git clone https://github.com/negativenagesh/Medical_Chatbot-Llama2.git
conda create --name mcbot python==3.8 -y
conda activate mcbot
pip install -r requirements.txt
PINECONE_API_KEY= "xxxxxxxxxxxxxxxxxxxxxxxxxx"
PINECONE_API_ENV='xxxxxxxxx'
llama-2-7b-chat.ggmlv3.q4_0.bin
https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main
https://pypi.org/project/ctransformers/0.1.0/
CTransformers (C Transformers) is a library or framework for efficiently using transformer models in various applications. Let's break down the components and the use cases:
The Sentence-Transformer model is a framework for embedding sentences into dense vector representations. It leverages architectures like BERT (Bidirectional Encoder Representations from Transformers) and its variants (e.g., RoBERTa, DistilBERT) to produce high-quality sentence embeddings that capture semantic information. The model is particularly useful for tasks requiring understanding the semantic similarity between sentences or text snippets.
Semantic Similarity: The primary use case for sentence transformers is to compute the semantic similarity between sentences. This is crucial for tasks like duplicate question detection in forums, clustering similar documents, and retrieving semantically related text.
Text Classification: By transforming sentences into embeddings, it becomes easier to apply various machine learning algorithms for classification tasks, such as sentiment analysis or topic classification.
Information Retrieval: Sentence embeddings can significantly improve the performance of search engines by allowing more accurate matching of queries with relevant documents.
Clustering: High-dimensional sentence embeddings can be used for clustering similar sentences or documents, which is valuable in organizing large datasets or identifying thematic patterns.
Summarization: In text summarization tasks, sentence embeddings help in identifying and extracting the most relevant sentences that represent the core content.
Question Answering Systems: To match user questions with relevant pre-existing answers or similar questions.
Chatbots: Enhancing the ability of chatbots to understand user queries and provide relevant responses.
Document Retrieval: Improving search results by retrieving documents based on semantic similarity rather than just keyword matching.
Recommendation Systems: For recommending text-based content, such as news articles, research papers, or books, based on the user's interests.
Paraphrase Identification: Detecting paraphrases in large text datasets, which is useful in data cleaning and deduplication tasks.
Universal Sentence Encoder (USE): Developed by Google, USE provides similar functionality with different architecture optimizations. It is also designed to produce embeddings that can be used for various NLP tasks.
InferSent: A model from Facebook AI that produces sentence embeddings using a combination of GloVe vectors and a BiLSTM network.
QuickThoughts: Developed by researchers at Google, this model learns sentence representations by training on a sequence prediction task.
GloVe and Word2Vec Averages: Averaging word embeddings from models like GloVe or Word2Vec can provide a simple, yet effective way to represent sentences.
ELMo: Embeddings from Language Models (ELMo) generate contextualized word embeddings which can be averaged or otherwise combined to create sentence embeddings.
Transformers Variants: Other transformer-based models, such as XLNet, T5, and GPT-3, can be fine-tuned to produce high-quality sentence embeddings.
https://pypi.org/project/pinecone-client/
Pinecone is a managed vector database service that is designed to handle high-dimensional vector data, which is commonly used in machine learning applications for tasks like similarity search and recommendation systems. The pinecone-client is the software library provided by Pinecone to interact with their service.
Pinecone allows you to store, index, and query high-dimensional vectors efficiently. This is essential for applications that require finding similar items based on vector representations, such as recommendation systems and image similarity search.
Pinecone is designed to handle large-scale vector data and can scale seamlessly as your data grows. This eliminates the need to manage and scale your own infrastructure.
Pinecone provides low-latency and high-throughput queries, which is critical for real-time applications like personalized recommendations or dynamic content retrieval.
The pinecone-client library provides a simple and intuitive API for interacting with Pinecone's managed service, making it easy to integrate into existing applications and workflows.
E-commerce platforms can use Pinecone to recommend products to users based on the similarity of item vectors.
Platforms that need to find similar images or videos based on their visual content can use Pinecone for efficient similarity search.
Applications that require semantic search or text similarity, such as chatbots or document retrieval systems, can benefit from Pinecone's vector search capabilities.
Services that provide personalized content, such as news articles, music, or movies, can use Pinecone to deliver relevant content to users based on their preferences and behavior.
While primarily a text search engine, Elasticsearch has capabilities for vector similarity search through plugins and extensions. It is widely used and integrates well with various data sources.
FAISS is an open-source library developed by Facebook for efficient similarity search and clustering of dense vectors. It is highly optimized and performs well on large datasets.
Annoy is an open-source library developed by Spotify for approximate nearest neighbor search in high-dimensional spaces. It is easy to use and well-suited for read-heavy workloads.
Developed by Google, ScaNN is an open-source library for efficient similarity search in high-dimensional spaces. It offers a balance between accuracy and performance.
Milvus is an open-source vector database designed for scalable similarity search. It supports various indexing methods and is optimized for large-scale vector data.
LangChain is a library designed to facilitate the development of applications powered by language models, such as GPT-4. It provides a framework that simplifies the integration of various components needed for building complex language-based applications.
LangChain is a framework for developing applications using large language models (LLMs). It helps in chaining different components together, allowing developers to create complex workflows and pipelines that utilize the power of LLMs.
It abstracts away many of the complexities involved in working with language models directly.
Allows for the combination of various components like text generation, summarization, translation, etc., into a cohesive workflow.
