# Install required packages!pip install -q transformers==4.34.0!pip install -q sentence-transformers==2.2.2!pip install -q faiss-cpu==1.7.4!pip install -q accelerate==0.23.0!pip install -q einops==0.7.0!pip install -q langchain==0.0.312!pip install -q langchain_community!pip install -q pypdf==3.15.1

import torch# Check if GPU is availableprint(f"GPU available: {torch.cuda.is_available()}")if torch.cuda.is_available():   print(f"GPU name: {torch.cuda.get_device_name(0)}")else:   print("Running on CPU. We'll use a CPU-compatible model.")

import osimport tempfile# Create a temporary directory for our documentsdocs_dir = tempfile.mkdtemp()print(f"Created temporary directory at {docs_dir}")# Create sample documents about AI conceptsdocuments = {   "vector_databases.txt": """   Vector databases are specialized database systems designed to store, manage, and search vector embeddings efficiently.   They are crucial for machine learning applications, particularly those involving natural language processing and image recognition.     Key features of vector databases include:   1. Fast similarity search using algorithms like HNSW, IVF, or exact search   2. Support for various distance metrics (cosine, euclidean, dot product)   3. Scalability for handling billions of vectors   4. Often support for metadata filtering alongside vector search     Popular vector databases include FAISS (Facebook AI Similarity Search), Pinecone, Weaviate, Milvus, and Chroma.   FAISS specifically was developed by Facebook AI Research and is an open-source library for efficient similarity search.   """,     "embeddings.txt": """   Embeddings are dense vector representations of data in a continuous vector space.   They capture semantic meaning and relationships between entities by positioning similar items closer together in the vector space.     Types of embeddings include:   1. Word embeddings (Word2Vec, GloVe)   2. Sentence embeddings (Universal Sentence Encoder, SBERT)   3. Document embeddings   4. Image embeddings   5. Audio embeddings     Embeddings are created through various techniques, including neural networks trained on specific tasks.   Modern embedding models like those from OpenAI, Cohere, or Sentence Transformers can capture nuanced semantic relationships.     The dimensionality of embeddings typically ranges from 100 to 1536 dimensions, with higher dimensions often capturing more information but requiring more storage and computation.   """,     "rag_systems.txt": """   Retrieval-Augmented Generation (RAG) is an AI architecture that combines information retrieval with text generation.     The RAG process typically works as follows:   1. User query is converted into an embedding vector   2. Similar documents or passages are retrieved from a knowledge base using vector similarity   3. Retrieved content is provided as context to the language model   4. The language model generates a response informed by both its parameters and the retrieved information     Benefits of RAG include:   1. Reduced hallucination compared to pure generative approaches   2. Up-to-date information without model retraining   3. Attribution of information sources   4. Lower computation costs than increasing model size     RAG systems can be enhanced through techniques like reranking, query reformulation, and hybrid search approaches.   """}# Write documents to filesfor filename, content in documents.items():   with open(os.path.join(docs_dir, filename), 'w') as f:       f.write(content)      print(f"Created {len(documents)} documents in {docs_dir}")

from langchain_community.document_loaders import TextLoaderfrom langchain.text_splitter import RecursiveCharacterTextSplitter# Initialize a list to store our documentsall_documents = []# Load each text filefor filename in documents.keys():   file_path = os.path.join(docs_dir, filename)   loader = TextLoader(file_path)   loaded_docs = loader.load()   all_documents.extend(loaded_docs)print(f"Loaded {len(all_documents)} documents")# Split documents into chunkstext_splitter = RecursiveCharacterTextSplitter(   chunk_size=500,   chunk_overlap=50,   separators=["nn", "n", ".", " ", ""])document_chunks = text_splitter.split_documents(all_documents)print(f"Created {len(document_chunks)} document chunks")# Let's look at a sample chunkprint("nSample chunk content:")print(document_chunks[0].page_content)print(f"Source: {document_chunks[0].metadata}")

from sentence_transformers import SentenceTransformerimport numpy as np# Initialize the embedding modelmodel_name = "sentence-transformers/all-MiniLM-L6-v2"  # A good balance of speed and qualityembedding_model = SentenceTransformer(model_name)print(f"Loaded embedding model: {model_name}")print(f"Embedding dimension: {embedding_model.get_sentence_embedding_dimension()}")# Create embeddings for all document chunkstexts = [doc.page_content for doc in document_chunks]embeddings = embedding_model.encode(texts)print(f"Created {len(embeddings)} embeddings with shape {embeddings.shape}")

