时间刻度向量 （Postgres）
Timescale Vector 为
PostgreSQL++AI 应用程序的向量数据库。
此笔记本介绍如何使用 Postgres 矢量数据库Timescale Vector.您将学习如何使用 TimescaleVector 进行 （1） 语义搜索，（2） 基于时间的向量搜索，（3） 自查询，以及 （4） 如何创建索引以加快查询速度。
什么是 Timescale Vector？
Timescale Vector使您能够有效地存储和查询数百万个向量嵌入PostgreSQL.
- 提高
pgvector通过DiskANN启发了索引算法。 - 通过基于时间的自动分区和索引实现基于时间的快速向量搜索。
- 提供熟悉的 SQL 接口，用于查询向量嵌入和关系数据。
Timescale Vector是云PostgreSQL适用于随您从 POC 扩展到生产环境的 AI：
- 使您能够将关系元数据、向量嵌入和时间序列数据存储在单个数据库中，从而简化作。
- 受益于坚如磐石的 PostgreSQL 基础，具有企业级功能，如流式备份和复制、高可用性和行级安全性。
- 通过企业级安全性和合规性实现无忧体验。
如何访问 Timescale Vector
Timescale Vector在云 PostgreSQL 平台 Timescale 上可用。（目前没有自托管版本。
LangChain 用户可以免费试用 Timescale Vector 的 90 天。
- 要开始使用，请注册 Timescale，创建一个新数据库并关注此笔记本！
- 有关更多详细信息和性能基准，请参阅 Timescale Vector 解释器博客。
- 有关在 Python 中使用 Timescale Vector 的更多详细信息，请参阅安装说明。
设置
请按照以下步骤作，为学习本教程做好准备。
# Pip install necessary packages
%pip install --upgrade --quiet timescale-vector
%pip install --upgrade --quiet langchain-openai langchain-community
%pip install --upgrade --quiet tiktoken
在此示例中，我们将使用OpenAIEmbeddings，那么让我们加载您的 OpenAI API 密钥。
import os
# Run export OPENAI_API_KEY=sk-YOUR_OPENAI_API_KEY...
# Get openAI api key by reading local .env file
from dotenv import find_dotenv, load_dotenv
_ = load_dotenv(find_dotenv())
OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
# Get the API key and save it as an environment variable
# import os
# import getpass
# os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from typing import Tuple
接下来，我们将导入所需的 Python 库和 LangChain 中的库。请注意，我们将timescale-vector库以及 TimescaleVector LangChain vectorstore 的 LangChain Vectorstore 中。
from datetime import datetime, timedelta
from langchain_community.document_loaders import TextLoader
from langchain_community.document_loaders.json_loader import JSONLoader
from langchain_community.vectorstores.timescalevector import TimescaleVector
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
1. 使用欧几里得距离进行相似性搜索（默认）
首先，我们将看一个对国情咨文演讲进行相似性搜索查询的示例，以查找与给定查询句子最相似的句子。我们将使用欧几里得距离作为相似度指标。
# Load the text and split it into chunks
loader = TextLoader("../../../extras/modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
接下来，我们将加载 Timescale 数据库的服务 URL。
如果您还没有，请注册 Timescale 并创建一个新数据库。
然后，要连接到您的 PostgreSQL 数据库，您需要您的服务 URI，该 URI 可以在备忘单或.env创建新数据库后下载的文件。
URI 将如下所示：postgres://tsdbadmin:<password>@<id>.tsdb.cloud.timescale.com:<port>/tsdb?sslmode=require.
