The Embedchain Project allows us to utilize a combination of resources from various sources.

EmbedChain Focuses on Data Management and LLM Integration - It excels at personalizing LLM responses by creating data embeddings that are tailored to the specific application.

It prepares data for LLMs, generates embeddings, and simplifies the process of interacting with LLMs.

  • 🎯 Today’s Objective:
  • These are the embedchain key components:
    • Data Sources: Harness a wide array of data sources as outlined in the Embedchain documentation. Data Sources Overview combined with various models.
    • Models: Access multiple models, including local and private ones like Ollama and GPT4All, for diverse applications. Explore the Models
    • Hugging Face Support: The project also includes support for models from Hugging Face, expanding its versatility. Hugging Face Integration
    • Vector Databases: Leverage vector databases integral to handling large datasets effectively. Vector Databases
    • Embedding Models: Utilize sophisticated embedding models for advanced data processing and analysis. Embedding Models

This structure offers a clear, organized view of the resources and features available through the Embedchain Project, enhancing navigation and understanding for readers.

The EmbedChain Project

  • Embedchain Key Features: đź”—
    • Data Streamlining: Simplifies managing unstructured data for training and personalizing LLMs. Segments data, generates embeddings, and stores them in a vector database for efficient retrieval.
    • Personalization Capabilities: Enables personalized responses by feeding the LLM with user-specific data embeddings.
    • Ease of Use: Emphasizes a “conventional but configurable” approach, catering to both software engineers and machine learning specialists. Offers a user-friendly interface and configuration options for specific needs.

Installing EmbedChain

Conda

https://docs.conda.io/projects/miniconda/en/latest/

conda --version
# conda create --name embedchain python=3.11
# conda activate embedchain
conda install numpy

Venv

# !python -m venv embedchain_venv
#Unix
#!source embedchain_venv/bin/activate
#Windows
#.\embedchain_venv\Scripts\activate

#deactivate

#Get-ExecutionPolicy
#Set-ExecutionPolicy RemoteSigned
#Set-ExecutionPolicy Restricted

!pip install embedchain

If you do pip show embedchain - you will see that it uses LangChain and ChromaDB for the RAG!

Summary: Simplest open source retrieval(RAG) framework
License: Apache License
Requires: beautifulsoup4, chromadb, gptcache, langchain, openai, posthog, pypdf, pysbd, python-dotenv, rich, schema, tiktoken
...

How to Use EmbedChain

Default with OpenAI

export OPENAI_API_KEY=sk-blablabla # on Linux/Mac

#set OPENAI_API_KEY=sk-blablabla #bash
$env:OPENAI_API_KEY = "sk-blablabla" #PS

# Create a bot instance
os.environ["OPENAI_API_KEY"] = "your_API_key"

With Ollama Locally

  1. Get Ollama Ready
  2. Follow: https://docs.embedchain.ai/components/llms#ollama

Code Scheleton for EmbedChain

import os
from embedchain import Pipeline as App

# Create a bot instance
#os.environ["OPENAI_API_KEY"] = "sk-..."

elon_bot = App()

# Embed online resources
elon_bot.add("https://en.wikipedia.org/wiki/Elon_Musk")
elon_bot.add("https://www.forbes.com/profile/elon-musk")

# Query the bot
elon_bot.query("How many companies does Elon Musk run and name those?")
# Answer: Elon Musk currently runs several companies. As of my knowledge, he is the CEO and lead designer of SpaceX, the CEO and product architect of Tesla, Inc., the CEO and founder of Neuralink, and the CEO and founder of The Boring Company. However, please note that this information may change over time, so it's always good to verify the latest updates.

EmbedChain for Scrapping Content

One use case for EmbedChain, is to make quick summaries about blog content.

By using Python 3.10.12 and https://pypi.org/project/embedchain/0.1.72/

# conda create --name embedchain python=3.11
# conda activate embedchain

# !python -m venv embedchain_venv


#Unix
#!source embedchain_venv/bin/activate
#Windows
#.\embedchain_venv\Scripts\activate

#deactivate

#Get-ExecutionPolicy
#Set-ExecutionPolicy RemoteSigned
#Set-ExecutionPolicy Restricted

!pip install embedchain #==0.1.72

And now, you can just adapt it to:

bachata = App()

# Embed online resources
bachata.add("https://bachatafests.com/")
bachata.add("https://bachatafests.com/barcelona/")

#elon_bot.add("https://www.forbes.com/profile/elon-musk")

# Query the bot
bachata.query("How festivals are upcoming?")

The provided context does not include information about upcoming festivals. Therefore, I don’t have the information to answer the query.

bachata.query("What do you know about barcelona festivals?")

Based on the given context, we know that Barcelona has various festivals that are celebrated in different neighborhoods or barrios. Some of the popular festivals mentioned include Festa Major de Sants, La Mercè, and Festa Major de Gràcia. These festivals feature music, dancing, parades, fireworks, and giant puppets.

bachata.query("What do you know about bachata in barcelona?")

Based on the given context, we can infer that Barcelona has a strong presence of bachata, a Latin dance style. The Ultimate Guide to discover Barcelona mentions “Flamenco” as one of the things to see and do in Barcelona, indicating that there are venues that offer flamenco shows. Since bachata is also a popular Latin dance style, it is likely that there are venues or events in Barcelona where bachata can be enjoyed as well. However, specific information about bachata in Barcelona, such as festivals or events, is not provided in the given context.

FAQ

Understanding Retrieval-Augmented Generation

RAG, or Retrieval-Augmented Generation, is a technique employed in large language models (LLMs) to enhance the quality and relevance of generated text. Here’s a breakdown of how RAG works and its benefits:

How RAG Works
  • Retrieval 🔍: Uses an initial query or prompt to retrieve relevant documents or passages from a large dataset.
  • Augmentation 🔄: Processes and incorporates the retrieved information into the LLM’s internal representation, utilizing techniques such as summarization and key concept encoding.
  • Generation đź“ť: Leverages the augmented data to generate responses or complete tasks, ensuring the text is on-topic, factual, and coherent.
Benefits of RAG
  • Improved Factual Accuracy: RAG enhances the accuracy and truthfulness of LLM-generated responses by utilizing retrieved information.
  • Enhanced Relevance: Ensures text remains relevant to the initial prompt or query.
  • Better Coherence: Aids in generating more cohesive and structured text by providing contextual support.
  • Popular RAG Implementations: Both utilize RAG to serve distinct purposes, showcasing the potential of RAG in enhancing LLM capabilities
    • LangChain: A framework for building complex LLM applications, integrating RAG within larger workflows for tasks like information retrieval and text generation.
      • A versatile framework that can incorporate RAG for diverse LLM applications.
    • LLAMIndex: Focuses on search and retrieval applications, using RAG to enhance LLM’s ability to search through large text corpora efficiently.
      • Specializes in creating search and retrieval systems using RAG-enhanced LLMs.

FREE Vector DataBases

Ollama embeddings

https://www.youtube.com/watch?v=Ml179HQoy9o

GPUs for ollama https://www.youtube.com/watch?v=QRot1WtivqI

Other F/OSS Q&A