Grafik RAG dengan Milvus

Penerapan model bahasa besar yang meluas menyoroti pentingnya meningkatkan akurasi dan relevansi respons mereka. Retrieval-Augmented Generation (RAG) meningkatkan model dengan basis pengetahuan eksternal, memberikan informasi yang lebih kontekstual dan mengurangi masalah seperti halusinasi dan pengetahuan yang tidak memadai. Namun, hanya mengandalkan paradigma RAG yang sederhana memiliki keterbatasan, terutama ketika berhadapan dengan hubungan entitas yang kompleks dan pertanyaan multi-hop, di mana model sering kali kesulitan untuk memberikan jawaban yang akurat.

Memperkenalkan grafik pengetahuan (KG) ke dalam sistem RAG menawarkan solusi baru. KG menyajikan entitas dan hubungannya secara terstruktur, memberikan informasi pengambilan yang lebih tepat dan membantu RAG untuk menangani tugas-tugas menjawab pertanyaan yang kompleks dengan lebih baik. KG-RAG masih dalam tahap awal, dan belum ada konsensus mengenai cara mengambil entitas dan hubungan secara efektif dari KG atau cara mengintegrasikan pencarian kemiripan vektor dengan struktur graf.

Dalam buku catatan ini, kami memperkenalkan sebuah pendekatan yang sederhana namun kuat untuk meningkatkan performa dari skenario ini. Ini adalah paradigma RAG sederhana dengan pengambilan multi-arah dan kemudian melakukan pemeringkatan ulang, tetapi mengimplementasikan Graph RAG secara logis, dan mencapai kinerja yang canggih dalam menangani pertanyaan-pertanyaan multi-hop. Mari kita lihat bagaimana implementasinya.

Prasyarat

Sebelum menjalankan notebook ini, pastikan Anda telah menginstal ketergantungan berikut ini:

$ pip install --upgrade --quiet pymilvus numpy scipy langchain langchain-core langchain-openai tqdm

Jika Anda menggunakan Google Colab, untuk mengaktifkan dependensi yang baru saja diinstal, Anda mungkin perlu memulai ulang runtime (klik menu "Runtime" di bagian atas layar, dan pilih "Restart session" dari menu tarik-turun).

Kita akan menggunakan model dari OpenAI. Anda harus menyiapkan kunci api OPENAI_API_KEY sebagai variabel lingkungan.

import os

os.environ["OPENAI_API_KEY"] = "sk-***********"

Impor library dan dependensi yang diperlukan.

import numpy as np

from collections import defaultdict
from scipy.sparse import csr_matrix
from pymilvus import MilvusClient
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.prompts import ChatPromptTemplate, HumanMessagePromptTemplate
from langchain_core.output_parsers import StrOutputParser, JsonOutputParser
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from tqdm import tqdm

Inisialisasi instance klien Milvus, LLM, dan model penyematan.

milvus_client = MilvusClient(uri="./milvus.db")

llm = ChatOpenAI(
    model="gpt-4o",
    temperature=0,
)
embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")

Pemuatan Data Offline

Persiapan Data

Kami akan menggunakan dataset nano yang memperkenalkan hubungan antara keluarga Bernoulli dan Euler untuk mendemonstrasikan sebagai contoh. Dataset nano berisi 4 bagian dan satu set triplet yang sesuai, di mana setiap triplet berisi subjek, predikat, dan objek. Dalam praktiknya, Anda dapat menggunakan pendekatan apa pun untuk mengekstrak triplet dari korpus kustom Anda sendiri.

nano_dataset = [
    {
        "passage": "Jakob Bernoulli (1654–1705): Jakob was one of the earliest members of the Bernoulli family to gain prominence in mathematics. He made significant contributions to calculus, particularly in the development of the theory of probability. He is known for the Bernoulli numbers and the Bernoulli theorem, a precursor to the law of large numbers. He was the older brother of Johann Bernoulli, another influential mathematician, and the two had a complex relationship that involved both collaboration and rivalry.",
        "triplets": [
            ["Jakob Bernoulli", "made significant contributions to", "calculus"],
            [
                "Jakob Bernoulli",
                "made significant contributions to",
                "the theory of probability",
            ],
            ["Jakob Bernoulli", "is known for", "the Bernoulli numbers"],
            ["Jakob Bernoulli", "is known for", "the Bernoulli theorem"],
            ["The Bernoulli theorem", "is a precursor to", "the law of large numbers"],
            ["Jakob Bernoulli", "was the older brother of", "Johann Bernoulli"],
        ],
    },
    {
        "passage": "Johann Bernoulli (1667–1748): Johann, Jakob’s younger brother, was also a major figure in the development of calculus. He worked on infinitesimal calculus and was instrumental in spreading the ideas of Leibniz across Europe. Johann also contributed to the calculus of variations and was known for his work on the brachistochrone problem, which is the curve of fastest descent between two points.",
        "triplets": [
            [
                "Johann Bernoulli",
                "was a major figure of",
                "the development of calculus",
            ],
            ["Johann Bernoulli", "was", "Jakob's younger brother"],
            ["Johann Bernoulli", "worked on", "infinitesimal calculus"],
            ["Johann Bernoulli", "was instrumental in spreading", "Leibniz's ideas"],
            ["Johann Bernoulli", "contributed to", "the calculus of variations"],
            ["Johann Bernoulli", "was known for", "the brachistochrone problem"],
        ],
    },
    {
        "passage": "Daniel Bernoulli (1700–1782): The son of Johann Bernoulli, Daniel made major contributions to fluid dynamics, probability, and statistics. He is most famous for Bernoulli’s principle, which describes the behavior of fluid flow and is fundamental to the understanding of aerodynamics.",
        "triplets": [
            ["Daniel Bernoulli", "was the son of", "Johann Bernoulli"],
            ["Daniel Bernoulli", "made major contributions to", "fluid dynamics"],
            ["Daniel Bernoulli", "made major contributions to", "probability"],
            ["Daniel Bernoulli", "made major contributions to", "statistics"],
            ["Daniel Bernoulli", "is most famous for", "Bernoulli’s principle"],
            [
                "Bernoulli’s principle",
                "is fundamental to",
                "the understanding of aerodynamics",
            ],
        ],
    },
    {
        "passage": "Leonhard Euler (1707–1783) was one of the greatest mathematicians of all time, and his relationship with the Bernoulli family was significant. Euler was born in Basel and was a student of Johann Bernoulli, who recognized his exceptional talent and mentored him in mathematics. Johann Bernoulli’s influence on Euler was profound, and Euler later expanded upon many of the ideas and methods he learned from the Bernoullis.",
        "triplets": [
            [
                "Leonhard Euler",
                "had a significant relationship with",
                "the Bernoulli family",
            ],
            ["leonhard Euler", "was born in", "Basel"],
            ["Leonhard Euler", "was a student of", "Johann Bernoulli"],
            ["Johann Bernoulli's influence", "was profound on", "Euler"],
        ],
    },
]

Kami membangun entitas dan relasi sebagai berikut:

• Entitas adalah subjek atau objek dalam kembar tiga, jadi kita langsung mengekstraknya dari kembar tiga.
• Di sini kita membangun konsep relasi dengan menggabungkan subjek, predikat, dan objek secara langsung dengan spasi di antaranya.

Kita juga menyiapkan sebuah dict untuk memetakan id entitas ke id relasi, dan sebuah dict lain untuk memetakan id relasi ke id bagian untuk digunakan di kemudian hari.

entityid_2_relationids = defaultdict(list)
relationid_2_passageids = defaultdict(list)

entities = []
relations = []
passages = []
for passage_id, dataset_info in enumerate(nano_dataset):
    passage, triplets = dataset_info["passage"], dataset_info["triplets"]
    passages.append(passage)
    for triplet in triplets:
        if triplet[0] not in entities:
            entities.append(triplet[0])
        if triplet[2] not in entities:
            entities.append(triplet[2])
        relation = " ".join(triplet)
        if relation not in relations:
            relations.append(relation)
            entityid_2_relationids[entities.index(triplet[0])].append(
                len(relations) - 1
            )
            entityid_2_relationids[entities.index(triplet[2])].append(
                len(relations) - 1
            )
        relationid_2_passageids[relations.index(relation)].append(passage_id)

Penyisipan Data

Buatlah koleksi Milvus untuk entitas, relasi, dan bagian. Koleksi entitas dan koleksi relasi digunakan sebagai koleksi utama untuk konstruksi graf dalam metode kami, sedangkan koleksi bagian digunakan sebagai perbandingan pengambilan RAG naif atau tujuan tambahan.

embedding_dim = len(embedding_model.embed_query("foo"))


def create_milvus_collection(collection_name: str):
    if milvus_client.has_collection(collection_name=collection_name):
        milvus_client.drop_collection(collection_name=collection_name)
    milvus_client.create_collection(
        collection_name=collection_name,
        dimension=embedding_dim,
        consistency_level="Strong",
    )


entity_col_name = "entity_collection"
relation_col_name = "relation_collection"
passage_col_name = "passage_collection"
create_milvus_collection(entity_col_name)
create_milvus_collection(relation_col_name)
create_milvus_collection(passage_col_name)

Masukkan data dengan informasi metadatanya ke dalam koleksi Milvus, termasuk koleksi entitas, relasi, dan bagian. Informasi metadata termasuk id bagian dan entitas kedekatan atau id relasi.

def milvus_insert(
    collection_name: str,
    text_list: list[str],
):
    batch_size = 512
    for row_id in tqdm(range(0, len(text_list), batch_size), desc="Inserting"):
        batch_texts = text_list[row_id : row_id + batch_size]
        batch_embeddings = embedding_model.embed_documents(batch_texts)

        batch_ids = [row_id + j for j in range(len(batch_texts))]
        batch_data = [
            {
                "id": id_,
                "text": text,
                "vector": vector,
            }
            for id_, text, vector in zip(batch_ids, batch_texts, batch_embeddings)
        ]
        milvus_client.insert(
            collection_name=collection_name,
            data=batch_data,
        )


milvus_insert(
    collection_name=relation_col_name,
    text_list=relations,
)

milvus_insert(
    collection_name=entity_col_name,
    text_list=entities,
)

milvus_insert(
    collection_name=passage_col_name,
    text_list=passages,
)

Inserting: 100%|███████████████████████████████████| 1/1 [00:00<00:00,  1.02it/s]
Inserting: 100%|███████████████████████████████████| 1/1 [00:00<00:00,  1.39it/s]
Inserting: 100%|███████████████████████████████████| 1/1 [00:00<00:00,  2.28it/s]

Kueri Online

Pengambilan Kemiripan

Kami mengambil entitas dan relasi yang paling mirip berdasarkan kueri masukan dari Milvus.

Ketika melakukan pengambilan entitas, pertama-tama kita harus mengekstrak entitas kueri dari teks kueri menggunakan beberapa metode tertentu seperti NER (Named-entity recognition). Untuk mempermudah, kami menyiapkan hasil NER di sini. Jika Anda ingin mengubah kueri sebagai pertanyaan khusus Anda, Anda harus mengubah daftar NER kueri yang sesuai. Dalam praktiknya, Anda dapat menggunakan model atau pendekatan lain untuk mengekstrak entitas dari kueri.

query = "What contribution did the son of Euler's teacher make?"

