Local Agentic RAG: Revolutionizing LLMs with Private Knowledge

Introduction: The Promise of 10x Performance

In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools for managing and interpreting vast amounts of information. However, a significant challenge remains: how can we make these models perform exceptionally well with private, organization-specific knowledge?

Enter Local Agentic RAG (Retrieval Augmented Generation) - a cutting-edge approach that promises to make LLMs perform "10x better with private knowledge". This isn't just an incremental improvement; it's a paradigm shift in how we leverage AI for information retrieval and processing within organizations.

"I want Llama3 to perform 10x with my private knowledge" - The driving vision behind Local Agentic RAG

The Evolution of Knowledge Integration in LLMs

To appreciate the revolutionary nature of Local Agentic RAG, let's first understand the traditional methods of integrating private knowledge into LLMs:

The Limitations of Simple RAG

While Simple RAG represented a significant step forward, it still faces several challenges when dealing with real-world, messy data:

• Difficulty in processing non-textual data (e.g., images, tables, code snippets)
• Inconsistent performance across different data types and query complexities
• Inability to handle multi-hop reasoning or questions requiring synthesized information from multiple sources
• Lack of dynamic adaptation to user intent and context

These limitations set the stage for the next evolution in knowledge integration: Local Agentic RAG.

Local Agentic RAG: A Deep Dive

Local Agentic RAG represents a quantum leap in how we approach knowledge retrieval and integration in LLMs. By incorporating AI agents into the RAG process, we can create a more dynamic, intelligent, and context-aware system.

Key Components of Local Agentic RAG

def adaptive_chunk(text, target_size=500):
  sentences = nltk.sent_tokenize(text)
  chunks = []
  current_chunk = ""
  
  for sentence in sentences:
      if len(current_chunk) + len(sentence) <= target_size:
          current_chunk += sentence + " "
      else:
          chunks.append(current_chunk.strip())
          current_chunk = sentence + " "
  
  if current_chunk:
      chunks.append(current_chunk.strip())
  
  return chunks

def hybrid_search(query, vector_db, keyword_index):
  vector_results = vector_db.similarity_search(query, k=20)
  keyword_results = keyword_index.search(query, k=20)
  
  combined_results = list(set(vector_results + keyword_results))
  
  reranker = SentenceTransformer('cross-encoder/ms-marco-MiniLM-L-6-v2')
  reranke
d_results = reranker.predict([(query, doc.page_content) for doc in combined_results])
  
  return [doc for _, doc in sorted(zip(reranked_results, combined_results), reverse=True)][:10]

The Agentic RAG Pipeline

The heart of Local Agentic RAG lies in its intelligent, agent-driven pipeline:

def query_translator(user_query):
  prompt = f"""
  Translate the following user query into a more comprehensive search query:
  User Query: {user_query}
  Translated Query:
  """
  response = llm(prompt)
  return response.strip()

def metadata_filter(query, docs):
  prompt = f"""
  Given the query "{query}", filter and rank the following documents based on their metadata relevance:
  {docs}
  Return the indices of the top 5 most relevant documents.
  """
  response = llm(prompt)
  return [int(idx) for idx in response.split()]

def corrective_rag_agent(query, vector_db):
  max_iterations = 3
  for i in range(max_iterations):
      translated_query = query_translator(query)
      relevant_docs = hybrid_search(translated_query, vector_db, keyword_index)
      filtered_docs = [relevant_docs[i] for i in metadata_filter(query, relevant_docs)]
      
      answer = generate_answer(query, filtered_docs)
      
      if not is_hallucinating(answer) and answers_question(answer, query):
          return answer
      
      if i < max_iterations - 1:
          query = refine_query(query, answer)
  
  return "I'm sorry, but I couldn't find a satisfactory answer to your question."

def refine_query(original_query, previous_answer):
  prompt = f"""
  The original query was: "{original_query}"
  The previous answer was: "{previous_answer}"
  This answer was not satisfactory. Please refine the original query to get a better answer.
  Refined query:
  """
  return llm(prompt).strip()

Implementing Local Agentic RAG

To implement Local Agentic RAG, follow these steps:

1. Set up your environment:

   pip install langchain gpt4all sentence-transformers nltk
   pip install llama-cpp-python

2. Initialize Llama 3:

   from llama_cpp import Llama
   
   llm = Llama(model_path="path/to/llama-3-model.bin", n_ctx=2048, n_threads=4)
   
   def llm(prompt):
     return llm(prompt, max_tokens=100)['choices'][0]['text']

3. Set up your vector database and keyword index:

   from langchain.vectorstores import Chroma
   from langchain.embeddings import HuggingFaceEmbeddings
   from langchain.text_splitter import RecursiveCharacterTextSplitter
   
   embeddings = HuggingFaceEmbeddings()
   text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
   
   docs = text_splitter.split_documents(your_documents)
   vector_db = Chroma.from_documents(docs, embeddings)
   
   # Set up a simple keyword index (you might want to use a more sophisticated solution in production)
   keyword_index = {doc.page_content: doc for doc in docs}

4. Implement the Agentic RAG pipeline:

   Use the code snippets provided in the previous section to implement the query translator, metadata filter, and corrective RAG agent.

5. Use the Local Agentic RAG system:

   user_query = "What are the key benefits of using Local Agentic RAG?"
   answer = corrective_rag_agent(user_query, vector_db)
   print(answer)

Conclusion: The Future of LLMs and Private Knowledge

Local Agentic RAG represents a monumental leap forward in our ability to leverage LLMs with private, organization-specific knowledge. By combining advanced parsing, intelligent chunking, hybrid search, and AI agents, we can create a system that truly performs "10x better" with private knowledge.

The benefits of this approach are manifold:

• Dramatically improved accuracy and relevance of responses
• Ability to handle complex, multi-faceted queries
• Dynamic adaptation to user intent and context
• Efficient processing of diverse data types
• Reduced hallucination and increased reliability

As we continue to refine and expand upon the Local Agentic RAG approach, we open up new possibilities for AI-driven information retrieval, knowledge management, and decision support across a wide range of industries and applications.

The future of LLMs is not just about bigger models or more data—it's about smarter, more adaptive systems that can truly understand and leverage the unique knowledge within each organization. Local Agentic RAG is a significant step towards that future.

Video Resources

For a deeper dive into the concepts of Local Agentic RAG, check out this informative video: