Upload RAG_using_Llama3.py.py

RAG_using_Llama3.py.py

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+# -*- coding: utf-8 -*-
+"""RAG_using_Llama3.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1b-ZDo3QQ-axgm804UlHu3ohZwnoXz5L1
+
+# install dependecies
+"""
+
+!pip install -q datasets sentence-transformers faiss-cpu accelerate
+
+from huggingface_hub import notebook_login
+notebook_login()
+
+"""# embed dataset
+
+this is a slow procedure so you might consider saving your results
+"""
+
+from datasets import load_dataset
+
+dataset = load_dataset("KarthikaRajagopal/wikipedia-2")
+
+dataset
+
+from sentence_transformers import SentenceTransformer
+ST = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
+
+# embed the dataset
+def embed(batch):
+  # or you can combine multiple columns here, for example the title and the text
+  information = batch["text"]
+  return {"embeddings" : ST.encode(information)}
+dataset = dataset.map(embed,batched=True,batch_size=16)
+
+!pip install datasets
+
+from datasets import load_dataset
+
+dataset = load_dataset("KarthikaRajagopal/wikipedia-2",revision = "embedded")
+
+# Push it to your Hugging Face repository
+dataset.push_to_hub("KarthikaRajagopal/wikipedia-2", revision="embedded")
+
+from datasets import load_dataset
+
+dataset = load_dataset("KarthikaRajagopal/wikipedia-2",revision = "embedded")
+
+data = dataset["train"]
+data = data.add_faiss_index("embeddings") # column name that has the embeddings of the dataset
+
+def search(query: str, k: int = 3 ):
+    """a function that embeds a new query and returns the most probable results"""
+    embedded_query = ST.encode(query) # embed new query
+    scores, retrieved_examples = data.get_nearest_examples( # retrieve results
+        "embeddings", embedded_query, # compare our new embedded query with the dataset embeddings
+        k=k # get only top k results
+    )
+    return scores, retrieved_examples
+
+scores , result = search("anarchy", 4 ) # search for word anarchy and get the best 4 matching values from the dataset
+
+# the lower the better
+scores
+
+result['title']
+
+print(result["text"][0])
+
+"""# chatbot on top of the retrieved results"""
+
+!pip install -q datasets sentence-transformers faiss-cpu accelerate bitsandbytes
+
+from sentence_transformers import SentenceTransformer
+ST = SentenceTransformer("mixedbread-ai/mxbai-embed-large-v1")
+
+from datasets import load_dataset
+
+dataset = load_dataset("KarthikaRajagopal/wikipedia-2",revision = "embedded")
+
+data = dataset["train"]
+data = data.add_faiss_index("embeddings") # column name that has the embeddings of the dataset
+
+def search(query: str, k: int = 3 ):
+    """a function that embeds a new query and returns the most probable results"""
+    embedded_query = ST.encode(query) # embed new query
+    scores, retrieved_examples = data.get_nearest_examples( # retrieve results
+        "embeddings", embedded_query, # compare our new embedded query with the dataset embeddings
+        k=k # get only top k results
+    )
+    return scores, retrieved_examples
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+
+model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    quantization_config=bnb_config
+)
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+SYS_PROMPT = """You are an assistant for answering questions.
+You are given the extracted parts of a long document and a question. Provide a conversational answer.
+If you don't know the answer, just say "I do not know." Don't make up an answer."""
+
+def format_prompt(prompt,retrieved_documents,k):
+  """using the retrieved documents we will prompt the model to generate our responses"""
+  PROMPT = f"Question:{prompt}\nContext:"
+  for idx in range(k) :
+    PROMPT+= f"{retrieved_documents['text'][idx]}\n"
+  return PROMPT
+
+def generate(formatted_prompt):
+  formatted_prompt = formatted_prompt[:2000] # to avoid GPU OOM
+  messages = [{"role":"system","content":SYS_PROMPT},{"role":"user","content":formatted_prompt}]
+  # tell the model to generate
+  input_ids = tokenizer.apply_chat_template(
+      messages,
+      add_generation_prompt=True,
+      return_tensors="pt"
+  ).to(model.device)
+  outputs = model.generate(
+      input_ids,
+      max_new_tokens=1024,
+      eos_token_id=terminators,
+      do_sample=True,
+      temperature=0.6,
+      top_p=0.9,
+  )
+  response = outputs[0][input_ids.shape[-1]:]
+  return tokenizer.decode(response, skip_special_tokens=True)
+
+def rag_chatbot(prompt:str,k:int=2):
+  scores , retrieved_documents = search(prompt, k)
+  formatted_prompt = format_prompt(prompt,retrieved_documents,k)
+  return generate(formatted_prompt)
+
+rag_chatbot("what's anarchy ?", k = 2)
+