query_ner_list = ["Euler"]
# query_ner_list = ner(query) # In practice, replace it with your custom NER approach

query_ner_embeddings = [
    embedding_model.embed_query(query_ner) for query_ner in query_ner_list
]

top_k = 3

entity_search_res = milvus_client.search(
    collection_name=entity_col_name,
    data=query_ner_embeddings,
    limit=top_k,
    output_fields=["id"],
)

query_embedding = embedding_model.embed_query(query)

relation_search_res = milvus_client.search(
    collection_name=relation_col_name,
    data=[query_embedding],
    limit=top_k,
    output_fields=["id"],
)[0]

Memperluas Subgraf

Kita menggunakan entitas dan relasi yang diambil untuk memperluas subgraf dan mendapatkan relasi kandidat, lalu menggabungkannya dari dua cara. Berikut adalah diagram alir dari proses perluasan subgraf:

Di sini kita membuat sebuah matriks kedekatan dan menggunakan perkalian matriks untuk menghitung informasi pemetaan kedekatan dalam beberapa derajat. Dengan cara ini, kita dapat dengan cepat mendapatkan informasi dari setiap derajat perluasan.

# Construct the adjacency matrix of entities and relations where the value of the adjacency matrix is 1 if an entity is related to a relation, otherwise 0.
entity_relation_adj = np.zeros((len(entities), len(relations)))
for entity_id, entity in enumerate(entities):
    entity_relation_adj[entity_id, entityid_2_relationids[entity_id]] = 1

# Convert the adjacency matrix to a sparse matrix for efficient computation.
entity_relation_adj = csr_matrix(entity_relation_adj)

# Use the entity-relation adjacency matrix to construct 1 degree entity-entity and relation-relation adjacency matrices.
entity_adj_1_degree = entity_relation_adj @ entity_relation_adj.T
relation_adj_1_degree = entity_relation_adj.T @ entity_relation_adj

# Specify the target degree of the subgraph to be expanded.
# 1 or 2 is enough for most cases.
target_degree = 1

# Compute the target degree adjacency matrices using matrix multiplication.
entity_adj_target_degree = entity_adj_1_degree
for _ in range(target_degree - 1):
    entity_adj_target_degree = entity_adj_target_degree * entity_adj_1_degree
relation_adj_target_degree = relation_adj_1_degree
for _ in range(target_degree - 1):
    relation_adj_target_degree = relation_adj_target_degree * relation_adj_1_degree

entity_relation_adj_target_degree = entity_adj_target_degree @ entity_relation_adj

Dengan mengambil nilai dari matriks perluasan derajat target, kita dapat dengan mudah memperluas derajat yang sesuai dari entitas dan relasi yang diambil untuk mendapatkan semua relasi dari subgraf.

expanded_relations_from_relation = set()
expanded_relations_from_entity = set()
# You can set the similarity threshold here to guarantee the quality of the retrieved ones.
# entity_sim_filter_thresh = ...
# relation_sim_filter_thresh = ...

filtered_hit_relation_ids = [
    relation_res["entity"]["id"]
    for relation_res in relation_search_res
    # if relation_res['distance'] > relation_sim_filter_thresh
]
for hit_relation_id in filtered_hit_relation_ids:
    expanded_relations_from_relation.update(
        relation_adj_target_degree[hit_relation_id].nonzero()[1].tolist()
    )

filtered_hit_entity_ids = [
    one_entity_res["entity"]["id"]
    for one_entity_search_res in entity_search_res
    for one_entity_res in one_entity_search_res
    # if one_entity_res['distance'] > entity_sim_filter_thresh
]

for filtered_hit_entity_id in filtered_hit_entity_ids:
    expanded_relations_from_entity.update(
        entity_relation_adj_target_degree[filtered_hit_entity_id].nonzero()[1].tolist()
    )

# Merge the expanded relations from the relation and entity retrieval ways.
relation_candidate_ids = list(
    expanded_relations_from_relation | expanded_relations_from_entity
)

relation_candidate_texts = [
    relations[relation_id] for relation_id in relation_candidate_ids
]

Kita telah mendapatkan relasi-relasi kandidat dengan memperluas subgraf, yang akan diperingkat ulang oleh LLM pada langkah berikutnya.

