Yellowbrick

Yellowbrick 是一个弹性、大规模并行处理 (MPP) SQL 数据库，它在云端和本地部署，使用 Kubernetes 实现规模化、弹性和云可移植性。Yellowbrick 旨在解决最大型、最复杂的业务关键型数据仓库用例。Yellowbrick 提供的大规模效率也使其能够作为高性能、可扩展的向量数据库，用于存储和使用 SQL 搜索向量。

将 Yellowbrick 用作 ChatGpt 的向量存储

本教程演示了如何创建一个由 ChatGpt 提供支持的简单聊天机器人，该机器人使用 Yellowbrick 作为向量存储以支持检索增强生成 (RAG)。您需要准备：

1. 一个 Yellowbrick 沙盒帐户
2. 一个来自 OpenAI 的 API 密钥

本教程分为五个部分。首先，我们将使用 langchain 创建一个不带向量存储的基线聊天机器人，以便与 ChatGpt 交互。其次，我们将在 Yellowbrick 中创建一个嵌入表，它将作为向量存储。第三，我们将加载一系列文档（Yellowbrick 手册的管理章节）。第四，我们将创建这些文档的向量表示并存储在 Yellowbrick 表中。最后，我们将向改进后的聊天框发送相同的查询以查看结果。

# Install all needed libraries
%pip install --upgrade --quiet  langchain
%pip install --upgrade --quiet  langchain-openai langchain-community
%pip install --upgrade --quiet  psycopg2-binary
%pip install --upgrade --quiet  tiktoken

设置：输入用于连接 Yellowbrick 和 OpenAI API 的信息

我们的聊天机器人通过 langchain 库与 ChatGpt 集成，因此您首先需要一个来自 OpenAI 的 API 密钥

获取 OpenAI API 密钥的步骤：

1. 在 https://platform.openai.com/ 注册
2. 添加支付方式 - 您不太可能超出免费额度
3. 创建 API 密钥

当您注册 Yellowbrick 沙盒帐户时，您还需要欢迎邮件中的用户名、密码和数据库名称。

以下内容应修改为包含您的 Yellowbrick 数据库和 OpenAPI 密钥信息

# Modify these values to match your Yellowbrick Sandbox and OpenAI API Key
YBUSER = "[SANDBOX USER]"
YBPASSWORD = "[SANDBOX PASSWORD]"
YBDATABASE = "[SANDBOX_DATABASE]"
YBHOST = "trialsandbox.sandbox.aws.yellowbrickcloud.com"

OPENAI_API_KEY = "[OPENAI API KEY]"

# Import libraries and setup keys / login info
import os
import pathlib
import re
import sys
import urllib.parse as urlparse
from getpass import getpass

import psycopg2
from IPython.display import Markdown, display
from langchain.chains import LLMChain, RetrievalQAWithSourcesChain
from langchain_community.vectorstores import Yellowbrick
from langchain_core.documents import Document
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter

# Establish connection parameters to Yellowbrick.  If you've signed up for Sandbox, fill in the information from your welcome mail here:
yellowbrick_connection_string = (
    f"postgres://{urlparse.quote(YBUSER)}:{YBPASSWORD}@{YBHOST}:5432/{YBDATABASE}"
)

YB_DOC_DATABASE = "sample_data"
YB_DOC_TABLE = "yellowbrick_documentation"
embedding_table = "my_embeddings"

# API Key for OpenAI.  Signup at https://platform.openai.com
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY

from langchain_core.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)

API参考：LLMChain | RetrievalQAWithSourcesChain | Yellowbrick | Document | ChatOpenAI | OpenAIEmbeddings | RecursiveCharacterTextSplitter | ChatPromptTemplate | HumanMessagePromptTemplate | SystemMessagePromptTemplate

第一部分：创建一个不带向量存储的 ChatGpt 基线聊天机器人

我们将使用 langchain 查询 ChatGPT。由于没有向量存储，ChatGPT 将没有上下文来回答问题。

# Set up the chat model and specific prompt
system_template = """If you don't know the answer, Make up your best guess."""
messages = [
    SystemMessagePromptTemplate.from_template(system_template),
    HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = ChatPromptTemplate.from_messages(messages)

chain_type_kwargs = {"prompt": prompt}
llm = ChatOpenAI(
    model_name="gpt-3.5-turbo",  # Modify model_name if you have access to GPT-4
    temperature=0,
    max_tokens=256,
)

chain = LLMChain(
    llm=llm,
    prompt=prompt,
    verbose=False,
)


def print_result_simple(query):
    result = chain(query)
    output_text = f"""### Question:
  {query}
  ### Answer: 
  {result["text"]}
    """
    display(Markdown(output_text))


# Use the chain to query
print_result_simple("How many databases can be in a Yellowbrick Instance?")

print_result_simple("What's an easy way to add users in bulk to Yellowbrick?")