# Timescale Vector needs the service url to your cloud database. You can see this as soon as you create the
# service in the cloud UI or in your credentials.sql file
SERVICE_URL = os.environ["TIMESCALE_SERVICE_URL"]
# Specify directly if testing
# SERVICE_URL = "postgres://tsdbadmin:<password>@<id>.tsdb.cloud.timescale.com:<port>/tsdb?sslmode=require"
# # You can get also it from an environment variables. We suggest using a .env file.
# import os
# SERVICE_URL = os.environ.get("TIMESCALE_SERVICE_URL", "")
接下来，我们创建一个 TimescaleVector vectorstore。我们指定一个集合名称，该名称将是存储数据的表的名称。
注意： 当创建 TimescaleVector 的新实例时，TimescaleVector 模块将尝试创建一个具有集合名称的表。因此，请确保集合名称是唯一的（即它不存在）。
# The TimescaleVector Module will create a table with the name of the collection.
COLLECTION_NAME = "state_of_the_union_test"
# Create a Timescale Vector instance from the collection of documents
db = TimescaleVector.from_documents(
embedding=embeddings,
documents=docs,
collection_name=COLLECTION_NAME,
service_url=SERVICE_URL,
)
现在我们已经加载了数据，我们可以执行相似性搜索。
query = "What did the president say about Ketanji Brown Jackson"
docs_with_score = db.similarity_search_with_score(query)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print(doc.page_content)
print("-" * 80)
--------------------------------------------------------------------------------
Score: 0.18443380687035138
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.18452197313308139
Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections.
Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service.
One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court.
And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.21720781018594182
A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.
And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.
We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.
We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.
We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster.
We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.21724902288621384
A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans.
And if we are to advance liberty and justice, we need to secure the Border and fix the immigration system.
We can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling.
We’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers.
We’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster.
We’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.
--------------------------------------------------------------------------------
使用 Timescale 向量作为检索器
初始化 TimescaleVector 存储后，您可以将其用作检索器。
# Use TimescaleVector as a retriever
retriever = db.as_retriever()
print(retriever)
tags=['TimescaleVector', 'OpenAIEmbeddings'] metadata=None vectorstore=<langchain_community.vectorstores.timescalevector.TimescaleVector object at 0x10fc8d070> search_type='similarity' search_kwargs={}
让我们看一个使用 Timescale Vector 作为 RetrievalQA 链和 stuff 文档链的检索器的示例。
在此示例中，我们将询问与上述相同的查询，但这次我们将从 Timescale Vector 返回的相关文档传递给 LLM，以用作上下文来回答我们的问题。
首先，我们将创建我们的 stuff 链：
# Initialize GPT3.5 model
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0.1, model="gpt-3.5-turbo-16k")
# Initialize a RetrievalQA class from a stuff chain
from langchain.chains import RetrievalQA
qa_stuff = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
verbose=True,
)
API 参考：ChatOpenAI | 检索 QA
query = "What did the president say about Ketanji Brown Jackson?"
response = qa_stuff.run(query)
[1m> Entering new RetrievalQA chain...[0m
[1m> Finished chain.[0m
print(response)
The President said that he nominated Circuit Court of Appeals Judge Ketanji Brown Jackson, who is one of our nation's top legal minds and will continue Justice Breyer's legacy of excellence. He also mentioned that since her nomination, she has received a broad range of support from various groups, including the Fraternal Order of Police and former judges appointed by Democrats and Republicans.
2. 使用基于时间的过滤进行相似性搜索
Timescale Vector 的一个关键用例是高效的基于时间的向量搜索。Timescale Vector 通过按时间自动对向量（和相关元数据）进行分区来实现这一点。这允许您通过与查询向量的相似性和时间来有效地查询向量。
基于时间的矢量搜索功能对以下应用很有帮助：
- 存储和检索 LLM 响应历史记录（例如聊天机器人）
- 查找类似于查询向量的最新嵌入（例如最近的新闻）。
- 将相似性搜索限制在相关的时间范围内（例如，询问有关知识库的基于时间的问题）
为了说明如何使用 TimescaleVector 的基于时间的向量搜索功能，我们将询问有关 TimescaleDB 的 git log history 的问题。我们将说明如何使用基于时间的 uuid 添加文档，以及如何使用时间范围筛选器运行相似性搜索。
从 git log JSON 中提取内容和元数据
首先，让我们将 git 日志数据加载到 PostgreSQL 数据库中名为timescale_commits.