Pemeringkatan ulang LLM

Pada tahap ini, kami menggunakan mekanisme self-attention yang kuat dari LLM untuk menyaring dan menyempurnakan lebih lanjut himpunan kandidat relasi. Kami menggunakan perintah sekali tembak, memasukkan kueri dan kumpulan kandidat hubungan ke dalam perintah, dan menginstruksikan LLM untuk memilih hubungan potensial yang dapat membantu menjawab kueri. Mengingat bahwa beberapa pertanyaan mungkin rumit, kami mengadopsi pendekatan Chain-of-Thought, yang memungkinkan LLM untuk mengartikulasikan proses berpikirnya dalam menjawab pertanyaan tersebut. Kami menetapkan bahwa respons LLM dalam format json untuk memudahkan penguraian.

query_prompt_one_shot_input = """I will provide you with a list of relationship descriptions. Your task is to select 3 relationships that may be useful to answer the given question. Please return a JSON object containing your thought process and a list of the selected relationships in order of their relevance.

Question:
When was the mother of the leader of the Third Crusade born?

Relationship descriptions:
[1] Eleanor was born in 1122.
[2] Eleanor married King Louis VII of France.
[3] Eleanor was the Duchess of Aquitaine.
[4] Eleanor participated in the Second Crusade.
[5] Eleanor had eight children.
[6] Eleanor was married to Henry II of England.
[7] Eleanor was the mother of Richard the Lionheart.
[8] Richard the Lionheart was the King of England.
[9] Henry II was the father of Richard the Lionheart.
[10] Henry II was the King of England.
[11] Richard the Lionheart led the Third Crusade.

"""
query_prompt_one_shot_output = """{"thought_process": "To answer the question about the birth of the mother of the leader of the Third Crusade, I first need to identify who led the Third Crusade and then determine who his mother was. After identifying his mother, I can look for the relationship that mentions her birth.", "useful_relationships": ["[11] Richard the Lionheart led the Third Crusade", "[7] Eleanor was the mother of Richard the Lionheart", "[1] Eleanor was born in 1122"]}"""

query_prompt_template = """Question:
{question}

Relationship descriptions:
{relation_des_str}

"""


def rerank_relations(
    query: str, relation_candidate_texts: list[str], relation_candidate_ids: list[str]
) -> list[int]:
    relation_des_str = "\n".join(
        map(
            lambda item: f"[{item[0]}] {item[1]}",
            zip(relation_candidate_ids, relation_candidate_texts),
        )
    ).strip()
    rerank_prompts = ChatPromptTemplate.from_messages(
        [
            HumanMessage(query_prompt_one_shot_input),
            AIMessage(query_prompt_one_shot_output),
            HumanMessagePromptTemplate.from_template(query_prompt_template),
        ]
    )
    rerank_chain = (
        rerank_prompts
        | llm.bind(response_format={"type": "json_object"})
        | JsonOutputParser()
    )
    rerank_res = rerank_chain.invoke(
        {"question": query, "relation_des_str": relation_des_str}
    )
    rerank_relation_ids = []
    rerank_relation_lines = rerank_res["useful_relationships"]
    id_2_lines = {}
    for line in rerank_relation_lines:
        id_ = int(line[line.find("[") + 1 : line.find("]")])
        id_2_lines[id_] = line.strip()
        rerank_relation_ids.append(id_)
    return rerank_relation_ids


rerank_relation_ids = rerank_relations(
    query,
    relation_candidate_texts=relation_candidate_texts,
    relation_candidate_ids=relation_candidate_ids,
)

Dapatkan Hasil Akhir

Kita bisa mendapatkan hasil akhir yang diambil dari relasi yang diurutkan ulang.

