How to use Groq to Chat with a Website
How to use Groq with a Streamlit RAG to chat with Websites
We already used Groq together with Streamlit to create a Youtube Video summarizer.
Now, it’s time to use Groq API to chat with Websites.
Streamlit RAG with Groq
- Fully available on GitHub ✅
What it is Powering this Project
- FAISS as Vector Store
- LLama2 as Embedding Model
- LLM Mixtral-8x7b (Via Groq API)
By default it will summarize the content of:
It has to create the embeddings - which will be the time consuming part (one time).
Later, the calls to Mixtral through Groq API will be pretty quick.
FAQ
Other F/OSS RAG Frameworks
https://fossengineer.com/embedchain-ai/
It abstracts the process of
But What it is a RAG?
Other F/OSS Projects Implementing a RAG
- It provides a Streamlit UI, to use together with OpenAI and HF API’s
- As a Vector Store it uses FAISS - A F/OSS Vector Database
- For the RAG, it uses LangChain
DifyAI - a development platform specifically designed for LLMs
- Integrates well with Retrieve, Ask, Generate (RAG) pipelines. RAG pipelines allow your LLM workflows to access and process information from the real world through search engines or other external sources, enhancing the capabilities of your LLM applications.
F/OSS Vector Databases for AI Projects
FAISS is great and open sourced with the MIT ❤️.
But that’s not all regarding Vector Stores, there are other popular alternatives like: ChromaDB, Qdrant, PineCone, …
Vector databases are specialized databases designed to handle vector data efficiently. They are essential for managing, indexing, and querying high-dimensional vectors, like embeddings, generated from text, images, or other types of data. Here’s an in-depth exploration of vector databases and their relationship with embeddings:
Handling Embeddings:
- Embeddings are vector representations of data, and vector databases are engineered to handle these representations efficiently. They store, manage, and allow for the retrieval of embeddings, facilitating operations that would be difficult or inefficient in traditional databases.
Nearest Neighbor Search:
- One common operation in vector databases is nearest neighbor search, which identifies the vectors in the database that are closest to a given query vector. This is crucial for many applications like similarity search, recommendation systems, etc., where finding similar items (in terms of cosine similarity or Euclidean distance) is essential.
Indexing and Scalability:
- Vector databases use specialized indexing techniques to ensure that nearest neighbor searches and other operations are fast, even as the amount of data grows. They are designed to scale to handle large numbers of vectors.
Integration with Machine Learning Pipelines:
- Vector databases are often integrated into machine learning pipelines, where embeddings generated by models are stored and queried to provide real-time or near-real-time responses to user queries.
Examples of Vector Databases:
- FAISS (Facebook AI Similarity Search): An efficient similarity search and clustering library which is particularly optimized for handling large databases of vectors.
- Milvus: An open-source vector database that supports similarity search and analytics, capable of handling large-scale vector data.
- Pinecone: A managed vector database service that simplifies the deployment of applications based on vector search.
- Elasticsearch: While primarily known as a search and analytics engine, Elasticsearch has capabilities for vector similarity search using its dense vector data type and similarity functions.
- Annoy (Approximate Nearest Neighbors Oh Yeah): A library for approximate nearest neighbor search, useful for handling vector data efficiently.
Benefits for Embedding-based Applications:
- Vector databases enhance the utility of embeddings by providing a robust infrastructure for managing vector data. They allow applications to leverage the semantic relationships captured in embeddings for various use cases like semantic search, content recommendation, and more.
Performance Optimization:
- By utilizing efficient indexing and query optimization techniques, vector databases ensure that operations on embeddings are performed in a timely manner, which is crucial for interactive applications and services.
Facilitating Analysis and Insight Extraction:
- By organizing and indexing embeddings, vector databases facilitate the analysis of high-dimensional data, aiding in the extraction of insights and the understanding of relationships within the data.
You can Manage Vector DBs with UI in the blink of an eye
What are Embeddings?
Embeddings are a fundamental concept in machine learning and natural language processing (NLP).
They are used to convert categorical variables, text, or other non-numeric data into a numeric format that can be used by machine learning algorithms. Here’s a detailed explanation of embeddings and their significance:
0$ embeddings- https://www.youtube.com/watch?v=QdDoFfkVkcw
Vector Representation:
- Embeddings are essentially a way to represent text or other types of data as vectors in a multi-dimensional space. The elements of these vectors capture meaningful relationships and characteristics of the data.
Dimensionality Reduction:
- Embeddings often serve as a form of dimensionality reduction, where high-dimensional data is mapped to a lower-dimensional space while preserving relevant information.
Semantic Relationships:
- In the context of text, embeddings can capture semantic relationships between words or phrases. For example, word embeddings like Word2Vec or GloVe can encapsulate relationships such as synonymy, antonymy, or analogy.
Contextual Embeddings:
- Modern NLP models like BERT or GPT create contextual embeddings, where the vector representation of a word is influenced by the words around it, thus capturing the context in which a word appears.
Applications:
- Embeddings are used in a wide range of NLP tasks including text classification, sentiment analysis, machine translation, and similarity measurement among others. They are crucial for feeding text data into neural networks.
Training:
- Embeddings can be learned from data as part of a machine learning model training process, or pre-trained embeddings can be used. Pre-trained embeddings are often learned from large text corpora and are beneficial for tasks with limited training data.
Entity Embeddings:
- Beyond text, embeddings can also be used for other types of data like categorical variables in tabular data. Entity embeddings, for instance, can be used to convert categorical variables into a numeric format that captures some of the inherent structure of the categories.
Embedding Layers:
- In neural networks, an embedding layer is often used to convert categorical variables or text into a dense vector representation that can be learned from the data.
Embedding Space:
- The space in which embeddings reside is often explored and analyzed to understand the relationships and structures captured by the embeddings, and to visualize the learned representations.
Distance and Similarity Measures:
- In the embedding space, distance measures like cosine similarity or Euclidean distance can be used to quantify the similarity or dissimilarity between entities (e.g., words, documents, etc.).
Embeddings are a cornerstone for handling non-numeric data in machine learning models, enabling the capture and utilization of complex relationships and semantic meanings inherent in the data.
They form the basis for many advanced machine learning and NLP techniques, contributing significantly to the progress and achievements in the field.