我们将定义一个辅助函数，以根据其时间戳为文档和关联的向量嵌入创建 uuid。我们将使用此函数为每个 git log 条目创建一个 uuid。
重要说明：如果您正在处理文档，并且希望当前日期和时间与向量关联以进行基于时间的搜索，则可以跳过此步骤。默认情况下，在摄取文档时，将自动生成 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)
接下来，我们将定义一个元数据函数，以从 JSON 记录中提取相关元数据。我们将此函数传递给 JSONLoader。有关更多详细信息，请参阅 JSON 文档加载器文档。
# 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()
email = input_string[start + 1 : end].strip()
return name, email
# Helper function to transform a date string into a timestamp_tz string
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
# Metadata extraction function to extract metadata from a JSON record
def extract_metadata(record: dict, metadata: dict) -> dict:
record_name, record_email = split_name(record["author"])
metadata["id"] = create_uuid(record["date"])
metadata["date"] = create_date(record["date"])
metadata["author_name"] = record_name
metadata["author_email"] = record_email
metadata["commit_hash"] = record["commit"]
return metadata
接下来，您需要下载示例数据集并将其放在与此笔记本相同的目录中。
您可以使用以下命令：
# Download the file using curl and save it as commit_history.csv
# Note: Execute this command in your terminal, in the same directory as the notebook
!curl -O https://s3.amazonaws.com/assets.timescale.com/ai/ts_git_log.json
最后，我们可以初始化 JSON 加载程序来解析 JSON 记录。为简单起见，我们还删除了空记录。
# Define path to the JSON file relative to this notebook
# Change this to the path to your JSON file
FILE_PATH = "../../../../../ts_git_log.json"
# Load data from JSON file and extract metadata
loader = JSONLoader(
file_path=FILE_PATH,
jq_schema=".commit_history[]",
text_content=False,
metadata_func=extract_metadata,
)
documents = loader.load()
# Remove documents with None dates
documents = [doc for doc in documents if doc.metadata["date"] is not None]
print(documents[0])
page_content='{"commit": "44e41c12ab25e36c202f58e068ced262eadc8d16", "author": "Lakshmi Narayanan Sreethar<lakshmi@timescale.com>", "date": "Tue Sep 5 21:03:21 2023 +0530", "change summary": "Fix segfault in set_integer_now_func", "change details": "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 "}' metadata={'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/ts_git_log.json', 'seq_num': 1, 'id': '8b407680-4c01-11ee-96a6-b82284ddccc6', 'date': '2023-09-5 21:03:21+0850', 'author_name': 'Lakshmi Narayanan Sreethar', 'author_email': 'lakshmi@timescale.com', 'commit_hash': '44e41c12ab25e36c202f58e068ced262eadc8d16'}
将文档和元数据加载到 TimescaleVector vectorstore 中
现在我们已经准备好了我们的文档，让我们处理它们并加载它们，以及它们的向量嵌入表示到我们的 TimescaleVector 向量存储中。
由于这是一个演示，我们只会加载前 500 条记录。在实践中，您可以根据需要加载任意数量的记录。
NUM_RECORDS = 500
documents = documents[:NUM_RECORDS]
然后，如果需要，我们使用 CharacterTextSplitter 将文档拆分为更小的块，以便于嵌入。请注意，此拆分过程会保留每个文档的元数据。
# Split the documents into chunks for embedding
text_splitter = CharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
)
docs = text_splitter.split_documents(documents)
接下来，我们将从完成预处理的文档集合中创建一个 Timescale Vector 实例。
首先，我们将定义一个集合名称，它将是 PostgreSQL 数据库中的表的名称。
我们还将定义一个时间增量，并将其传递给time_partition_interval参数，该参数将用作按时间对数据进行分区的间隔。每个分区将包含指定时间长度的数据。为简单起见，我们将使用 7 天，但您可以选择任何适合您的用例的值 - 例如，如果您经常查询最近的向量，则可能需要使用较小的时间增量，例如 1 天，或者如果您查询长达 10 年的向量，则可能需要使用更大的时间增量，例如 6 个月或 1 年。
最后，我们将创建 TimescaleVector 实例。我们指定ids参数设置为uuid字段。我们这样做是因为我们希望 uuid 的时间部分反映过去的日期（即提交的时间）。但是，如果我们希望当前日期和时间与我们的文档相关联，我们可以删除 id 参数，并且 uuid 将使用当前日期和时间自动创建。
# Define collection name
COLLECTION_NAME = "timescale_commits"
embeddings = OpenAIEmbeddings()
# Create a Timescale Vector instance from the collection of documents
db = TimescaleVector.from_documents(
embedding=embeddings,
ids=[doc.metadata["id"] for doc in docs],
documents=docs,
collection_name=COLLECTION_NAME,
service_url=SERVICE_URL,
time_partition_interval=timedelta(days=7),
)
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性查询它们。
TimescaleVector 通过使用基于时间的过滤进行相似性搜索，提供了多种查询向量的方法。
让我们来看看下面的每种方法：
# Time filter variables
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
query = "What's new with TimescaleDB functions?"
