本 notebook 展示如何使用 Postgres 向量存储 TimescaleVector 来存储和查询向量嵌入。

什么是 Timescale Vector？

Timescale Vector 是面向 AI 应用的 PostgreSQL++。

Timescale Vector 让你能够在 PostgreSQL 中高效地存储和查询数百万个向量嵌入。

• 通过受 DiskANN 启发的索引算法，增强 pgvector，使其能够在数百万向量上实现更快、更准确的相似度搜索。
• 通过自动基于时间的分区和索引，实现快速的时间维度向量搜索。
• 提供熟悉的 SQL 接口，用于查询向量嵌入和关系数据。

Timescale Vector 可伴随你从 POC 扩展到生产环境：

• 通过让你在单个数据库中存储关系元数据、向量嵌入和时间序列数据，简化运维。
• 受益于稳固的 PostgreSQL 基础，具备流式备份和复制、高可用性以及行级安全等企业级功能。
• 通过企业级安全性和合规性，带来无忧体验。

如何使用 Timescale Vector

Timescale Vector 可在云 PostgreSQL 平台 Timescale 上使用。（目前没有自托管版本。）

LlamaIndex 用户可获得 Timescale Vector 的 90 天免费试用。

• 要开始使用，请注册 Timescale，创建一个新数据库，并按照本 notebook 操作！
• 有关详细信息和性能基准，请参阅 Timescale Vector 说明博客。
• 有关在 Python 中使用 Timescale Vector 的更多详细信息，请参阅安装说明。

0. 设置

让我们导入本 notebook 所需的所有内容。

如果你在 Colab 中打开这个 Notebook，可能需要先安装 LlamaIndex 🦙。

%pip install llama-index-embeddings-openai
%pip install llama-index-vector-stores-timescalevector

!pip install llama-index

# import logging
# import sys

# Uncomment to see debug logs
# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

import timescale_vector
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.timescalevector import TimescaleVectorStore
from llama_index.core.vector_stores import VectorStoreQuery, MetadataFilters
import textwrap
import openai

设置 OpenAI API 密钥

要为加载到索引中的文档创建嵌入，请配置你的 OpenAI API 密钥：

# Get openAI api key by reading local .env file
# The .env file should contain a line starting with `OPENAI_API_KEY=sk-`
import os
from dotenv import load_dotenv, find_dotenv

_ = load_dotenv(find_dotenv())

# OR set it explicitly
# import os
# os.environ["OPENAI_API_KEY"] = "<your key>"
openai.api_key = os.environ["OPENAI_API_KEY"]

创建 PostgreSQL 数据库并获取 Timescale 服务 URL

你需要一个服务 URL 来连接到你的 Timescale 数据库实例。

首先，在 Timescale 中启动一个新的云数据库（使用上面的链接免费注册）。

要连接到你的云 PostgreSQL 数据库，你需要服务 URI，它可以在创建新数据库后下载的速查表或 .env 文件中找到。

该 URI 大致如下：postgres://tsdbadmin:<password>@<id>.tsdb.cloud.timescale.com:<port>/tsdb?sslmode=require

# Get the service url by reading local .env file
# The .env file should contain a line starting with `TIMESCALE_SERVICE_URL=postgresql://`
import os
from dotenv import load_dotenv, find_dotenv

_ = load_dotenv(find_dotenv())

TIMESCALE_SERVICE_URL = os.environ["TIMESCALE_SERVICE_URL"]

# OR set it explicitly
# TIMESCALE_SERVICE_URL = "postgres://tsdbadmin:<password>@<id>.tsdb.cloud.timescale.com:<port>/tsdb?sslmode=require"

1. 使用 Timescale Vector 进行简单相似度搜索

下载数据

!mkdir -p 'data/paul_graham/'
!wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'

加载文档

在本例中，我们将使用 SimpleDirectoryReader 加载存储在 paul_graham_essay 目录中的文档。

SimpleDirectoryReader 是 LlamaIndex 最常用的数据连接器之一，用于从目录中读取一个或多个文件。

# load sample data from the data directory using a SimpleDirectoryReader
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)

Document ID: 740ce1a1-4d95-40cc-b7f7-6d2874620a53

使用 TimescaleVectorStore 创建 VectorStore 索引

接下来，为了执行相似度搜索，我们首先创建一个 TimescaleVector 向量存储，用于存储文章内容生成的向量嵌入。TimescaleVectorStore 接受几个参数，即上面加载的 service_url，以及 table_name，后者将是存储向量的表名。

然后，我们使用之前的文档，在由 Timescale 支持的文档上创建向量存储索引。

# Create a TimescaleVectorStore to store the documents
vector_store = TimescaleVectorStore.from_params(
    service_url=TIMESCALE_SERVICE_URL,
    table_name="paul_graham_essay",
)

# Create a new VectorStoreIndex using the TimescaleVectorStore
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

查询索引

现在我们已经为 VectorStore 中的文档建立了索引，可以使用默认的 query_engine 针对索引中的文档提问。

请注意，你还可以配置查询引擎，以设置返回的最相似结果数量 top_k，以及用于过滤结果的元数据过滤器。有关更多详细信息，请参阅配置标准查询设置部分。

query_engine = index.as_query_engine()
response = query_engine.query("Did the author work at YC?")

print(textwrap.fill(str(response), 100))

Yes, the author did work at YC.

response = query_engine.query("What did the author work on before college?")

print(textwrap.fill(str(response), 100))

Before college, the author worked on writing and programming. They wrote short stories and also
tried programming on the IBM 1401 computer using an early version of Fortran.