final_top_k = 2

final_passages = []
final_passage_ids = []
for relation_id in rerank_relation_ids:
    for passage_id in relationid_2_passageids[relation_id]:
        if passage_id not in final_passage_ids:
            final_passage_ids.append(passage_id)
            final_passages.append(passages[passage_id])
passages_from_our_method = final_passages[:final_top_k]

Kita dapat membandingkan hasilnya dengan metode RAG naif, yang mengambil bagian topK berdasarkan penyematan kueri secara langsung dari koleksi bagian.

naive_passage_res = milvus_client.search(
    collection_name=passage_col_name,
    data=[query_embedding],
    limit=final_top_k,
    output_fields=["text"],
)[0]
passages_from_naive_rag = [res["entity"]["text"] for res in naive_passage_res]

print(
    f"Passages retrieved from naive RAG: \n{passages_from_naive_rag}\n\n"
    f"Passages retrieved from our method: \n{passages_from_our_method}\n\n"
)


prompt = ChatPromptTemplate.from_messages(
    [
        (
            "human",
            """Use the following pieces of retrieved context to answer the question. If there is not enough information in the retrieved context to answer the question, just say that you don't know.
Question: {question}
Context: {context}
Answer:""",
        )
    ]
)

rag_chain = prompt | llm | StrOutputParser()

answer_from_naive_rag = rag_chain.invoke(
    {"question": query, "context": "\n".join(passages_from_naive_rag)}
)
answer_from_our_method = rag_chain.invoke(
    {"question": query, "context": "\n".join(passages_from_our_method)}
)

print(
    f"Answer from naive RAG: {answer_from_naive_rag}\n\nAnswer from our method: {answer_from_our_method}"
)

Passages retrieved from naive RAG: 
['Leonhard Euler (1707–1783) was one of the greatest mathematicians of all time, and his relationship with the Bernoulli family was significant. Euler was born in Basel and was a student of Johann Bernoulli, who recognized his exceptional talent and mentored him in mathematics. Johann Bernoulli’s influence on Euler was profound, and Euler later expanded upon many of the ideas and methods he learned from the Bernoullis.', 'Johann Bernoulli (1667–1748): Johann, Jakob’s younger brother, was also a major figure in the development of calculus. He worked on infinitesimal calculus and was instrumental in spreading the ideas of Leibniz across Europe. Johann also contributed to the calculus of variations and was known for his work on the brachistochrone problem, which is the curve of fastest descent between two points.']

Passages retrieved from our method: 
['Leonhard Euler (1707–1783) was one of the greatest mathematicians of all time, and his relationship with the Bernoulli family was significant. Euler was born in Basel and was a student of Johann Bernoulli, who recognized his exceptional talent and mentored him in mathematics. Johann Bernoulli’s influence on Euler was profound, and Euler later expanded upon many of the ideas and methods he learned from the Bernoullis.', 'Daniel Bernoulli (1700–1782): The son of Johann Bernoulli, Daniel made major contributions to fluid dynamics, probability, and statistics. He is most famous for Bernoulli’s principle, which describes the behavior of fluid flow and is fundamental to the understanding of aerodynamics.']


Answer from naive RAG: I don't know. The retrieved context does not provide information about the contributions made by the son of Euler's teacher.

Answer from our method: The son of Euler's teacher, Daniel Bernoulli, made major contributions to fluid dynamics, probability, and statistics. He is most famous for Bernoulli’s principle, which describes the behavior of fluid flow and is fundamental to the understanding of aerodynamics.

Seperti yang dapat kita lihat, bagian yang diambil dari RAG naif melewatkan bagian yang benar, yang menyebabkan jawaban yang salah. Bagian yang diambil dari metode kami adalah benar, dan ini membantu untuk mendapatkan jawaban yang akurat untuk pertanyaan tersebut.