方法 1：在提供的开始日期和结束日期内进行筛选。
# Method 1: Query for vectors between start_date and end_date
docs_with_score = db.similarity_search_with_score(
query, start_date=start_dt, end_date=end_dt
)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print("Date: ", doc.metadata["date"])
print(doc.page_content)
print("-" * 80)
--------------------------------------------------------------------------------
Score: 0.17488396167755127
Date: 2023-08-29 18:13:24+0320
{"commit": " e4facda540286b0affba47ccc63959fefe2a7b26", "author": "Sven Klemm<sven@timescale.com>", "date": "Tue Aug 29 18:13:24 2023 +0200", "change summary": "Add compatibility layer for _timescaledb_internal functions", "change details": "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. "}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.18102192878723145
Date: 2023-08-20 22:47:10+0320
{"commit": " 0a66bdb8d36a1879246bd652e4c28500c4b951ab", "author": "Sven Klemm<sven@timescale.com>", "date": "Sun Aug 20 22:47:10 2023 +0200", "change summary": "Move functions to _timescaledb_functions schema", "change details": "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: - to_unix_microseconds(timestamptz) - to_timestamp(bigint) - to_timestamp_without_timezone(bigint) - to_date(bigint) - to_interval(bigint) - interval_to_usec(interval) - time_to_internal(anyelement) - subtract_integer_from_now(regclass, bigint) "}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.18150119891755445
Date: 2023-08-22 12:01:19+0320
{"commit": " cf04496e4b4237440274eb25e4e02472fc4e06fc", "author": "Sven Klemm<sven@timescale.com>", "date": "Tue Aug 22 12:01:19 2023 +0200", "change summary": "Move utility functions to _timescaledb_functions schema", "change details": "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() "}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.18422493887617963
Date: 2023-08-9 15:26:03+0500
{"commit": " 44eab9cf9bef34274c88efd37a750eaa74cd8044", "author": "Konstantina Skovola<konstantina@timescale.com>", "date": "Wed Aug 9 15:26:03 2023 +0300", "change summary": "Release 2.11.2", "change details": "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，查询如何仅返回指定日期范围内的结果。
方法 2：在提供的开始日期内进行筛选，并在之后的某个时间增量内进行筛选。
# Method 2: Query for vectors between start_dt and a time delta td later
# Most relevant vectors between 1 August and 7 days later
docs_with_score = db.similarity_search_with_score(
query, start_date=start_dt, time_delta=td
)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print("Date: ", doc.metadata["date"])
print(doc.page_content)
print("-" * 80)
--------------------------------------------------------------------------------
Score: 0.18458807468414307
Date: 2023-08-3 14:30:23+0500
{"commit": " 7aeed663b9c0f337b530fd6cad47704a51a9b2ec", "author": "Dmitry Simonenko<dmitry@timescale.com>", "date": "Thu Aug 3 14:30:23 2023 +0300", "change summary": "Feature flags for TimescaleDB features", "change details": "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 "}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.20492422580718994
Date: 2023-08-7 18:31:40+0320
{"commit": " 07762ea4cedefc88497f0d1f8712d1515cdc5b6e", "author": "Sven Klemm<sven@timescale.com>", "date": "Mon Aug 7 18:31:40 2023 +0200", "change summary": "Test timescaledb debian 12 packages in CI", "change details": ""}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.21106326580047607
Date: 2023-08-3 14:36:39+0500
{"commit": " 2863daf3df83c63ee36c0cf7b66c522da5b4e127", "author": "Dmitry Simonenko<dmitry@timescale.com>", "date": "Thu Aug 3 14:36:39 2023 +0300", "change summary": "Support CREATE INDEX ONLY ON main table", "change details": "This PR adds support for CREATE INDEX ONLY ON clause which allows to create index only on the main table excluding chunks. Fix #5908 "}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.21698051691055298
Date: 2023-08-2 20:24:14+0140
