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import json
import os
from typing import Literal
import openai
from llama_index.core import (
Document,
Settings,
SimpleDirectoryReader,
StorageContext,
VectorStoreIndex,
load_index_from_storage,
)
from llama_index.core.node_parser import (
HierarchicalNodeParser,
SentenceWindowNodeParser,
get_leaf_nodes,
)
from llama_index.core.postprocessor import (
MetadataReplacementPostProcessor,
SentenceTransformerRerank,
)
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import AutoMergingRetriever
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.llms.openai import OpenAI
from src.mythesis_chatbot.utils import get_config_hash, get_openai_api_key
SupportedRags = Literal[
"classic retrieval", "sentence window retrieval", "auto-merging retrieval"
]
SupportedOpenAIllms = Literal["gpt-4o-mini", "gpt-3.5-turbo"]
SupportedEmbedModels = Literal["BAAI/bge-small-en-v1.5"]
SupportedRerankModels = Literal["cross-encoder/ms-marco-MiniLM-L-2-v2"]
def load_data(input_file: str) -> Document:
reader = SimpleDirectoryReader(input_files=[input_file])
documents = reader.load_data() # List of Document objects (one object per page)
# Merge into single document
document = Document(text="\n\n".join([doc.text for doc in documents]))
return document
def build_sentence_window_index(
input_file: str,
save_dir: str,
index_config: dict[str, str | int],
):
config_hash = get_config_hash(index_config)
save_dir = os.path.join(save_dir, "sentence_window", config_hash)
Settings.embed_model = HuggingFaceEmbedding(model_name=index_config["embed_model"])
if not os.path.exists(save_dir):
document = load_data(input_file)
# Create the sentence window node parser w/ default settings.
# A node is a chunck of text. Each node returned by the sentence window node
# parser also contains its context as metadata (closest chuncks of texts)
node_parser = SentenceWindowNodeParser.from_defaults(
window_size=index_config["sentence_window_size"],
window_metadata_key="window",
original_text_metadata_key="original_text",
)
Settings.node_parser = node_parser
sentence_index = VectorStoreIndex.from_documents([document])
sentence_index.storage_context.persist(persist_dir=save_dir)
with open(os.path.join(save_dir, "meta.json"), "w") as f:
json.dump(index_config, f, indent=2)
else:
sentence_index = load_index_from_storage(
StorageContext.from_defaults(persist_dir=save_dir)
)
return sentence_index
def build_automerging_index(
input_file: str,
save_dir: str,
index_config: dict[str, str | list[int]],
):
config_hash = get_config_hash(index_config)
save_dir = os.path.join(save_dir, "auto_merging", config_hash)
Settings.embed_model = HuggingFaceEmbedding(model_name=index_config["embed_model"])
if not os.path.exists(save_dir):
document = load_data(input_file)
node_parser = HierarchicalNodeParser.from_defaults(
chunk_sizes=index_config["chunk_sizes"]
)
nodes = node_parser.get_nodes_from_documents([document])
leaf_nodes = get_leaf_nodes(nodes)
Settings.node_parser = node_parser
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
automerging_index = VectorStoreIndex(
leaf_nodes,
storage_context=storage_context,
)
automerging_index.storage_context.persist(persist_dir=save_dir)
with open(os.path.join(save_dir, "meta.json"), "w") as f:
json.dump(index_config, f, indent=2)
else:
automerging_index = load_index_from_storage(
StorageContext.from_defaults(persist_dir=save_dir),
)
return automerging_index
def get_sentence_window_query_engine(
sentence_index,
similarity_top_k: int = 6,
rerank_top_n: int = 2,
rerank_model: str = "cross-encoder/ms-marco-MiniLM-L-2-v2",
):
# Used to replace the node content with a field from the node metadata.
postproc = MetadataReplacementPostProcessor(target_metadata_key="window")
