rag.py

# %%

import os
import json


import torch
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer
from transformers import (
    pipeline,
    TextGenerationPipeline,
    AutoModelForCausalLM,
    AutoTokenizer,
)


HF_TOKEN = os.environ["hf_token"]


SYSTEM_PROMPT = """You are a helpful question answering assistant. You will be given a context and a question. You need to provide the answer to the question based on the context. Answer briefly, based on the context. Only output the answer, and nothing else. Here is an example:

>> Context
Fascin is an actin-bundling protein that induces membrane protrusions and cell motility after the formation of lamellipodia or filopodia. Fascin expression has been associated with progression or prognosis in various neoplasms; however, its role in intrahepatic cholangiocarcinoma is unknown.

>> Question
What type of protein is fascin?

>> Answer
Actin-bundling protein

Now answer the user's question based on the user's given context.
"""

USER_PROMPT = """
>> Context
{context}

>> Question
{question}

>> Answer
"""



def load_embedder(model_path: str, device: str) -> SentenceTransformer:
    embedder = SentenceTransformer(model_path)
    embedder.to(device)
    return embedder



def load_contexts(context_file: str) -> list[str]:
    contexts = []
    with open(context_file, "r") as f_in:
        for line in f_in:
            context = json.loads(line)
            contexts.append(context["context"])

    return contexts



def load_index(index_file: str) -> faiss.Index:
    return faiss.read_index(index_file)



def load_reader(model_path: str, device: str) -> TextGenerationPipeline:
    model = AutoModelForCausalLM.from_pretrained(model_path, token=HF_TOKEN)

    tokenizer = AutoTokenizer.from_pretrained(model_path, token=HF_TOKEN)
    tokenizer.pad_token = tokenizer.eos_token

    reader = pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        torch_dtype=torch.bfloat16,
        token=HF_TOKEN,
        device=device,
    )

    return reader



def construct_prompt(contexts: list[str], question: str) -> list[dict]:
    return [
        {"role": "system", "content": SYSTEM_PROMPT},
        {
            "role": "user",
            "content": USER_PROMPT.format(
                context="\n".join(contexts), question=question
            ),
        },
    ]



def load_all(
    embedder_path: str,
    context_file: str,
    index_file: str,
    reader_path: str,
) -> tuple[SentenceTransformer, list[str], faiss.Index, TextGenerationPipeline]:
    embedder = load_embedder(embedder_path, "cpu")
    contexts = load_contexts(context_file)
    index = load_index(index_file)
    reader_device = "cuda" if torch.cuda.is_available() else "cpu"
    reader = load_reader(reader_path, reader_device)

    return {
        "embedder": embedder,
        "contexts": contexts,
        "index": index,
        "reader": reader,
    }



def run_query(
    question: str,
    embedder: SentenceTransformer,
    index: faiss.Index,
    contexts: list[str],
    reader: TextGenerationPipeline,
    top_k: int = 3,
) -> tuple[list[int], list[str], str]:
    query_embedding = embedder.encode([question], normalize_embeddings=True)
    _, retrieved_context_ids = index.search(query_embedding, top_k)
    retrieved_context_ids = np.array(retrieved_context_ids)  # shape: (1, top_k)

    retrieved_contexts = []
    for row in retrieved_context_ids:
        retrieved_contexts.append(
            [contexts[i] if contexts[i] is not None else "" for i in row]
        )

    # The code below is for a single question.
    prompt = construct_prompt(retrieved_contexts[0], question)
    answer = reader(prompt, max_new_tokens=128, return_full_text=False)
    print(answer)
    answer_text = answer[0]["generated_text"]
    if ">> Answer" in answer_text:
        answer_text = answer_text.split(">> Answer")[1].strip()

    return retrieved_context_ids[0].tolist(), retrieved_contexts[0], answer_text


# %%
# embedder_path = "Snowflake/snowflake-arctic-embed-l"
# reader_path = "meta-llama/Llama-3.2-1B-Instruct"
# context_file = "../data/bioasq_contexts.jsonl"
# index_file = "../data/bioasq_contexts__snowflake-arctic-embed-l__float32_hnsw.index"

# embedder, contexts, index, reader = load_all(
#     embedder_path, "cpu", context_file, index_file, reader_path, "mps"
# )

# query = "What cellular structures does fascin induce?"

# retrieved_context_ids, retrieved_contexts, answer_text = run_query(
#     query, embedder, index, contexts, reader
# )


# %%