查询现有索引

在上面的示例中，我们根据加载的文档创建了新的 Timescale Vector 向量存储和索引。接下来，我们将了解如何查询现有索引。我们只需要服务 URI 和想要访问的表名。

vector_store = TimescaleVectorStore.from_params(
    service_url=TIMESCALE_SERVICE_URL,
    table_name="paul_graham_essay",
)

index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do before YC?")

print(textwrap.fill(str(response), 100))

Before YC, the author wrote all of YC's internal software in Arc. They also worked on HN and had
three projects: writing essays, working on YC, and working in Arc. However, they gradually stopped
working on Arc due to time constraints and the increasing dependence on it for infrastructure.

2. 使用 ANN 搜索索引加速查询

（注意：这些索引是 ANN 索引，与 LlamaIndex 中的索引概念不同）

你可以通过在嵌入列上创建索引来加速相似度查询。只应在已摄取大部分数据后执行此操作。

Timescale Vector 支持以下索引：

• timescale_vector_index：受 DiskANN 启发的图索引，用于快速相似度搜索（默认）。
• pgvector 的 HNSW 索引：分层可导航小世界图索引，用于快速相似度搜索。
• pgvector 的 IVFFLAT 索引：倒排文件索引，用于快速相似度搜索。

重要提示：在 PostgreSQL 中，每张表在特定列上只能有一个索引。因此，如果你想测试不同索引类型的性能，可以通过以下方式实现：(1) 创建多个使用不同索引的表；(2) 在同一张表中创建多个向量列，并为每列创建不同的索引；或 (3) 删除并重建同一列上的索引并比较结果。

# Instantiate the TimescaleVectorStore from part 1
vector_store = TimescaleVectorStore.from_params(
    service_url=TIMESCALE_SERVICE_URL,
    table_name="paul_graham_essay",
)

在不提供额外参数的情况下使用 create_index() 函数，将使用默认参数创建一个 timescale_vector (DiskANN) 索引。

# Create a timescale vector index (DiskANN)
vector_store.create_index()

你也可以指定索引的参数。有关不同参数及其对性能影响的完整讨论，请参阅 Timescale Vector 文档。

# drop old index
vector_store.drop_index()

# create new timescale vector index (DiskANN) with specified parameters
vector_store.create_index("tsv", max_alpha=1.0, num_neighbors=50)

Timescale Vector 还支持 HNSW 和 ivfflat 索引：

vector_store.drop_index()

# Create an HNSW index
# Note: You don't need to specify m and ef_construction parameters as we set smart defaults.
vector_store.create_index("hnsw", m=16, ef_construction=64)

# Create an IVFFLAT index
# Note: You don't need to specify num_lists and num_records parameters as we set smart defaults.
vector_store.drop_index()
vector_store.create_index("ivfflat", num_lists=20, num_records=1000)

通常我们建议使用 timescale-vector 或 HNSW 索引。

# drop the ivfflat index
vector_store.drop_index()
# Create a timescale vector index (DiskANN)
vector_store.create_index()

3. 带时间过滤的相似度搜索

Timescale Vector 的一个关键用例是高效的时间维度向量搜索。Timescale Vector 通过按时间自动分区向量（及其关联元数据）来实现这一点。这让你能够同时按与查询向量的相似度和时间高效地查询向量。

时间维度向量搜索功能适用于以下应用：

• 存储和检索 LLM 响应历史（例如聊天机器人）
• 查找与查询向量相似的最新嵌入（例如近期新闻）。
• 将相似度搜索限制在相关时间范围内（例如针对知识库提出基于时间的问题）

为了说明如何使用 TimescaleVector 的时间维度向量搜索功能，我们将使用 TimescaleDB 的 git 日志历史作为示例数据集，并针对它提问。每个 git 提交条目都有一个关联的时间戳，以及自然语言消息和其他元数据（例如作者、提交哈希等）。

我们将说明如何创建带基于时间的 uuid 的节点，以及如何使用 TimescaleVector 向量存储运行带时间范围过滤器的相似度搜索。

从 git 日志 CSV 文件中提取内容和元数据

首先，让我们将 git 日志 CSV 文件加载到 PostgreSQL 数据库中名为 timescale_commits 的新集合中。

注意：由于这是演示，我们只处理前 1000 条记录。在实际应用中，你可以加载任意数量的记录。

import pandas as pd
from pathlib import Path

file_path = Path("../data/csv/commit_history.csv")
# Read the CSV file into a DataFrame
df = pd.read_csv(file_path)