{"commit": " 3af0d282ea71d9a8f27159a6171e9516e62ec9cb", "author": "Lakshmi Narayanan Sreethar<lakshmi@timescale.com>", "date": "Wed Aug 2 20:24:14 2023 +0100", "change summary": "PG16: ExecInsertIndexTuples requires additional parameter", "change details": "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 "}
--------------------------------------------------------------------------------
再次注意我们如何在指定的时间过滤器内获得结果，这与前面的查询不同。
方法 3：在提供的结束日期和更早的时间增量内进行筛选。
# Method 3: Query for vectors between end_dt and a time delta td earlier
# Most relevant vectors between 30 August and 7 days earlier
docs_with_score = db.similarity_search_with_score(query, end_date=end_dt, time_delta=td)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print("Date: ", doc.metadata["date"])
print(doc.page_content)
print("-" * 80)
--------------------------------------------------------------------------------
Score: 0.17488396167755127
Date: 2023-08-29 18:13:24+0320
{"commit": " e4facda540286b0affba47ccc63959fefe2a7b26", "author": "Sven Klemm<sven@timescale.com>", "date": "Tue Aug 29 18:13:24 2023 +0200", "change summary": "Add compatibility layer for _timescaledb_internal functions", "change details": "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. "}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.18496227264404297
Date: 2023-08-29 10:49:47+0320
{"commit": " a9751ccd5eb030026d7b975d22753f5964972389", "author": "Sven Klemm<sven@timescale.com>", "date": "Tue Aug 29 10:49:47 2023 +0200", "change summary": "Move partitioning functions to _timescaledb_functions schema", "change details": "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) "}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.1871250867843628
Date: 2023-08-28 23:26:23+0320
{"commit": " b2a91494a11d8b82849b6f11f9ea6dc26ef8a8cb", "author": "Sven Klemm<sven@timescale.com>", "date": "Mon Aug 28 23:26:23 2023 +0200", "change summary": "Move ddl_internal functions to _timescaledb_functions schema", "change details": "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) "}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.18867712088363497
Date: 2023-08-27 13:20:04+0320
{"commit": " e02b1f348eb4c48def00b7d5227238b4d9d41a4a", "author": "Sven Klemm<sven@timescale.com>", "date": "Sun Aug 27 13:20:04 2023 +0200", "change summary": "Simplify schema move update script", "change details": "Use dynamic sql to create the ALTER FUNCTION statements for those functions that may not exist in previous versions. "}
--------------------------------------------------------------------------------
方法 4：我们还可以通过仅在查询中指定开始日期来筛选给定日期之后的所有向量。
方法 5：同样，我们可以通过在查询中仅指定结束日期来过滤给定日期之前的向量或所有向量。
# Method 4: Query all vectors after start_date
docs_with_score = db.similarity_search_with_score(query, start_date=start_dt)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print("Date: ", doc.metadata["date"])
print(doc.page_content)
print("-" * 80)
--------------------------------------------------------------------------------
Score: 0.17488396167755127
Date: 2023-08-29 18:13:24+0320
{"commit": " e4facda540286b0affba47ccc63959fefe2a7b26", "author": "Sven Klemm<sven@timescale.com>", "date": "Tue Aug 29 18:13:24 2023 +0200", "change summary": "Add compatibility layer for _timescaledb_internal functions", "change details": "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. "}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.18102192878723145
Date: 2023-08-20 22:47:10+0320
{"commit": " 0a66bdb8d36a1879246bd652e4c28500c4b951ab", "author": "Sven Klemm<sven@timescale.com>", "date": "Sun Aug 20 22:47:10 2023 +0200", "change summary": "Move functions to _timescaledb_functions schema", "change details": "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: - to_unix_microseconds(timestamptz) - to_timestamp(bigint) - to_timestamp_without_timezone(bigint) - to_date(bigint) - to_interval(bigint) - interval_to_usec(interval) - time_to_internal(anyelement) - subtract_integer_from_now(regclass, bigint) "}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.18150119891755445