# Rerank can speed up an LLM query without sacrificing accuracy. It does so by
# pruning away irrelevant nodes from the context.
rerank = SentenceTransformerRerank(top_n=rerank_top_n, model=rerank_model)
sentence_window_engine = sentence_index.as_query_engine(
similarity_top_k=similarity_top_k, node_postprocessors=[postproc, rerank]
)
return sentence_window_engine
def get_automerging_query_engine(
automerging_index,
similarity_top_k: int = 12,
rerank_top_n: int = 6,
rerank_model: str = "cross-encoder/ms-marco-MiniLM-L-2-v2",
):
base_retriever = automerging_index.as_retriever(similarity_top_k=similarity_top_k)
retriever = AutoMergingRetriever(
base_retriever, automerging_index.storage_context, verbose=True
)
rerank = SentenceTransformerRerank(top_n=rerank_top_n, model=rerank_model)
auto_merging_engine = RetrieverQueryEngine.from_args(
retriever, node_postprocessors=[rerank]
)
return auto_merging_engine
def sentence_window_retrieval_setup(
input_file: str,
save_dir: str,
llm_openai_model: SupportedOpenAIllms = "gpt-4o-mini",
temperature: float = 0.1,
embed_model: SupportedEmbedModels = "BAAI/bge-small-en-v1.5",
sentence_window_size: int = 3,
similarity_top_k: int = 6,
rerank_model: SupportedRerankModels = "cross-encoder/ms-marco-MiniLM-L-2-v2",
rerank_top_n: int = 2,
**kwargs
):
openai.api_key = get_openai_api_key()
# This allows to uniquely identify the index
config = {
"doc_source": os.path.basename(input_file),
"embed_model": embed_model,
"sentence_window_size": sentence_window_size,
}
# 1. Build index
index = build_sentence_window_index(input_file, save_dir, config)
Settings.llm = OpenAI(model=llm_openai_model, temperature=temperature)
# 2. Get engine
sentence_window_engine = get_sentence_window_query_engine(
index,
similarity_top_k=similarity_top_k,
rerank_model=rerank_model,
rerank_top_n=rerank_top_n,
)
return sentence_window_engine
def automerging_retrieval_setup(
input_file: str,
save_dir: str,
llm_openai_model: SupportedOpenAIllms = "gpt-4o-mini",
temperature: float = 0.1,
embed_model: SupportedEmbedModels = "BAAI/bge-small-en-v1.5",
chunk_sizes=[2048, 512, 128],
similarity_top_k: int = 6,
rerank_model: SupportedRerankModels = "cross-encoder/ms-marco-MiniLM-L-2-v2",
rerank_top_n: int = 2,
**kwargs
):
openai.api_key = get_openai_api_key()
# This allows to uniquely identify the index
config = {
"doc_source": os.path.basename(input_file),
"embed_model": embed_model,
"chunk_sizes": chunk_sizes,
}
# 1. Build index
index = build_automerging_index(input_file, save_dir, config)
Settings.llm = OpenAI(model=llm_openai_model, temperature=temperature)
# 2. Get engine
automerging_engine = get_sentence_window_query_engine(
index,
similarity_top_k=similarity_top_k,
rerank_model=rerank_model,
rerank_top_n=rerank_top_n,
)
return automerging_engine
def basic_rag_setup(
input_file: str,
save_dir: str,
llm_openai_model: SupportedOpenAIllms = "gpt-4o-mini",
temperature: float = 0.1,
embed_model: SupportedEmbedModels = "BAAI/bge-small-en-v1.5",
similarity_top_k: int = 6,
rerank_model: SupportedRerankModels = "cross-encoder/ms-marco-MiniLM-L-2-v2",
rerank_top_n: int = 2,
**kwargs
):
openai.api_key = get_openai_api_key()
Settings.embed_model = HuggingFaceEmbedding(model_name=embed_model)
save_dir = os.path.join(save_dir, "basic")
if not os.path.exists(save_dir):
document = load_data(input_file)
index = VectorStoreIndex.from_documents([document])
index.storage_context.persist(persist_dir=save_dir)
else:
index = load_index_from_storage(
StorageContext.from_defaults(persist_dir=save_dir)
)
rerank = SentenceTransformerRerank(top_n=rerank_top_n, model=rerank_model)
engine = index.as_query_engine(
llm=OpenAI(model=llm_openai_model, temperature=temperature),
similarity_top_k=similarity_top_k,
node_postprocessors=[rerank],
)
return engine
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