Supports the creation of custom pipelines and workflows tailored to specific use cases.
Easily integrates with other tools and libraries used in natural language processing (NLP).
Building intelligent and context-aware conversational agents.
Creating concise summaries of long documents.
Automating the creation of articles, blogs, and other content.
Developing multilingual applications that require translation capabilities.
Using language models to extract insights and generate reports from data.
Enhancing virtual assistants with advanced language understanding and generation capabilities.
There are several alternatives to LangChain, each with its own set of features and use cases: Some of the popular ones include:
A popular library for working with transformer models. It provides pre-trained models and tools for various NLP tasks. Use Cases: Text generation, translation, summarization, question answering, etc.
An industrial-strength NLP library that provides tools for tokenization, part-of-speech tagging, named entity recognition, and more. Use Cases: Text processing, named entity recognition, dependency parsing.
A library for working with human language data. It provides tools for text processing and classification. Use Cases: Educational purposes, text processing, linguistic research.
Provides access to OpenAI's language models like GPT-3 and GPT-4 through an API. Use Cases: Text generation, conversation, content creation, etc.
A library built on PyTorch for designing and evaluating deep learning models for NLP. Use Cases: Research and development in NLP, building custom models.
A simple library for processing textual data. It provides a simple API for diving into common NLP tasks. Use Cases: Text processing, sentiment analysis, classification.
This is used to create structured prompts for the language model, ensuring consistency and proper formatting in the inputs fed to the model. It helps in generating better and more reliable outputs.
This chain is designed for question-answering systems that need to retrieve relevant documents before generating an answer. It combines retrieval and generation in one seamless process.
These embeddings convert text into dense vector representations, capturing the semantic meaning. They are essential for tasks like similarity search, document retrieval, and clustering.
This loader is used for extracting text from PDF documents using the PyMuPDF library. It is useful when you need to process and analyze text content from PDFs.
This loader allows you to read multiple documents from a directory. It supports various file types and is helpful for batch processing and loading large sets of documents for analysis or indexing.
This tool splits long texts into smaller, manageable chunks based on character count. It ensures that the text fits within the token limits of language models and helps in efficient text processing and retrieval.
Creating a Pinecone index is essential for managing vector embeddings efficiently. In the context of chatbots, especially those requiring sophisticated natural language understanding and response generation, Pinecone provides the following benefits:
Chatbots often use embeddings (vector representations) of text data to understand and generate responses. Pinecone indexes store these embeddings efficiently, allowing for fast and scalable retrieval.
Pinecone allows for similarity searches, such as finding the closest embeddings to a given query. This is crucial for tasks like finding the most relevant previous conversation snippet or retrieving relevant knowledge base entries.
As the amount of data grows, managing embeddings and ensuring quick retrieval becomes challenging. Pinecone is designed to handle large-scale vector data, making it ideal for chatbots with extensive conversation histories or large knowledge bases.
Pinecone’s serverless approach means it can scale dynamically based on the load, ensuring that your chatbot can handle varying levels of traffic without manual intervention.
Pinecone is optimized for low-latency operations, which is critical for chatbots that need to respond in real-time.
It supports high-throughput operations, enabling the chatbot to handle multiple simultaneous requests efficiently.
Chatbots use vector embeddings to represent text data in a way that captures semantic meaning. Here’s how Pinecone fits into the architecture:
When a user sends a message, the chatbot converts the text into an embedding using a pre-trained model (e.g., BERT, GPT).
The embedding is then used to search the Pinecone index to find the most similar previous queries, intents, or responses.
The chatbot can store and retrieve embeddings of past conversation snippets to maintain context, ensuring more coherent and contextually relevant responses.
For chatbots designed to answer FAQs, user queries can be converted into embeddings and matched against a pre-indexed knowledge base in Pinecone to find the most relevant answers.
Embeddings capture the semantic meaning of text chunks, allowing for better representation and comparison of text data. They help in understanding and retrieving text based on content similarity rather than just keyword matching.
By converting text into vectors, you can use similarity search techniques to quickly find and retrieve relevant information from large datasets. This is crucial for building effective search engines or recommendation systems.
Embeddings allow handling and querying large volumes of text data efficiently. Pinecone, in this case, is optimized for managing and searching vector embeddings at scale.
Term Frequency-Inverse Document Frequency (TF-IDF) represents text based on the importance of terms within a document and across a corpus. It's simpler but doesn't capture semantic meaning as well as embeddings.
Represents text by counting the frequency of each word. It's straightforward but doesn't account for word order or semantics.
These are earlier methods for generating word embeddings that capture semantic meanings but may not be as advanced or context-aware as newer models like those used in Hugging Face's embeddings.
Uses exact or partial keyword matching without semantic understanding, which may be less effective for nuanced queries.
This setup is used to configure and use a specific language model for generating text or responses based on the given parameters.
This setup enables efficient retrieval of a specified number of top documents from Pinecone based on their relevance to the query.
This setup enables efficient retrieval of compressed and contextually relevant information from documents, improving search accuracy and relevance.
This setup allows the QA system to answer questions by retrieving relevant information from documents and generating responses using the specified language model and retrieval strategy.
Imports the warnings module, which handles warning messages.
This is typically done to reduce clutter in the output, especially when warnings are known and not relevant to the current execution context.
This sequence allows for interactive querying of the QA system and provides a pause before the program terminates.