Date: 2023-08-22 12:01:19+0320
{"commit": " cf04496e4b4237440274eb25e4e02472fc4e06fc", "author": "Sven Klemm<sven@timescale.com>", "date": "Tue Aug 22 12:01:19 2023 +0200", "change summary": "Move utility functions to _timescaledb_functions schema", "change details": "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() "}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.18422493887617963
Date: 2023-08-9 15:26:03+0500
{"commit": " 44eab9cf9bef34274c88efd37a750eaa74cd8044", "author": "Konstantina Skovola<konstantina@timescale.com>", "date": "Wed Aug 9 15:26:03 2023 +0300", "change summary": "Release 2.11.2", "change details": "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 "}
--------------------------------------------------------------------------------
# Method 5: Query all vectors before end_date
docs_with_score = db.similarity_search_with_score(query, end_date=end_dt)
for doc, score in docs_with_score:
print("-" * 80)
print("Score: ", score)
print("Date: ", doc.metadata["date"])
print(doc.page_content)
print("-" * 80)
--------------------------------------------------------------------------------
Score: 0.16723191738128662
Date: 2023-04-11 22:01:14+0320
{"commit": " 0595ff0888f2ffb8d313acb0bda9642578a9ade3", "author": "Sven Klemm<sven@timescale.com>", "date": "Tue Apr 11 22:01:14 2023 +0200", "change summary": "Move type support functions into _timescaledb_functions schema", "change details": ""}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.1706540584564209
Date: 2023-04-6 13:00:00+0320
{"commit": " 04f43335dea11e9c467ee558ad8edfc00c1a45ed", "author": "Sven Klemm<sven@timescale.com>", "date": "Thu Apr 6 13:00:00 2023 +0200", "change summary": "Move aggregate support function into _timescaledb_functions", "change details": "This patch moves the support functions for histogram, first and last into the _timescaledb_functions schema. Since we alter the schema of the existing functions in upgrade scripts and do not change the aggregates this should work completely transparently for any user objects using those aggregates. "}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.17462033033370972
Date: 2023-03-31 08:22:57+0320
{"commit": " feef9206facc5c5f506661de4a81d96ef059b095", "author": "Sven Klemm<sven@timescale.com>", "date": "Fri Mar 31 08:22:57 2023 +0200", "change summary": "Add _timescaledb_functions schema", "change details": "Currently internal user objects like chunks and our functions live in the same schema making locking down that schema hard. This patch adds a new schema _timescaledb_functions that is meant to be the schema used for timescaledb internal functions to allow separation of code and chunks or other user objects. "}
--------------------------------------------------------------------------------
--------------------------------------------------------------------------------
Score: 0.17488396167755127
Date: 2023-08-29 18:13:24+0320
{"commit": " e4facda540286b0affba47ccc63959fefe2a7b26", "author": "Sven Klemm<sven@timescale.com>", "date": "Tue Aug 29 18:13:24 2023 +0200", "change summary": "Add compatibility layer for _timescaledb_internal functions", "change details": "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. "}
--------------------------------------------------------------------------------
主要收获是，在上面的每个结果中，仅返回指定时间范围内的向量。这些查询非常高效，因为它们只需要搜索相关分区。
我们还可以将此功能用于问答，我们希望在指定时间范围内找到最相关的向量，以用作回答问题的上下文。让我们看一下下面的示例，使用 Timescale Vector 作为检索器：
# Set timescale vector as a retriever and specify start and end dates via kwargs
retriever = db.as_retriever(search_kwargs={"start_date": start_dt, "end_date": end_dt})
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0.1, model="gpt-3.5-turbo-16k")
from langchain.chains import RetrievalQA
qa_stuff = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
verbose=True,
)
query = (
"What's new with the timescaledb functions? Tell me when these changes were made."
