LangChain（4）检索增强 Retrieval Augmentation-Toy模板网

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Large Language Models (LLMs) 的能力或者知识来自两方面：模型在训练时候的输入；模型训练好后以提示词方式输入到模型中的知识source knowledge。检索增强就是指后期输入到模型中的附加信息。

文本分段

按顺序安装包：

!pip install -qU \
    datasets==2.12.0 \
    apache_beam \
    mwparserfromhell
 
 !pip install -qU \
  langchain==0.0.162 \
  openai==0.27.7 \
  tiktoken==0.4.0 \
  "pinecone-client[grpc]"==2.2.2

from datasets import load_dataset
# 下载维基百科资料
data = load_dataset("wikipedia", "20220301.simple", split='train[:10000]')

# 分词工具
import tiktoken
tiktoken.encoding_for_model('gpt-3.5-turbo')

import tiktoken
tokenizer = tiktoken.get_encoding('cl100k_base')
# 计算分词后的token数 create the length function
def tiktoken_len(text):
    tokens = tokenizer.encode(
        text,
        disallowed_special=()
    )
    return len(tokens)

# 使用 RecursiveCharacterTextSplitter 将整段文本分割，限定每个片段的最大token数
from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=400,
    chunk_overlap=20,
    length_function=tiktoken_len, #计量token数
    separators=["\n\n", "\n", " ", ""]
)

# 使用方式
chunks = text_splitter.split_text(data[6]['text'])[:3]
# 计算token数
tiktoken_len(chunks[0])

构建 Embedding

import os
# 设置OPENAI_API_KEY  get openai api key from platform.openai.com
OPENAI_API_KEY = os.getenv('OPENAI_API_KEY') or 'OPENAI_API_KEY'

from langchain.embeddings.openai import OpenAIEmbeddings
# 向量化的模型
model_name = 'text-embedding-ada-002'

embed = OpenAIEmbeddings(
    model=model_name,
    openai_api_key=OPENAI_API_KEY
)

# 测试文本
texts = [
    'this is the first chunk of text',
    'then another second chunk of text is here']

res = embed.embed_documents(texts)
print(len(res), len(res[0]))
>>>2 1536 # 向量长度为 1536

存储向量

使用 Pinecone 存储向量。

index_name = 'langchain-retrieval-augmentation'

import pinecone

# find API key in console at app.pinecone.io
PINECONE_API_KEY = os.getenv('PINECONE_API_KEY') or 'PINECONE_API_KEY'
# find ENV (cloud region) next to API key in console
PINECONE_ENVIRONMENT = os.getenv('PINECONE_ENVIRONMENT') or 'PINECONE_ENVIRONMENT'

pinecone.init(
    api_key=YOUR_API_KEY,
    environment=YOUR_ENV
)

if index_name not in pinecone.list_indexes():
    # we create a new index
    pinecone.create_index(
        name=index_name,
        metric='cosine',
        dimension=len(res[0])  # 1536 dim of text-embedding-ada-002
        )

# 连接库索引
index = pinecone.GRPCIndex(index_name)
print(index.describe_index_stats()) # 库索引统计信息

>>>{'dimension': 1536,
 'index_fullness': 0.1,
 'namespaces': {'': {'vector_count': 27437}},
 'total_vector_count': 27437}

按批将数据插入索引库中

from tqdm.auto import tqdm
from uuid import uuid4
# 批量大小
batch_limit = 100

texts = []
metadatas = []

for i, record in enumerate(tqdm(data)):
    # 维基百科中文本原始信息 first get metadata fields for this record
    metadata = {
        'wiki-id': str(record['id']),
        'source': record['url'],
        'title': record['title']
    }
    # 文本分段 now we create chunks from the record text
    record_texts = text_splitter.split_text(record['text'])
    # 为每一个分段文本创建元信息：j第几个片段 text片段文本 其它几个维基百科字段：wiki-id、source、title  create individual metadata dicts for each chunk
    record_metadatas = [{"chunk": j, "text": text, **metadata} for j, text in enumerate(record_texts)]
    # append these to current batches
    texts.extend(record_texts)
    metadatas.extend(record_metadatas)
    # if we have reached the batch_limit we can add texts
    if len(texts) >= batch_limit:
        ids = [str(uuid4()) for _ in range(len(texts))]
        embeds = embed.embed_documents(texts)
        index.upsert(vectors=zip(ids, embeds, metadatas))
        texts = []
        metadatas = []

if len(texts) > 0:
    ids = [str(uuid4()) for _ in range(len(texts))]
    embeds = embed.embed_documents(texts)
    index.upsert(vectors=zip(ids, embeds, metadatas))

向量查询

from langchain.vectorstores import Pinecone

text_field = "text" # 需要查询出来的字段
# 向量化的模型
model_name = 'text-embedding-ada-002'
embed = OpenAIEmbeddings(
    model=model_name,
    openai_api_key=OPENAI_API_KEY
)

# switch back to normal index for langchain
index = pinecone.Index(index_name)

vectorstore = Pinecone(
    index, embed.embed_query, text_field
)

# 查询信息
query = "who was Benito Mussolini?"
vectorstore.similarity_search(
    query,  # our search query
    k=3  # return 3 most relevant docs
)

检索信息结合LLM

from langchain.chains import RetrievalQA

# completion llm
llm = ChatOpenAI(
    openai_api_key=OPENAI_API_KEY,
    model_name='gpt-3.5-turbo',
    temperature=0.0
)

qa = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

print(qa.run(query))

>>>'Benito Mussolini was an Italian politician and journalist who served as the Prime Minister of Italy from 1922 until 1943.'

有时 LLM 回答不着边，没有完全按照提供的信息回答，可以通过 RetrievalQAWithSourcesChain 使得回答更可信，模型会返回参考的来源信息

from langchain.chains import RetrievalQAWithSourcesChain

qa_with_sources = RetrievalQAWithSourcesChain.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)
print(qa_with_sources(query))

>>>{'question': 'who was Benito Mussolini?',
 'answer': 'Benito Mussolini was an Italian politician and journalist who was the Prime Minister of Italy from 1922 until 1943.', 
 'sources': 'https://simple.wikipedia.org/wiki/Benito%20Mussolini, https://simple.wikipedia.org/wiki/Fascism'}