第二部分：连接 Yellowbrick 并创建嵌入表

要将文档嵌入加载到 Yellowbrick 中，您应该创建自己的表来存储它们。请注意，表所在的 Yellowbrick 数据库必须是 UTF-8 编码的。

在 UTF-8 数据库中创建具有以下模式的表，并提供您选择的表名

# Establish a connection to the Yellowbrick database
try:
    conn = psycopg2.connect(yellowbrick_connection_string)
except psycopg2.Error as e:
    print(f"Error connecting to the database: {e}")
    exit(1)

# Create a cursor object using the connection
cursor = conn.cursor()

# Define the SQL statement to create a table
create_table_query = f"""
CREATE TABLE IF NOT EXISTS {embedding_table} (
    doc_id uuid NOT NULL,
    embedding_id smallint NOT NULL,
    embedding double precision NOT NULL
)
DISTRIBUTE ON (doc_id);
truncate table {embedding_table};
"""

# Execute the SQL query to create a table
try:
    cursor.execute(create_table_query)
    print(f"Table '{embedding_table}' created successfully!")
except psycopg2.Error as e:
    print(f"Error creating table: {e}")
    conn.rollback()

# Commit changes and close the cursor and connection
conn.commit()
cursor.close()
conn.close()

第三部分：从 Yellowbrick 中现有表提取要索引的文档

从现有的 Yellowbrick 表中提取文档路径和内容。我们将在下一步中使用这些文档来创建嵌入。

yellowbrick_doc_connection_string = (
    f"postgres://{urlparse.quote(YBUSER)}:{YBPASSWORD}@{YBHOST}:5432/{YB_DOC_DATABASE}"
)

print(yellowbrick_doc_connection_string)

# Establish a connection to the Yellowbrick database
conn = psycopg2.connect(yellowbrick_doc_connection_string)

# Create a cursor object
cursor = conn.cursor()

# Query to select all documents from the table
query = f"SELECT path, document FROM {YB_DOC_TABLE}"

# Execute the query
cursor.execute(query)

# Fetch all documents
yellowbrick_documents = cursor.fetchall()

print(f"Extracted {len(yellowbrick_documents)} documents successfully!")

# Close the cursor and connection
cursor.close()
conn.close()

第四部分：使用文档加载 Yellowbrick 向量存储

浏览文档，将其分成可消化的块，创建嵌入并插入到 Yellowbrick 表中。这大约需要 5 分钟。

# Split documents into chunks for conversion to embeddings
DOCUMENT_BASE_URL = "https://docs.yellowbrick.com/6.7.1/"  # Actual URL


separator = "\n## "  # This separator assumes Markdown docs from the repo uses ### as logical main header most of the time
chunk_size_limit = 2000
max_chunk_overlap = 200

documents = [
    Document(
        page_content=document[1],
        metadata={"source": DOCUMENT_BASE_URL + document[0].replace(".md", ".html")},
    )
    for document in yellowbrick_documents
]

text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=chunk_size_limit,
    chunk_overlap=max_chunk_overlap,
    separators=[separator, "\nn", "\n", ",", " ", ""],
)
split_docs = text_splitter.split_documents(documents)

docs_text = [doc.page_content for doc in split_docs]

embeddings = OpenAIEmbeddings()
vector_store = Yellowbrick.from_documents(
    documents=split_docs,
    embedding=embeddings,
    connection_string=yellowbrick_connection_string,
    table=embedding_table,
)

print(f"Created vector store with {len(documents)} documents")

