Timescale 向量存储 (PostgreSQL)

本笔记本展示了如何使用Postgres向量存储TimescaleVector来存储和查询向量嵌入。

什么是 Timescale Vector？

Timescale Vector 是专为AI应用打造的 PostgreSQL++。

Timescale Vector 使您能够在 PostgreSQL 中高效存储和查询数百万个向量嵌入。

通过受DiskANN启发的索引算法，在数百万向量上实现更快更准确的相似性搜索，从而增强pgvector。
通过基于时间的自动分区和索引，实现快速的基于时间的向量搜索。
为查询向量嵌入和关系数据提供熟悉的SQL接口。

Timescale Vector 从概念验证到生产环境都能随您扩展：

通过在单一数据库中存储关系型元数据、向量嵌入和时间序列数据，简化操作。
受益于坚如磐石的 PostgreSQL 基础，具备企业级功能，如流式备份与复制、高可用性和行级安全性。
提供企业级安全与合规性，带来无忧体验。

如何使用 Timescale Vector

Timescale Vector 可在 Timescale 云端 PostgreSQL 平台上使用。（目前暂无自托管版本。）

LlamaIndex 用户可享受 Timescale Vector 的 90 天免费试用。

要开始使用，请注册 Timescale，创建新数据库并按照此笔记本操作！
查看 Timescale Vector 解释博客了解详情和性能基准。
查看安装说明以获取有关在 Python 中使用 Timescale Vector 的更多详细信息。

0. 设置

让我们导入本笔记本所需的所有内容。

如果您在 Colab 上打开这个笔记本，您可能需要安装 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向量，让我们配置您的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"]

创建一个PostgreSQL数据库并获取Timescale服务URL

您需要一个服务URL来连接到您的Timescale数据库实例。

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

要连接到您的云端 PostgreSQL 数据库，您需要服务 URI，该信息可在速查表或创建新数据库后下载的 .env 文件中找到。

URI 将类似于这样：postgres://tsdbadmin:<password>@<id>.tsdb.cloud.timescale.com:<port>/tsdb?sslmode=requirepostgres://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 进行简单相似性搜索

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

对于这个示例，我们将使用 SimpleDirectoryReader 来加载存储在 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 创建向量存储索引

接下来，为了执行相似性搜索，我们首先创建一个 TimescaleVector 向量存储来存储从文章内容中提取的向量嵌入。TimescaleVectorStore 接受几个参数，即我们之前加载的 service_url，以及一个 table_name，这将是存储向量的表名。

然后我们使用之前的文档在由 Timescale 支持的文档上创建一个向量存储索引。

# 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

现在我们已经将文档索引到VectorStore中，可以通过使用默认的query_engine来询问索引中文档的相关问题。

请注意，您还可以配置查询引擎以设置返回的前k个最相似结果，以及用于筛选结果的元数据过滤器。更多详情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，我们从加载的文档创建了一个新的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：这些索引是近似最近邻索引，与LlamaIndex中的索引概念不同）

您可以通过在嵌入列上创建索引来加速相似性查询。您应该在已摄入大部分数据后才执行此操作。

Timescale Vector 支持以下索引类型：

timescale_vector_index：一种受磁盘-ann启发的图索引，用于快速相似性搜索（默认）。
pgvector的HNSW索引：一种用于快速相似性搜索的分层可导航小世界图索引。
pgvector的IVFFLAT索引：用于快速相似性搜索的倒排文件索引。

重要提示：在 PostgreSQL 中，每个表在特定列上只能有一个索引。因此，如果您想测试不同索引类型的性能，可以通过以下方式实现：(1) 创建具有不同索引的多个表，(2) 在同一表中创建多个向量列并在每个列上创建不同的索引，或 (3) 删除并重新在同一列上创建索引并比较结果。

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

使用 create_index() 函数而不带额外参数时，默认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()

您也可以为索引指定参数。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)

我们通常建议使用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. 基于时间过滤的相似性搜索

Timescale Vector 的一个关键应用场景是高效的时间向量搜索。Timescale Vector 通过按时间自动分区向量（及相关元数据）来实现这一功能。这使您能够根据与查询向量的相似度和时间两个维度来高效查询向量。

基于时间的向量搜索功能对于以下应用场景很有帮助：

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（例如最新新闻）。
将相似性搜索限制在相关时间范围内（例如，对知识库提出基于时间的问题）

为了演示如何使用 TimescaleVector 基于时间的向量搜索功能，我们将使用 TimescaleDB 的 git 日志历史作为示例数据集并提出相关问题。每个 git 提交条目都包含与之关联的时间戳、自然语言消息和其他元数据（例如作者、提交哈希等）。

我们将演示如何使用TimescaleVector向量存储创建基于时间的UUID节点，以及如何运行带时间范围过滤器的相似性搜索。

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：由于这是一个演示，我们仅处理前1000条记录。在实际应用中，您可以根据需要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，用于根据时间戳为节点及其关联的向量嵌入创建唯一标识符。我们将使用此函数为每个git日志条目生成唯一标识符。

重要提示：如果您正在处理文档/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 表中的时候，系统会自动生成一个 uuid。在我们的案例中，由于我们希望 uuid 基于过去的时间戳，我们需要手动创建这些 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来为每个git日志条目创建TextNode。我们将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生成uuid。同时我们将使用上面定义的辅助函数create_date()和split_name()，从git日志条目中提取相关元数据并将其添加到节点中。

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，以便能够对与每个节点关联的文本执行相似性搜索。我们将使用 OpenAIEmbedding 模型来创建嵌入。

# 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

让我们检查集合中的第一个节点，看看它是什么样子的。

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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0.007319526746869087, 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它们添加了嵌入，让我们将它们添加到我们的 TimescaleVector 向量存储中。

我们将从创建的节点列表中创建一个 Timescale Vector 实例。

首先，我们将定义一个集合名称，这将是我们在 PostgreSQL 数据库中的表名。

我们还将定义一个时间增量，将其传递给 time_partition_interval 参数，该参数将用作按时间划分数据的时间间隔。每个分区将包含指定时长内的数据。为简化起见，我们将使用7天，但您可以根据实际用例选择任意合适的值——例如，如果您频繁查询近期向量，可能希望使用较小的时间增量（如1天）；如果您查询跨越十年时间跨度的向量，则可能希望使用较大的时间增量（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，我们可以按时间和相似度进行查询。

TimescaleVector 提供了多种方法，通过基于时间的过滤进行相似性搜索来查询向量。下面让我们逐一了解每种方法。

首先我们定义一个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进行筛选。

# 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')])

让我们检查从相似性搜索返回的节点：

# 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：在提供的起始日期和时间增量后筛选。

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.

再次注意，只有起始日期（8月1日）与定义的时间增量之后（7天后）之间的节点被返回。

方法3：在提供的结束日期和时间增量之前进行筛选。

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.
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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. 使用 TimescaleVector 存储作为检索器和查询引擎

既然我们已经探讨了基础相似性搜索和带有时基过滤器的相似性搜索，现在让我们来看看如何将这些Timescale Vector的功能与LLamaIndex的检索器和查询引擎结合使用。

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。在创建检索器时，我们只需将时间过滤器参数作为 vector_strored_kwargs 传入。

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。

再次，我们使用 TimescaleVector 的时间过滤器，通过在创建查询引擎时将时间过滤参数作为 vector_strored_kwargs 传递，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.