)
response = qa_stuff.run(query)
print(response)
API 参考：ChatOpenAI | 检索 QA
[1m> Entering new RetrievalQA chain...[0m
[1m> Finished chain.[0m
The following changes were made to the timescaledb functions:
1. "Add compatibility layer for _timescaledb_internal functions" - This change was made on Tue Aug 29 18:13:24 2023 +0200.
2. "Move functions to _timescaledb_functions schema" - This change was made on Sun Aug 20 22:47:10 2023 +0200.
3. "Move utility functions to _timescaledb_functions schema" - This change was made on Tue Aug 22 12:01:19 2023 +0200.
4. "Move partitioning functions to _timescaledb_functions schema" - This change was made on Tue Aug 29 10:49:47 2023 +0200.
请注意，LLM 用于撰写答案的上下文仅来自指定日期范围内检索到的文档。
这显示了如何使用 Timescale Vector 通过在与查询相关的时间范围内检索文档来增强检索增强生成。
3. 使用 ANN 搜索索引加快查询速度
您可以通过在 embedding 列上创建索引来加快相似性查询的速度。只有在摄取了大部分数据后，才应执行此作。
Timescale Vector 支持以下索引：
- timescale_vector索引 （TSV）：用于快速相似性搜索的受 disk-ann 启发的图形索引（默认）。
- pgvector 的 HNSW 索引：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，可以通过以下方式进行测试：（1） 创建具有不同索引的多个表，（2） 在同一个表中创建多个向量列并在每个列上创建不同的索引，或者 （3） 删除并重新创建同一列的索引并比较结果。
# Initialize an existing TimescaleVector store
COLLECTION_NAME = "timescale_commits"
embeddings = OpenAIEmbeddings()
db = TimescaleVector(
collection_name=COLLECTION_NAME,
service_url=SERVICE_URL,
embedding_function=embeddings,
)
使用create_index()函数将默认使用 default 参数创建 timescale_vector_index。
# create an index
# by default this will create a Timescale Vector (DiskANN) index
db.create_index()
您还可以指定索引的参数。请参阅 Timescale Vector 文档，了解不同参数及其对性能影响的完整讨论。
注意：您无需指定参数，因为我们设置了智能默认值。但是，如果您想为特定数据集试验更高的性能，您始终可以指定自己的参数。
# drop the old index
db.drop_index()
# create an index
# Note: You don't need to specify m and ef_construction parameters as we set smart defaults.
db.create_index(index_type="tsv", max_alpha=1.0, num_neighbors=50)
Timescale Vector 还支持 HNSW ANN 索引算法以及 ivfflat ANN 索引算法。只需在index_type参数，并可选择指定索引的参数。
# drop the old index
db.drop_index()
# Create an HNSW index
# Note: You don't need to specify m and ef_construction parameters as we set smart defaults.
db.create_index(index_type="hnsw", m=16, ef_construction=64)
# drop the old index
db.drop_index()
