Language Models
While LLMs like GPT-4 are powerful, they have limitations:
- Context Length: They can only process a limited amount of text at once.
- Knowledge Cutoff: They may not have information on recent events or data not included in their training set.
- Hallucinations: They might generate plausible but incorrect information.
By integrating vector search and RAG, we can mitigate these limitations by providing the model with relevant, up-to-date context.
Integrating Vector Search for Enhanced AI Applications
Combining vector embeddings, semantic search, and large language models unlocks new possibilities:
- Develop intelligent chatbots that provide accurate information based on your data.
- Create recommendation systems that understand user preferences on a deeper level.
- Enhance search functionalities in applications to deliver more relevant results.
By leveraging MongoDB Atlas as a vector store, you can efficiently manage and query your embeddings at scale.
Conclusion
In this tutorial, we’ve explored how to combine vector search with large language models to build advanced, semantic-aware applications. By understanding and implementing these concepts, you’re well on your way to enhancing your AI applications with sophisticated search and retrieval capabilities.
Happy coding!
Tutorial on Incorporating Vector Search with Large Language Models (LLMs) for an Advanced Semantic Search
Welcome to this detailed tutorial on amalgamating vector search and embeddings with advanced large language models like GPT-4. The focus is on harnessing these cutting-edge technologies to develop superior search applications that not only comprehend the surface-level data but delve deeper to understand the semantic meaning of your data, substantially improving your information retrieval applications.
LLMs augmented with vector space models can enrich text-based embeddings (vector representations of text) and lay the groundwork for building knowledge-based semantic search applications, a step beyond simple keyword searches.
A Deep Dive into Vector Embeddings
Vector embeddings serve as numerical representations of data that interpret the semantic meaning of lexemes, sentences or entire documents. In a nutshell, they translate text into vectors, numerically corresponding to the semantic meaning of words in a high-dimension vector space. This conversion allows us to take a comparative measure of similarity between distinct data based on their contextual meaning rather than just the surface-level data.
- Vector embeddings offer a step beyond mere keyword matches to perform semantic searches.
- They play a crucial role in tasks such as information retrieval, text clustering, recommendation engines, natural language understanding and processing, machine translation, and sentiment analysis.
Overall, these embeddings provide a richer representation of textual data by taking into account their contextual meaning, advancing computer’s understanding of human languages.
How to Build Vector Embeddings for Semantic Similarity Searches
To execute semantic similarity searches with precise results, it is essential first to convert our raw textual data into meaningful vector embeddings. Below is an outlined process using Python programming language and the Hugging Face API:
import torch
from transformers import AutoTokenizer, AutoModel
# Loading a pre-trained model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
# Defining a function to convert text to embeddings
def get_embedding(text):
encoded_input = tokenizer(text, padding=True, truncation=True, return_tensors='pt')
with torch.no_grad():
model_output = model(**encoded_input)
embeddings = model_output.last_hidden_state.mean(dim=1)
return embeddings.numpy()
This code snippet begins by loading a pre-trained model and tokenizer using Hugging Face’s transformer library. It then defines a function that leverages the model to convert text into semantic vector embeddings.
Storing and Querying Vector Embeddings in MongoDB with Atlas Vector Search
MongoDB Atlas has introduced Vector Search capabilities, enabling users to efficiently store and query vector embeddings. To effectively use this feature, follow the steps below:
Setting Up MongoDB Atlas
- Begin by registering for a free MongoDB Atlas account at MongoDB Atlas.
- Proceed by creating a new cluster and database.
- Lastly, enable Vector Search in your newly created cluster settings.
Procedures of Creating Vector Search Indices
Upon setting up your database successfully, navigate to the Indexes tab in your cluster to create a vector search index:
- From the Indexes tab in your cluster dashboard, proceed to create a new index.
- Select Vector as the index type to create a vector search index.
- Define the field in your documents where the generated vector embeddings will reside.
Atlas uses the specified fields to find similar vectors and corresponding documents based on geometric closeness, making your document searches precise and accurate.