import faiss# Get the dimensionality of our embeddingsdimension = embeddings.shape[1]# Create a FAISS index - we'll use a simple Flat L2 index for demonstration# For larger datasets, consider using indexes like IVF or HNSW for better performanceindex = faiss.IndexFlatL2(dimension)  # L2 is Euclidean distance# Add our vectors to the indexindex.add(embeddings.astype(np.float32))  # FAISS requires float32print(f"Created FAISS index with {index.ntotal} vectors")# Create a mapping from index position to document chunk for retrievalindex_to_doc_chunk = {i: doc for i, doc in enumerate(document_chunks)}

from transformers import AutoTokenizer, AutoModelForCausalLM# We'll use a smaller model that works on CPUmodel_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"# Load the tokenizer and modeltokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(   model_id,   torch_dtype=torch.float32,  # Use float32 for CPU compatibility   device_map="auto"  # Will use CPU if GPU is not available)print(f"Successfully loaded {model_id}")

def rag_response(query, index, embedding_model, llm_model, llm_tokenizer, index_to_doc_map, top_k=3):   """   Generate a response using the RAG pattern.   Args:       query: The user's question       index: FAISS index       embedding_model: Model to create embeddings       llm_model: Language model for generation       llm_tokenizer: Tokenizer for the language model       index_to_doc_map: Mapping from index positions to document chunks       top_k: Number of documents to retrieve   Returns:       response: The generated response       sources: The source documents used   """   # Step 1: Convert query to embedding   query_embedding = embedding_model.encode([query])   query_embedding = query_embedding.astype(np.float32)  # Convert to float32 for FAISS   # Step 2: Search for similar documents   distances, indices = index.search(query_embedding, top_k)   # Step 3: Retrieve the actual document chunks   retrieved_docs = [index_to_doc_map[idx] for idx in indices[0]]   # Create context from retrieved documents   context = "nn".join([doc.page_content for doc in retrieved_docs])   # Step 4: Create prompt for the LLM (TinyLlama format)   prompt = f"""<|system|>You are a helpful AI assistant. Answer the question based only on the provided context.If you don't know the answer based on the context, say "I don't have enough information to answer this question."Context:{context}<|user|>{query}<|assistant|>"""   # Step 5: Generate response from LLM   input_ids = llm_tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)   generation_config = {       "max_new_tokens": 256,       "temperature": 0.7,       "top_p": 0.95,       "do_sample": True   }   # Generate the output   with torch.no_grad():       output = llm_model.generate(           input_ids=input_ids,           *generation_config       )   # Decode the output   generated_text = llm_tokenizer.decode(output[0], skip_special_tokens=True)   # Extract the assistant's response (remove the prompt)   response = generated_text.split("<|assistant|>")[-1].strip()   # Return both the response and the sources   sources = [(doc.page_content, doc.metadata) for doc in retrieved_docs]   return response, sources

#Define some test questionstest_questions = [   "What is FAISS and what is it used for?",   "How do embeddings capture semantic meaning?",   "What are the benefits of RAG systems?",   "How does vector search work?"]# Test our RAG pipelinefor question in test_questions:   print(f"nn{'='50}")   print(f"Question: {question}")   print(f"{'='*50}n")   response, sources = rag_response(       query=question,       index=index,       embedding_model=embedding_model,       llm_model=model,       llm_tokenizer=tokenizer,       index_to_doc_map=index_to_doc_chunk,       top_k=2  # Retrieve top 2 most relevant chunks   )   print(f"Response: {response}n")   print("Sources:")   for i, (content, metadata) in enumerate(sources):       print(f"nSource {i+1}:")       print(f"Metadata: {metadata}")       print(f"Content snippet: {content[:100]}...")