# Light data cleaning on CSV
df.dropna(inplace=True)
df = df.astype(str)
df = df[:1000]

# Take a look at the data in the csv (optional)
df.head()

我们将定义一个辅助函数，根据节点的时间戳为节点及关联的向量嵌入创建 uuid。我们会使用此函数为每个 git 日志条目创建 uuid。

重要提示：如果你处理的是文档/节点，并希望将当前日期和时间与向量关联以进行基于时间的搜索，可以跳过此步骤。默认情况下，当节点添加到 Timescale Vector 中的表时，会自动生成 uuid。在我们的场景中，由于希望 uuid 基于过去的时间戳，因此需要手动创建 uuid。

from timescale_vector import client

# Function to take in a date string in the past and return a uuid v1
def create_uuid(date_string: str):
    if date_string is None:
        return None
    time_format = "%a %b %d %H:%M:%S %Y %z"
    datetime_obj = datetime.strptime(date_string, time_format)
    uuid = client.uuid_from_time(datetime_obj)
    return str(uuid)

# Helper functions
from typing import List, Tuple

# Helper function to split name and email given an author string consisting of Name Lastname <email>
def split_name(input_string: str) -> Tuple[str, str]:
    if input_string is None:
        return None, None
    start = input_string.find("<")
    end = input_string.find(">")
    name = input_string[:start].strip()
    return name

from datetime import datetime, timedelta

def create_date(input_string: str) -> datetime:
    if input_string is None:
        return None
    # Define a dictionary to map month abbreviations to their numerical equivalents
    month_dict = {
        "Jan": "01",
        "Feb": "02",
        "Mar": "03",
        "Apr": "04",
        "May": "05",
        "Jun": "06",
        "Jul": "07",
        "Aug": "08",
        "Sep": "09",
        "Oct": "10",
        "Nov": "11",
        "Dec": "12",
    }

    # Split the input string into its components
    components = input_string.split()
    # Extract relevant information
    day = components[2]
    month = month_dict[components[1]]
    year = components[4]
    time = components[3]
    timezone_offset_minutes = int(
        components[5]
    )  # Convert the offset to minutes
    timezone_hours = timezone_offset_minutes // 60  # Calculate the hours
    timezone_minutes = (
        timezone_offset_minutes % 60
    )  # Calculate the remaining minutes
    # Create a formatted string for the timestamptz in PostgreSQL format
    timestamp_tz_str = (
        f"{year}-{month}-{day} {time}+{timezone_hours:02}{timezone_minutes:02}"
    )
    return timestamp_tz_str

接下来，我们将定义一个函数，为每个 git 日志条目创建 TextNode。我们会使用上面定义的辅助函数 create_uuid()，根据每个节点的时间戳为其创建 uuid。还会使用上面的辅助函数 create_date() 和 split_name()，从 git 日志条目中提取相关元数据并添加到节点中。

from llama_index.core.schema import TextNode, NodeRelationship, RelatedNodeInfo

# Create a Node object from a single row of data
def create_node(row):
    record = row.to_dict()
    record_name = split_name(record["author"])
    record_content = (
        str(record["date"])
        + " "
        + record_name
        + " "
        + str(record["change summary"])
        + " "
        + str(record["change details"])
    )
    # Can change to TextNode as needed
    node = TextNode(
        id_=create_uuid(record["date"]),
        text=record_content,
        metadata={
            "commit": record["commit"],
            "author": record_name,
            "date": create_date(record["date"]),
        },
    )
    return node

nodes = [create_node(row) for _, row in df.iterrows()]

接下来，我们将为每个节点的内容创建向量嵌入，以便对与每个节点关联的文本执行相似度搜索。我们将使用 OpenAIEmbedding 模型创建嵌入。

# Create embeddings for nodes
from llama_index.embeddings.openai import OpenAIEmbedding

embedding_model = OpenAIEmbedding()

for node in nodes:
    node_embedding = embedding_model.get_text_embedding(
        node.get_content(metadata_mode="all")
    )
    node.embedding = node_embedding

让我们查看集合中的第一个节点，看看它是什么样子。

print(nodes[0].get_content(metadata_mode="all"))

commit: 44e41c12ab25e36c202f58e068ced262eadc8d16
author: Lakshmi Narayanan Sreethar
date: 2023-09-5 21:03:21+0850

Tue Sep 5 21:03:21 2023 +0530 Lakshmi Narayanan Sreethar Fix segfault in set_integer_now_func When an invalid function oid is passed to set_integer_now_func, it finds out that the function oid is invalid but before throwing the error, it calls ReleaseSysCache on an invalid tuple causing a segfault. Fixed that by removing the invalid call to ReleaseSysCache.  Fixes #6037

print(nodes[0].get_embedding())