第五部分：创建一个使用 Yellowbrick 作为向量存储的聊天机器人

接下来，我们将 Yellowbrick 添加为向量存储。该向量存储已填充了代表 Yellowbrick 产品文档管理章节的嵌入。

我们将发送与上述相同的查询，以查看改进后的响应。

system_template = """Use the following pieces of context to answer the users question.
Take note of the sources and include them in the answer in the format: "SOURCES: source1 source2", use "SOURCES" in capital letters regardless of the number of sources.
If you don't know the answer, just say that "I don't know", don't try to make up an answer.
----------------
{summaries}"""
messages = [
    SystemMessagePromptTemplate.from_template(system_template),
    HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = ChatPromptTemplate.from_messages(messages)

vector_store = Yellowbrick(
    OpenAIEmbeddings(),
    yellowbrick_connection_string,
    embedding_table,  # Change the table name to reflect your embeddings
)

chain_type_kwargs = {"prompt": prompt}
llm = ChatOpenAI(
    model_name="gpt-3.5-turbo",  # Modify model_name if you have access to GPT-4
    temperature=0,
    max_tokens=256,
)
chain = RetrievalQAWithSourcesChain.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vector_store.as_retriever(search_kwargs={"k": 5}),
    return_source_documents=True,
    chain_type_kwargs=chain_type_kwargs,
)


def print_result_sources(query):
    result = chain(query)
    output_text = f"""### Question: 
  {query}
  ### Answer: 
  {result["answer"]}
  ### Sources: 
  {result["sources"]}
  ### All relevant sources:
  {", ".join(list(set([doc.metadata["source"] for doc in result["source_documents"]])))}
    """
    display(Markdown(output_text))


# Use the chain to query

print_result_sources("How many databases can be in a Yellowbrick Instance?")

print_result_sources("Whats an easy way to add users in bulk to Yellowbrick?")

第六部分：引入索引以提高性能

Yellowbrick 还支持使用局部敏感哈希 (Locality-Sensitive Hashing) 方法进行索引。这是一种近似最近邻搜索技术，允许以牺牲准确性为代价来换取相似度搜索时间。该索引引入了两个新的可调参数

• 超平面的数量，作为 create_lsh_index(num_hyperplanes) 的参数提供。文档越多，需要的超平面就越多。LSH 是一种降维形式。原始嵌入被转换为低维向量，其中分量数量与超平面数量相同。
• 汉明距离，一个表示搜索广度的整数。较小的汉明距离会导致更快的检索速度但较低的准确性。

以下是如何在加载到 Yellowbrick 的嵌入上创建索引。我们还将重新运行之前的聊天会话，但这次检索将使用索引。请注意，对于如此少量的文档，您不会在性能方面看到索引的优势。

system_template = """Use the following pieces of context to answer the users question.
Take note of the sources and include them in the answer in the format: "SOURCES: source1 source2", use "SOURCES" in capital letters regardless of the number of sources.
If you don't know the answer, just say that "I don't know", don't try to make up an answer.
----------------
{summaries}"""
messages = [
    SystemMessagePromptTemplate.from_template(system_template),
    HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = ChatPromptTemplate.from_messages(messages)

vector_store = Yellowbrick(
    OpenAIEmbeddings(),
    yellowbrick_connection_string,
    embedding_table,  # Change the table name to reflect your embeddings
)

lsh_params = Yellowbrick.IndexParams(
    Yellowbrick.IndexType.LSH, {"num_hyperplanes": 8, "hamming_distance": 2}
)
vector_store.create_index(lsh_params)

chain_type_kwargs = {"prompt": prompt}
llm = ChatOpenAI(
    model_name="gpt-3.5-turbo",  # Modify model_name if you have access to GPT-4
    temperature=0,
    max_tokens=256,
)
chain = RetrievalQAWithSourcesChain.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vector_store.as_retriever(
        k=5, search_kwargs={"index_params": lsh_params}
    ),
    return_source_documents=True,
    chain_type_kwargs=chain_type_kwargs,
)


def print_result_sources(query):
    result = chain(query)
    output_text = f"""### Question: 
  {query}
  ### Answer: 
  {result["answer"]}
  ### Sources: 
  {result["sources"]}
  ### All relevant sources:
  {", ".join(list(set([doc.metadata["source"] for doc in result["source_documents"]])))}
    """
    display(Markdown(output_text))


# Use the chain to query

print_result_sources("How many databases can be in a Yellowbrick Instance?")

print_result_sources("Whats an easy way to add users in bulk to Yellowbrick?")

后续步骤：

此代码可以修改以提出不同的问题。您还可以将自己的文档加载到向量存储中。langchain 模块非常灵活，可以解析各种文件（包括 HTML、PDF 等）。

您还可以修改此设置，以使用 Huggingface 嵌入模型和 Meta 的 Llama 2 LLM，从而获得完全私密的聊天框体验。

相关

• 向量存储 概念指南
• 向量存储 操作指南