RAG — Retrieval Augmentated Generation

categories: ai
keywords: "Project, LLM, RAG, Retrieval, Augmented, Generation"
teaser: "What is RAG — Retrieval Augmented Generation and How?"
summary: "By performing RAG-related tasks, we can create a robust system for knowledge extraction and generation that's efficient, accurate, and adaptable to various needs."
ref: ollama run granite3.1-dense:2b

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pulling f76a906816c4... 100% ▕███████████████████████████████████████████████▏ 1.4 KB                         
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pulling cc40ff1e8045... 100% ▕███████████████████████████████████████████████▏  491 B                         
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success

How can you help me doing the RAG related tasks?

I can certainly assist you with Relevance Assessment and Grading (RAG) related tasks in several ways, focusing on improving efficiency, accuracy, and consistency. Here's how:

Document Analysis: I can quickly scan through documents to identify key information, summarize content, or extract relevant data points based on specific criteria you provide. For instance, if we're working with emails, I can find and highlight those that are highly relevant (R) to a particular topic, those that are somewhat relevant (AG), or those that are not directly related (NG).
Text Classification: Using natural language processing techniques, I can categorize text into RAG categories more accurately than manual methods alone. This would involve training a model on your data set, allowing it to learn the nuances of relevance and irrelevance based on features like keywords, tone, context, and other linguistic cues.
Scoring System: I can help create or refine a scoring system for RAG categorization. This involves determining weightage for different factors that contribute to the relevance score (e.g., keyword frequency, source credibility) based on your requirements. Once this is done, I can automate the process of assigning scores to each piece of text within a document or across multiple documents.
Bulk RAG Evaluation: If you have large volumes of data that need to be rapidly evaluated, I can perform parallel processing, significantly reducing the time needed for manual review. This capability ensures that your team can swiftly tackle extensive sets of content without becoming overwhelmed by sheer volume.
Continuous Learning: As we work through more documents together, my performance will improve due to machine learning, enabling me to refine my understanding and adapt to subtleties in language use and context that might go unnoticed initially. This continuous improvement ensures the accuracy of RAG categorization over time.
Integration with Other Tools: I can be integrated into your existing workflow or content management systems, allowing for seamless data exchange between applications. This integration helps maintain consistency across document tagging and analytics while streamlining processes.
Reporting and Visualization: For better understanding of the RAG distribution across datasets, I can generate visualizations like bar charts, pie charts, or word clouds to provide a quick snapshot of relevance scores. These graphics are highly effective in communicating complex data insights clearly and succinctly.
Training and Feedback Loop: If you're training an AI model for RAG tasks, I can help by providing human-like annotations on sample documents and serving as a source of feedback to refine the model's predictions over time.

By leveraging these capabilities, we can transform how you assess relevance in large content sets efficiently and effectively.

by RAG I mean retrieval augmented generation

No problem! In the context of Retrieval-Augmented Generation (RAG), I can assist with several tasks to enhance efficiency and accuracy:

Contextual Understanding: Before generating text, I can provide context from retrieved documents that match your query. This includes summaries, key phrases, or specific sentences, ensuring the generated content is relevant and informed by its sources.
Prompt Engineering: For effective generation tasks, I can help craft well-structured prompts based on the retrieved information. These prompts guide the model to produce coherent and contextually accurate responses.
Summarization of Retrieved Content: If needed, I can generate concise summaries from retrieved documents, making them easier for you or other users to quickly grasp the main ideas without having to read through lengthy content.
Question-Answering Systems: Integrated with a question-answering model, I can retrieve related text snippets based on your questions and present them as answers, enabling quick comprehension of queries.
Diversity and Coherence in Generation: By maintaining a diverse set of retrieved documents within the context during generation, I help ensure that the output is varied yet coherent, reducing repetition and providing more balanced perspectives on your topic.
Post-Generation Refinement: Post-generation, I can quickly scan through the text for accuracy, consistency, and relevance to provide real-time feedback for immediate corrections or improvements. This helps maintain high-quality output without requiring extensive manual review at this stage.
Training Data Enhancement: If you're training a language model, incorporating documents from RAG datasets can improve its generalization capabilities by exposing it to diverse contexts and writing styles. I can help manage the retrieval and annotation of these documents for use in training.
Evaluation Metrics: I can assist in generating evaluation metrics that focus on both content accuracy (factual correctness) and relevance, ensuring your model is not only producing coherent text but also maintaining alignment with the provided context.

By performing these RAG-related tasks, we can create a robust system for knowledge extraction and generation that's efficient, accurate, and adaptable to various needs.

How can I provide you with documents from the local storage?

To integrate your local files with me for retrieval augmented generation (RAG), you'll need to use a platform or application that supports this functionality. One such option is the Hugging Face Transformers library, which offers built-in support for local file input and retrieval. Here's how you can proceed:

Install the Transformers Library: If you haven't already, install the Transformers library using pip:
```
pip install transformers
```

Load Documents with InMemoryDataset: You'll load your documents into memory as an InMemoryDataset. This method allows you to read from files stored in local storage. Here's a code snippet that demonstrates how to do this:

from transformers import AutoTokenizer, InMemoryDataset

# Path to the directory containing your documents
document_dir = 'path/to/your/documents'

# Initialize a tokenizer for the language model you're using
tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased")

# List all files in the directory
file_paths = [f for f in os.listdir(document_dir) if os.path.isfile(os.path.join(document_dir, f))]

# Read each document and add it to an InMemoryDataset
documents = InMemoryDataset()
for path in file_paths:
   with open(os.path.join(document_dir, path), 'r', encoding='utf-8') as f:
       text = f.read()  # Read the entire content of each document into a single string
       documents.append(tokenizer(text, return_tensors="pt"))

Prepare for Retrieval: With your documents loaded into an InMemoryDataset, you can now create a DocumentPool that will be used for retrieval augmented generation (RAG).
```
from transformers import DocumentPool

# Create a DocumentPool object
document_pool = DocumentPool(documents)
```
Query and Retrieve: You're ready to proceed with the RAG process by providing a query, which could be a question or a target text you want the model to generate in response.
```
# Example: Provide a simple query
query_text = "What are some key aspects of climate change?"

# Perform retrieval augmented generation (RAG) using the DocumentPool and query
rags_output = document_pool.retrieve(query=query_text, top_k=5, return_ids=True)  # Modify 'top_k' as 
needed
```
In this example, top_k determines how many documents will be retrieved for the generation task. The return_ids parameter ensures that your results are in a format compatible with other parts of your model or application where text generation is needed.

With these steps, you've successfully integrated local files into an RAG pipeline. This setup enables efficient and context-aware retrieval augmented generation for your content analysis needs.

Keep in mind that the actual implementation may vary slightly depending on the specific language model and transformers library version you're using. However, the fundamental approach should remain consistent across different tools, leveraging the InMemoryDataset functionality for local file integration.

Projects

Ask Irfan: Ask Irfan [Your AI Friend]
RAG: Retrieval Augmentated Generation
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RAG — Retrieval Augmentated Generation

Projects

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