[-0.005366453900933266, 0.0016374519327655435, 0.005981510039418936, -0.026256779208779335, -0.03944991156458855, 0.026299940422177315, -0.0200558640062809, -0.01252412423491478, -0.04241368919610977, -0.004758591763675213, 0.05639812350273132, 0.006578581873327494, 0.014833281747996807, 0.009509989991784096, 0.0009675443288870156, -0.013157163746654987, -0.002265996066853404, -0.017048921436071396, 0.006553404498845339, -0.00217068032361567, 0.009085564874112606, 0.011775985360145569, -0.02514895796775818, -0.002679630182683468, 0.0030608929228037596, -3.439458305365406e-05, -0.00363818253390491, -0.03939236328005791, 0.0016806137282401323, -0.01207092497497797, 0.01739421673119068, -0.02241537719964981, -0.01753808930516243, -0.023782167583703995, -0.01598426327109337, -0.02575322426855564, -0.016876274719834328, -0.006380756851285696, -0.0009149408433586359, 0.00704616867005825, -0.0013290246715769172, -0.009776154533028603, -0.013200325891375542, -0.024832438677549362, 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-0.021278781816363335, 0.0027929299976676702, 0.022199567407369614, -0.00439531309530139, -0.004470846150070429, 0.023882878944277763, 0.0055966502986848354, 0.01752370223402977, 0.03401152044534683, 0.007524545304477215, 0.023739006370306015, 0.011214882135391235, 0.029666563495993614, -0.02242976427078247, 0.0077187735587358475, -0.030673673376441002, 0.011869503185153008, -0.005143451038748026, -0.013545620255172253, -0.012624834664165974, -0.02844364568591118, -0.0009243824752047658, -0.037867311388254166, 0.020214123651385307, -0.005165032111108303, 0.07366285473108292, 0.0038521932438015938, -0.020559417083859444, -0.03855790197849274, 0.018070418387651443, -0.007218815851956606, 0.01831500232219696, -0.0014189451467245817, 0.028760164976119995, -0.027666732668876648, 0.022890156134963036, 0.0019530727295204997, 0.011862309649586678, 0.0022030516993254423, -0.012243571691215038, -0.008661140687763691, 0.0015169584657996893, 0.02845803275704384, -0.010711327195167542, 0.007319526746869087, 0.022170793265104294, 0.009186276234686375, 0.019422823563218117, 0.018948042765259743, 0.0028810519725084305, -0.005330485757440329, 0.03004063293337822, -0.002987158251926303, -0.0058700088411569595, -0.04316182807087898, -0.0008155787363648415, -0.012286733835935593, 0.011552982963621616, -0.00666850246489048, 0.0076612248085439205, -0.022026920691132545, -0.001176159828901291, 0.0009864276507869363, 0.00605344632640481, 0.010013544000685215, -0.011912664398550987, 0.010466743260622025, -0.0481685996055603, -0.020458707585930824, 0.016818726435303688, -0.02411307580769062, 0.017912158742547035, 0.002213842235505581, -0.022026920691132545]

将文档和元数据加载到 TimescaleVector 向量存储中

现在我们已经准备好节点并为其添加了嵌入，接下来将它们添加到 TimescaleVector 向量存储中。

我们将根据创建的节点列表创建一个 Timescale Vector 实例。

首先，我们定义一个集合名称，它将成为 PostgreSQL 数据库中的表名。

我们还会定义一个时间增量，并将其传给 time_partition_interval 参数，该参数将用作按时间对数据分区的间隔。每个分区将包含指定时间长度内的数据。为简单起见，我们使用 7 天，但你可以根据用例选择任何合适的值——例如，如果你频繁查询近期向量，可能希望使用更小的时间增量，如 1 天；如果你查询跨越十年以上时间段的向量，则可能希望使用更大的时间增量，如 6 个月或 1 年。

然后，我们将节点添加到 Timescale Vector 向量存储中。

# Create a timescale vector store and add the newly created nodes to it
ts_vector_store = TimescaleVectorStore.from_params(
    service_url=TIMESCALE_SERVICE_URL,
    table_name="li_commit_history",
    time_partition_interval=timedelta(days=7),
)
_ = ts_vector_store.add(nodes)

按时间和相似度查询向量

现在我们已经将文档加载到 TimescaleVector 中，可以按时间和相似度查询它们。

TimescaleVector 提供多种通过带时间过滤的相似度搜索来查询向量的方法。下面我们逐一介绍每种方法。

首先，我们定义查询字符串，并获取该查询字符串的向量嵌入。

# Define query and generate embedding for it
query_str = "What's new with TimescaleDB functions?"
embed_model = OpenAIEmbedding()
query_embedding = embed_model.get_query_embedding(query_str)

然后，我们设置一些将在时间过滤器中使用的变量。

# Time filter variables for query
start_dt = datetime(
    2023, 8, 1, 22, 10, 35
)  # Start date = 1 August 2023, 22:10:35
end_dt = datetime(
    2023, 8, 30, 22, 10, 35
)  # End date = 30 August 2023, 22:10:35
td = timedelta(days=7)  # Time delta = 7 days

方法 1：在提供的开始日期和结束日期范围内过滤。

# Query the vector database
vector_store_query = VectorStoreQuery(
    query_embedding=query_embedding, similarity_top_k=5
)