def evaluate_rag_response(question, response, retrieved_sources, ground_truth_sources=None):   """   Simple evaluation of RAG response quality   Args:       question: The query       response: Generated response       retrieved_sources: Sources used for generation       ground_truth_sources: (Optional) Known correct sources   Returns:       evaluation metrics   """   # Basic metrics   response_length = len(response.split())   num_sources = len(retrieved_sources)   # Simple relevance score - we'd use better methods in production   sourcerelevance = []   for content,  in retrieved_sources:       # Count overlapping words between question and source       q_words = set(question.lower().split())       s_words = set(content.lower().split())       overlap = len(q_words.intersection(s_words))       source_relevance.append(overlap / len(q_words) if q_words else 0)   avg_relevance = sum(source_relevance) / len(source_relevance) if source_relevance else 0   return {       "response_length": response_length,       "num_sources": num_sources,       "source_relevance_scores": source_relevance,       "avg_relevance": avg_relevance   }# Evaluate one of our previous responsesquestion = test_questions[0]response, sources = rag_response(   query=question,   index=index,   embedding_model=embedding_model,   llm_model=model,   llm_tokenizer=tokenizer,   index_to_doc_map=index_to_doc_chunk,   top_k=2)# Run evaluationeval_results = evaluate_rag_response(question, response, sources)print(f"nEvaluation results for question: '{question}'")for metric, value in eval_results.items():   print(f"{metric}: {value}")

# Here's the implementation of the expand_query function:def expand_query(original_query, llm_model, llm_tokenizer):   """   Generate multiple search queries from an original query to improve retrieval   Args:       original_query: The user's original question       llm_model: The language model for generating variations       llm_tokenizer: Tokenizer for the language model   Returns:       List of query variations including the original   """   # Create a prompt for query expansion   prompt = f"""<|system|>You are a helpful assistant. Generate two alternative versions of the given search query.The goal is to create variations that might help retrieve relevant information.Only list the alternative queries, one per line. Do not include any explanations, numbering, or other text.<|user|>Generate alternative versions of this search query: "{original_query}"<|assistant|>"""   # Generate variations   input_ids = llm_tokenizer(prompt, return_tensors="pt").input_ids.to(llm_model.device)   with torch.no_grad():       output = llm_model.generate(           input_ids=input_ids,           max_new_tokens=100,           temperature=0.7,           do_sample=True       )   # Decode the output   generated_text = llm_tokenizer.decode(output[0], skip_special_tokens=True)   # Extract the generated variations   response_part = generated_text.split("<|assistant|>")[-1].strip()   # Split response by lines to get individual variations   variations = [line.strip() for line in response_part.split('n') if line.strip()]   # Ensure we have at least some variations   if not variations:       variations = [original_query]   # Add the original query and return the list with duplicates removed   all_queries = [original_query] + variations   return list(dict.fromkeys(all_queries))  # Remove duplicates while preserving order

# Example usage of expand_query functiontest_query = "How does FAISS help with vector search?"# Generate query variationsexpanded_queries = expand_query(   original_query=test_query,   llm_model=model,   llm_tokenizer=tokenizer)print(f"Original Query: {test_query}")print(f"Expanded Queries:")for i, query in enumerate(expanded_queries):   print(f"  {i+1}. {query}")# Enhanced RAG with query expansionall_retrieved_docs = []all_scores = {}# Retrieve documents for each query variationfor query in expanded_queries:   # Get query embedding   query_embedding = embedding_model.encode([query]).astype(np.float32)   # Search in FAISS index   distances, indices = index.search(query_embedding, 3)   # Track document scores across queries (using 1/(1+distance) as score)   for idx, dist in zip(indices[0], distances[0]):       score = 1.0 / (1.0 + dist)       if idx in all_scores:           # Take max score if document retrieved by multiple query variations           all_scores[idx] = max(all_scores[idx], score)       else:           all_scores[idx] = score# Get top documents based on scorestop_indices = sorted(all_scores.keys(), key=lambda idx: all_scores[idx], reverse=True)[:3]expanded_retrieved_docs = [index_to_doc_chunk[idx] for idx in top_indices]print("nRetrieved documents using query expansion:")for i, doc in enumerate(expanded_retrieved_docs):   print(f"nResult {i+1}:")   print(f"Source: {doc.metadata['source']}")   print(f"Content snippet: {doc.page_content[:150]}...")# Now use these documents with the LLM to generate a responsecontext = "nn".join([doc.page_content for doc in expanded_retrieved_docs])# Create prompt for the LLMprompt = f"""<|system|>You are a helpful AI assistant. Answer the question based only on the provided context.If you don't know the answer based on the context, say "I don't have enough information to answer this question."Context:{context}<|user|>{test_query}<|assistant|>"""# Generate responseinput_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)with torch.no_grad():   output = model.generate(       input_ids=input_ids,       max_new_tokens=256,       temperature=0.7,       top_p=0.95,       do_sample=True   )# Extract responsegenerated_text = tokenizer.decode(output[0], skip_special_tokens=True)response = generated_text.split("<|assistant|>")[-1].strip()print("nFinal RAG Response with Query Expansion:")print(response)