Implementing a Practical Solution: Building a Semantic Search Application
Now let’s bring the theoretical discussion into reality by building a simple semantic search application that sifts through movie plots:
Preparing the Data
- Start by gathering a robust dataset of movie plots.
- Create vector embeddings for each movie plot using the predefined function earlier.
- Store these movie plots and their corresponding embeddings in a MongoDB Atlas database.
Executing a Semantic Search
// A demonstration using Node.js and the MongoDB driver
const { MongoClient } = require('mongodb');
async function semanticSearch(query) {
const client = new MongoClient('your_mongodb_connection_string');
await client.connect();
const database = client.db('movieDB');
const collection = database.collection('movies');
const queryEmbedding = getEmbedding(query);
const results = await collection.find({
embedding: {
$near: {
$vector: queryEmbedding,
$maxDistance: 0.5
}
}
}).toArray();
console.log(results);
await client.close();
}
This Node.js example queries the MongoDB Atlas database for movies with plots that bear semantic similarity to the user’s search query. This type of search is much more efficient and relevant than traditional keyword-based searches.
Incorporating Retrieval Augmented Generation (RAG) with GPT-4
The RAG architecture blends retrieval mechanisms with advanced generative models like GPT-4 to yield context-specific responses. Follow the steps below to modify a ChatGPT clone and integrate RAG:
Integration with LangChain
LangChain is a powerful framework that simplifies the development of applications powered by large language models like GPT-3 and GPT-4. Here’s how you can incorporate LangChain into your workflows:
- Start by installing LangChain using
pip install langchainin your Python environment. - Set up your OpenAI API key to access OpenAI services such as GPT-3 and GPT-4.
- Configure LangChain to utilize MongoDB Atlas as a vector store for storing and retrieving vector embeddings.
RAG Implementation
from langchain import OpenAI, VectorDBQA
from langchain.vectorstores import MongoDBAtlasVectorSearch
# Set up the vector store
vector_store = MongoDBAtlasVectorSearch(
mongodb_atlas_cluster_uri=\"your_mongodb_connection_string\",
index_name=\"your_vector_index\"
)
# Initialize the QA system
qa = VectorDBQA(
llm=OpenAI(model_name='gpt-4'),
vectorstore=vector_store
)
# Ask a question
response = qa(\"What is the plot of a movie where dreams are within dreams?\")
print(response)
In this section, we set up a QA system that retrieves relevant data from MongoDB Atlas and uses a GPT-4 model to generate precise context-specific responses. This implementation considerably improves your application’s ability to understand and respond to complex queries.
Analyzing the Limitations and Potential of Large Language Models
Despite the immense capabilities of LLMs such as GPT-4, they possess some inherent limitations that users must acknowledge:
- Context Length: These models can process and interpret only a limited amount of text at once.
- Knowledge Cutoff: Owing to the limitations of their training sets, LLMs might lack up-to-date information on recent events or specific topics.
- Hallucinations: These models might produce plausible but invalid or false information.
By integrating vector search with RAG, we can overcome these limitations by providing the model with an extensive, up-to-date context that enhances its response capabilities.
Joining Forces of Vector Search for Amplifying AI Applications
By combining vector embeddings, semantic search, and large language models, we can unlock unexplored possibilities and enrich AI applications:
- Develop conversational chatbots that provide accurate and context-specific information based on your underlying data.
- Create recommendation systems that understand user preferences on a deeper, semantic level, enhancing user experience.
- Elevate search functionalities in applications to deliver relevant and context-specific results, improving user satisfaction.
By leveraging MongoDB Atlas as a vector store for managing your vector embeddings, you can efficiently manage and query your embeddings at scale, improving the speed and accuracy of your AI applications.
Conclusion
Throughout this tutorial, we’ve explored how to combine vector search with large language models to build advanced, semantic-aware applications, a critical skill in enhancing your AI applications with sophisticated search and retrieval capabilities. It’s time now to put your knowledge into practice and begin exploring this fascinating world of semantic search and AI further.
We wish you all the success on your learning journey and as always, happy coding!