# return most similar vectors to query between start date and end date date range
# returns a VectorStoreQueryResult object
query_result = ts_vector_store.query(
    vector_store_query, start_date=start_dt, end_date=end_dt
)
query_result

VectorStoreQueryResult(nodes=[TextNode(id_='22747180-31f1-11ee-bd8e-101e36c28c91', embedding=None, metadata={'commit': ' 7aeed663b9c0f337b530fd6cad47704a51a9b2ec', 'author': 'Dmitry Simonenko', 'date': '2023-08-3 14:30:23+0500'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='3273f20a98f02c75847896b929888b05e8751ae5e258d7feb8605bd5290ef8ca', text='Thu Aug 3 14:30:23 2023 +0300 Dmitry Simonenko Feature flags for TimescaleDB features This PR adds several GUCs which allow to enable/disable major timescaledb features:  - enable_hypertable_create - enable_hypertable_compression - enable_cagg_create - enable_policy_create ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='faa8ea00-4686-11ee-b933-c2c7df407c25', embedding=None, metadata={'commit': ' e4facda540286b0affba47ccc63959fefe2a7b26', 'author': 'Sven Klemm', 'date': '2023-08-29 18:13:24+0320'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='6f45ab1cccf673ddf75c625983b6cf2f4a66bbf865a4c1c65025997a470f3bb3', text='Tue Aug 29 18:13:24 2023 +0200 Sven Klemm Add compatibility layer for _timescaledb_internal functions With timescaledb 2.12 all the functions present in _timescaledb_internal were moved into the _timescaledb_functions schema to improve schema security. This patch adds a compatibility layer so external callers of these internal functions will not break and allow for more flexibility when migrating. ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='d7080180-40d2-11ee-af6f-f43e81a0925a', embedding=None, metadata={'commit': ' cf04496e4b4237440274eb25e4e02472fc4e06fc', 'author': 'Sven Klemm', 'date': '2023-08-22 12:01:19+0320'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='d5a20dc83ae04f44aa901ba2f654e80ca68cb21f6a313bd91afcd91e404b471e', text='Tue Aug 22 12:01:19 2023 +0200 Sven Klemm Move utility functions to _timescaledb_functions schema To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - generate_uuid() - get_git_commit() - get_os_info() - tsl_loaded() ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='01b10780-4649-11ee-a375-5719b2881af3', embedding=None, metadata={'commit': ' a9751ccd5eb030026d7b975d22753f5964972389', 'author': 'Sven Klemm', 'date': '2023-08-29 10:49:47+0320'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='8fde14d147def41808d82bf2ffa35e1e0ed78b0331962907cee856af34a34e44', text='Tue Aug 29 10:49:47 2023 +0200 Sven Klemm Move partitioning functions to _timescaledb_functions schema To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - get_partition_for_key(val anyelement) - get_partition_hash(val anyelement) ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), TextNode(id_='e7ba7f80-36af-11ee-9479-6c18a6a65db1', embedding=None, metadata={'commit': ' 44eab9cf9bef34274c88efd37a750eaa74cd8044', 'author': 'Konstantina Skovola', 'date': '2023-08-9 15:26:03+0500'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='f0db9c719928ecc16653bbf0a44f1eaeb221ac79dae16fd36710044d1561dbfa', text='Wed Aug 9 15:26:03 2023 +0300 Konstantina Skovola Release 2.11.2 This release contains bug fixes since the 2.11.1 release. We recommend that you upgrade at the next available opportunity.  **Features** * #5923 Feature flags for TimescaleDB features  **Bugfixes** * #5680 Fix DISTINCT query with JOIN on multiple segmentby columns * #5774 Fixed two bugs in decompression sorted merge code * #5786 Ensure pg_config --cppflags are passed * #5906 Fix quoting owners in sql scripts. * #5912 Fix crash in 1-step integer policy creation  **Thanks** * @mrksngl for submitting a PR to fix extension upgrade scripts * @ericdevries for reporting an issue with DISTINCT queries using segmentby columns of compressed hypertable ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n')], similarities=[0.18141598590707553, 0.1821951378700205, 0.1948705199438009, 0.19657938500765504, 0.19664154042725346], ids=[UUID('22747180-31f1-11ee-bd8e-101e36c28c91'), UUID('faa8ea00-4686-11ee-b933-c2c7df407c25'), UUID('d7080180-40d2-11ee-af6f-f43e81a0925a'), UUID('01b10780-4649-11ee-a375-5719b2881af3'), UUID('e7ba7f80-36af-11ee-9479-6c18a6a65db1')])

让我们检查相似度搜索返回的节点：

# for each node in the query result, print the node metadata date
for node in query_result.nodes:
    print("-" * 80)
    print(node.metadata["date"])
    print(node.get_content(metadata_mode="all"))

--------------------------------------------------------------------------------
2023-08-3 14:30:23+0500
commit:  7aeed663b9c0f337b530fd6cad47704a51a9b2ec
author: Dmitry Simonenko
date: 2023-08-3 14:30:23+0500

Thu Aug 3 14:30:23 2023 +0300 Dmitry Simonenko Feature flags for TimescaleDB features This PR adds several GUCs which allow to enable/disable major timescaledb features:  - enable_hypertable_create - enable_hypertable_compression - enable_cagg_create - enable_policy_create
--------------------------------------------------------------------------------
2023-08-29 18:13:24+0320
commit:  e4facda540286b0affba47ccc63959fefe2a7b26
author: Sven Klemm
date: 2023-08-29 18:13:24+0320

Tue Aug 29 18:13:24 2023 +0200 Sven Klemm Add compatibility layer for _timescaledb_internal functions With timescaledb 2.12 all the functions present in _timescaledb_internal were moved into the _timescaledb_functions schema to improve schema security. This patch adds a compatibility layer so external callers of these internal functions will not break and allow for more flexibility when migrating.
--------------------------------------------------------------------------------
2023-08-22 12:01:19+0320
commit:  cf04496e4b4237440274eb25e4e02472fc4e06fc
author: Sven Klemm
date: 2023-08-22 12:01:19+0320

Tue Aug 22 12:01:19 2023 +0200 Sven Klemm Move utility functions to _timescaledb_functions schema To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - generate_uuid() - get_git_commit() - get_os_info() - tsl_loaded()
--------------------------------------------------------------------------------
2023-08-29 10:49:47+0320
commit:  a9751ccd5eb030026d7b975d22753f5964972389
author: Sven Klemm
date: 2023-08-29 10:49:47+0320

Tue Aug 29 10:49:47 2023 +0200 Sven Klemm Move partitioning functions to _timescaledb_functions schema To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - get_partition_for_key(val anyelement) - get_partition_hash(val anyelement)
--------------------------------------------------------------------------------
2023-08-9 15:26:03+0500
commit:  44eab9cf9bef34274c88efd37a750eaa74cd8044
author: Konstantina Skovola
date: 2023-08-9 15:26:03+0500

Wed Aug 9 15:26:03 2023 +0300 Konstantina Skovola Release 2.11.2 This release contains bug fixes since the 2.11.1 release. We recommend that you upgrade at the next available opportunity.  **Features** * #5923 Feature flags for TimescaleDB features  **Bugfixes** * #5680 Fix DISTINCT query with JOIN on multiple segmentby columns * #5774 Fixed two bugs in decompression sorted merge code * #5786 Ensure pg_config --cppflags are passed * #5906 Fix quoting owners in sql scripts. * #5912 Fix crash in 1-step integer policy creation  **Thanks** * @mrksngl for submitting a PR to fix extension upgrade scripts * @ericdevries for reporting an issue with DISTINCT queries using segmentby columns of compressed hypertable

请注意，查询仅返回指定日期范围内的结果。

方法 2：在提供的开始日期以及之后的某个时间增量范围内过滤。

vector_store_query = VectorStoreQuery(
    query_embedding=query_embedding, similarity_top_k=5
)

# return most similar vectors to query from start date and a time delta later
query_result = ts_vector_store.query(
    vector_store_query, start_date=start_dt, time_delta=td
)

for node in query_result.nodes:
    print("-" * 80)
    print(node.metadata["date"])
    print(node.get_content(metadata_mode="all"))

--------------------------------------------------------------------------------
2023-08-3 14:30:23+0500
commit:  7aeed663b9c0f337b530fd6cad47704a51a9b2ec
author: Dmitry Simonenko
date: 2023-08-3 14:30:23+0500

Thu Aug 3 14:30:23 2023 +0300 Dmitry Simonenko Feature flags for TimescaleDB features This PR adds several GUCs which allow to enable/disable major timescaledb features:  - enable_hypertable_create - enable_hypertable_compression - enable_cagg_create - enable_policy_create
--------------------------------------------------------------------------------
2023-08-7 19:49:47+-500
commit:  5bba74a2ec083728f8e93e09d03d102568fd72b5
author: Fabrízio de Royes Mello
date: 2023-08-7 19:49:47+-500

Mon Aug 7 19:49:47 2023 -0300 Fabrízio de Royes Mello Relax strong table lock when refreshing a CAGG When refreshing a Continuous Aggregate we take a table lock on _timescaledb_catalog.continuous_aggs_invalidation_threshold when processing the invalidation logs (the first transaction of the refresh Continuous Aggregate procedure). It means that even two different Continuous Aggregates over two different hypertables will wait each other in the first phase of the refreshing procedure. Also it lead to problems when a pg_dump is running because it take an AccessShareLock on tables so Continuous Aggregate refresh execution will wait until the pg_dump finish.  Improved it by relaxing the strong table-level lock to a row-level lock so now the Continuous Aggregate refresh procedure can be executed in multiple sessions with less locks.  Fix #3554
--------------------------------------------------------------------------------
2023-08-3 14:36:39+0500
commit:  2863daf3df83c63ee36c0cf7b66c522da5b4e127
author: Dmitry Simonenko
date: 2023-08-3 14:36:39+0500

Thu Aug 3 14:36:39 2023 +0300 Dmitry Simonenko Support CREATE INDEX ONLY ON main table This PR adds support for CREATE INDEX ONLY ON clause which allows to create index only on the main table excluding chunks.  Fix #5908
--------------------------------------------------------------------------------
2023-08-2 20:24:14+0140
commit:  3af0d282ea71d9a8f27159a6171e9516e62ec9cb
author: Lakshmi Narayanan Sreethar
date: 2023-08-2 20:24:14+0140

Wed Aug 2 20:24:14 2023 +0100 Lakshmi Narayanan Sreethar PG16: ExecInsertIndexTuples requires additional parameter PG16 adds a new boolean parameter to the ExecInsertIndexTuples function to denote if the index is a BRIN index, which is then used to determine if the index update can be skipped. The fix also removes the INDEX_ATTR_BITMAP_ALL enum value.  Adapt these changes by updating the compat function to accomodate the new parameter added to the ExecInsertIndexTuples function and using an alternative for the removed INDEX_ATTR_BITMAP_ALL enum value.  postgres/postgres@19d8e23
--------------------------------------------------------------------------------
2023-08-7 16:36:17+0500
commit:  373c55662ca5f8a2993abf9b2aa7f5f4006b3229
author: Konstantina Skovola
date: 2023-08-7 16:36:17+0500

Mon Aug 7 16:36:17 2023 +0300 Konstantina Skovola Fix ordered append for partially compressed chunks In the exclusive presence of partially compressed chunks, this optimization was not applied because no pathkeys were supplied. Additionally, this patch makes sure that if applicable, the `enable_decompression_sorted_merge` optimization is chosen for the path, since it is more beneficial due to the ability to push down the sort below DecompressChunk.

同样，请注意仅返回开始日期（8 月 1 日）与定义的稍后时间增量（7 天后）之间的节点。

方法 3：在提供的结束日期以及之前的某个时间增量范围内过滤。

vector_store_query = VectorStoreQuery(
    query_embedding=query_embedding, similarity_top_k=5
)

# return most similar vectors to query from end date and a time delta earlier
query_result = ts_vector_store.query(
    vector_store_query, end_date=end_dt, time_delta=td
)

for node in query_result.nodes:
    print("-" * 80)
    print(node.metadata["date"])
    print(node.get_content(metadata_mode="all"))

--------------------------------------------------------------------------------
2023-08-29 18:13:24+0320
commit:  e4facda540286b0affba47ccc63959fefe2a7b26
author: Sven Klemm
date: 2023-08-29 18:13:24+0320

Tue Aug 29 18:13:24 2023 +0200 Sven Klemm Add compatibility layer for _timescaledb_internal functions With timescaledb 2.12 all the functions present in _timescaledb_internal were moved into the _timescaledb_functions schema to improve schema security. This patch adds a compatibility layer so external callers of these internal functions will not break and allow for more flexibility when migrating.
--------------------------------------------------------------------------------
2023-08-29 10:49:47+0320
commit:  a9751ccd5eb030026d7b975d22753f5964972389
author: Sven Klemm
date: 2023-08-29 10:49:47+0320

Tue Aug 29 10:49:47 2023 +0200 Sven Klemm Move partitioning functions to _timescaledb_functions schema To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - get_partition_for_key(val anyelement) - get_partition_hash(val anyelement)
--------------------------------------------------------------------------------
2023-08-28 23:26:23+0320
commit:  b2a91494a11d8b82849b6f11f9ea6dc26ef8a8cb
author: Sven Klemm
date: 2023-08-28 23:26:23+0320

Mon Aug 28 23:26:23 2023 +0200 Sven Klemm Move ddl_internal functions to _timescaledb_functions schema To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - chunk_constraint_add_table_constraint(_timescaledb_catalog.chunk_constraint) - chunk_drop_replica(regclass,name) - chunk_index_clone(oid) - chunk_index_replace(oid,oid) - create_chunk_replica_table(regclass,name) - drop_stale_chunks(name,integer[]) - health() - hypertable_constraint_add_table_fk_constraint(name,name,name,integer) - process_ddl_event() - wait_subscription_sync(name,name,integer,numeric)
--------------------------------------------------------------------------------
2023-08-29 14:47:57+0320
commit:  08231c8aacd17152f315ad36d95c031fb46073aa
author: Jan Nidzwetzki
date: 2023-08-29 14:47:57+0320

Tue Aug 29 14:47:57 2023 +0200 Jan Nidzwetzki Export is_decompress_chunk_path / is_gapfill_path This patch adds the 'ts_' prefix to the function names of is_decompress_chunk_path and is_gapfill_path and makes them available for use by other parts of TimescaleDB.
--------------------------------------------------------------------------------
2023-08-28 15:32:54+0320
commit:  6576d969b319dac8e7fd08a9cf4cfc8197b34d1d
author: Sven Klemm
date: 2023-08-28 15:32:54+0320

Mon Aug 28 15:32:54 2023 +0200 Sven Klemm Move log invalidation functions to _timescaledb_functions schema To increase schema security we do not want to mix our own internal objects with user objects. Since chunks are created in the _timescaledb_internal schema our internal functions should live in a different dedicated schema. This patch make the necessary adjustments for the following functions:  - cagg_watermark(integer) - cagg_watermark_materialized(integer) - hypertable_invalidation_log_delete(integer) - invalidation_cagg_log_add_entry(integer,bigint,bigint) - invalidation_hyper_log_add_entry(integer,bigint,bigint) - invalidation_process_cagg_log(integer,integer,regtype,bigint,bigint,integer[],bigint[],bigint[]) - invalidation_process_cagg_log(integer,integer,regtype,bigint,bigint,integer[],bigint[],bigint[],text[]) - invalidation_process_hypertable_log(integer,integer,regtype,integer[],bigint[],bigint[]) - invalidation_process_hypertable_log(integer,integer,regtype,integer[],bigint[],bigint[],text[]) - materialization_invalidation_log_delete(integer)

主要结论是，在上述每个结果中，仅返回指定时间范围内的向量。这些查询非常高效，因为它们只需搜索相关分区。

4. 将 TimescaleVector 存储用作检索器和查询引擎

现在我们已经探索了基本相似度搜索和带时间过滤器的相似度搜索，接下来看看如何将 Timescale Vector 的这些功能与 LlamaIndex 的检索器和查询引擎配合使用。

首先，我们将了解如何将 TimescaleVector 用作检索器，特别是向量存储检索器。

为了将检索到的节点限制在相关时间范围内，我们可以使用 TimescaleVector 的时间过滤器。只需在创建检索器时将时间过滤器参数作为 vector_strored_kwargs 传入即可。

from llama_index.core import VectorStoreIndex
from llama_index.core import StorageContext

index = VectorStoreIndex.from_vector_store(ts_vector_store)
retriever = index.as_retriever(
    vector_store_kwargs=({"start_date": start_dt, "time_delta": td})
)
retriever.retrieve("What's new with TimescaleDB functions?")

[NodeWithScore(node=TextNode(id_='22747180-31f1-11ee-bd8e-101e36c28c91', embedding=None, metadata={'commit': ' 7aeed663b9c0f337b530fd6cad47704a51a9b2ec', 'author': 'Dmitry Simonenko', 'date': '2023-08-3 14:30:23+0500'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='3273f20a98f02c75847896b929888b05e8751ae5e258d7feb8605bd5290ef8ca', text='Thu Aug 3 14:30:23 2023 +0300 Dmitry Simonenko Feature flags for TimescaleDB features This PR adds several GUCs which allow to enable/disable major timescaledb features:  - enable_hypertable_create - enable_hypertable_compression - enable_cagg_create - enable_policy_create ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.1813839050209377),
 NodeWithScore(node=TextNode(id_='b5583780-3574-11ee-871a-5a8c45d660c8', embedding=None, metadata={'commit': ' 5bba74a2ec083728f8e93e09d03d102568fd72b5', 'author': 'Fabrízio de Royes Mello', 'date': '2023-08-7 19:49:47+-500'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, hash='ec25a09b9dd34ed2650aefc2ce71e1b11fa471ffc43683715de788d202c6cdc8', text='Mon Aug 7 19:49:47 2023 -0300 Fabrízio de Royes Mello Relax strong table lock when refreshing a CAGG When refreshing a Continuous Aggregate we take a table lock on _timescaledb_catalog.continuous_aggs_invalidation_threshold when processing the invalidation logs (the first transaction of the refresh Continuous Aggregate procedure). It means that even two different Continuous Aggregates over two different hypertables will wait each other in the first phase of the refreshing procedure. Also it lead to problems when a pg_dump is running because it take an AccessShareLock on tables so Continuous Aggregate refresh execution will wait until the pg_dump finish.  Improved it by relaxing the strong table-level lock to a row-level lock so now the Continuous Aggregate refresh procedure can be executed in multiple sessions with less locks.  Fix #3554 ', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.23511557892997959)]

接下来，我们将了解如何将 TimescaleVector 用作查询引擎。

同样，我们在创建查询引擎时将时间过滤器参数作为 vector_strored_kwargs 传入，从而使用 TimescaleVector 的时间过滤器将搜索限制在相关时间范围内。

index = VectorStoreIndex.from_vector_store(ts_vector_store)
query_engine = index.as_query_engine(
    vector_store_kwargs=({"start_date": start_dt, "end_date": end_dt})
)

# query_str = "What's new with TimescaleDB? List 3 new features"
query_str = (
    "What's new with TimescaleDB functions? When were these changes made and"
    " by whom?"
)
response = query_engine.query(query_str)
print(str(response))

TimescaleDB functions have undergone changes recently. These changes were made by Sven Klemm on August 29, 2023. The changes involve adding a compatibility layer for _timescaledb_internal functions. This layer ensures that external callers of these internal functions will not break and allows for more flexibility when migrating.