# Create an IVFFLAT index
# Note: You don't need to specify num_lists and num_records parameters as we set smart defaults.
db.create_index(index_type="ivfflat", num_lists=20, num_records=1000)
通常，我们建议使用默认的时间刻度向量索引或 HNSW 索引。
# drop the old index
db.drop_index()
# Create a new timescale vector index
db.create_index()
4. 使用 Timescale Vector 的自查询检索器
Timescale Vector 还支持自查询检索器功能，这使其能够查询自身。给定一个带有查询语句和过滤器（单个或复合）的自然语言查询，检索器使用构建 LLM 链的查询来编写 SQL 查询，然后将其应用于 Timescale Vector vectorstore 中的底层 PostgreSQL 数据库。
有关自查询的更多信息，请参阅文档。
为了说明 Timescale Vector 的自查询，我们将使用 Part 3 中相同的 gitlog 数据集。
COLLECTION_NAME = "timescale_commits"
vectorstore = TimescaleVector(
embedding_function=OpenAIEmbeddings(),
collection_name=COLLECTION_NAME,
service_url=SERVICE_URL,
)
接下来，我们将创建自查询检索器。为此，我们需要预先提供有关文档支持的元数据字段的一些信息，以及文档内容的简短描述。
from langchain.chains.query_constructor.base import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain_openai import OpenAI
# Give LLM info about the metadata fields
metadata_field_info = [
AttributeInfo(
name="id",
description="A UUID v1 generated from the date of the commit",
type="uuid",
),
AttributeInfo(
name="date",
description="The date of the commit in timestamptz format",
type="timestamptz",
),
AttributeInfo(
name="author_name",
description="The name of the author of the commit",
type="string",
),
AttributeInfo(
name="author_email",
description="The email address of the author of the commit",
type="string",
),
]
document_content_description = "The git log commit summary containing the commit hash, author, date of commit, change summary and change details"
# Instantiate the self-query retriever from an LLM
llm = OpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(
llm,
vectorstore,
document_content_description,
metadata_field_info,
enable_limit=True,
verbose=True,
)
现在让我们在 gitlog 数据集上测试自查询检索器。
运行下面的查询，并注意如何以自然语言指定查询、带过滤器的查询以及带复合过滤器的查询（带 AND、OR 的过滤器），自查询检索器会将该查询转换为 SQL，并在 Timescale Vector PostgreSQL 向量存储上执行搜索。
这说明了 self-query 检索器的强大功能。您可以使用它在 vectorstore 上执行复杂的搜索，而无需您或您的用户直接编写任何 SQL！
# This example specifies a relevant query
retriever.invoke("What are improvements made to continuous aggregates?")
/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/libs/langchain/langchain/chains/llm.py:275: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.
warnings.warn(
``````output
query='improvements to continuous aggregates' filter=None limit=None
[Document(page_content='{"commit": " 35c91204987ccb0161d745af1a39b7eb91bc65a5", "author": "Fabr\\u00edzio de Royes Mello<fabriziomello@gmail.com>", "date": "Thu Nov 24 13:19:36 2022 -0300", "change summary": "Add Hierarchical Continuous Aggregates validations", "change details": "Commit 3749953e introduce Hierarchical Continuous Aggregates (aka Continuous Aggregate on top of another Continuous Aggregate) but it lacks of some basic validations. Validations added during the creation of a Hierarchical Continuous Aggregate: * Forbid create a continuous aggregate with fixed-width bucket on top of a continuous aggregate with variable-width bucket. * Forbid incompatible bucket widths: - should not be equal; - bucket width of the new continuous aggregate should be greater than the source continuous aggregate; - bucket width of the new continuous aggregate should be multiple of the source continuous aggregate. "}', metadata={'id': 'c98d1c00-6c13-11ed-9bbe-23925ce74d13', 'date': '2022-11-24 13:19:36+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 446, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 35c91204987ccb0161d745af1a39b7eb91bc65a5', 'author_email': 'fabriziomello@gmail.com'}),
Document(page_content='{"commit": " 3749953e9704e45df8f621607989ada0714ce28d", "author": "Fabr\\u00edzio de Royes Mello<fabriziomello@gmail.com>", "date": "Wed Oct 5 18:45:40 2022 -0300", "change summary": "Hierarchical Continuous Aggregates", "change details": "Enable users create Hierarchical Continuous Aggregates (aka Continuous Aggregates on top of another Continuous Aggregates). With this PR users can create levels of aggregation granularity in Continuous Aggregates making the refresh process even faster. A problem with this feature can be in upper levels we can end up with the \\"average of averages\\". But to get the \\"real average\\" we can rely on \\"stats_aggs\\" TimescaleDB Toolkit function that calculate and store the partials that can be finalized with other toolkit functions like \\"average\\" and \\"sum\\". Closes #1400 "}', metadata={'id': '0df31a00-44f7-11ed-9794-ebcc1227340f', 'date': '2022-10-5 18:45:40+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 470, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 3749953e9704e45df8f621607989ada0714ce28d', 'author_email': 'fabriziomello@gmail.com'}),
Document(page_content='{"commit": " a6ff7ba6cc15b280a275e5acd315741ec9c86acc", "author": "Mats Kindahl<mats@timescale.com>", "date": "Tue Feb 28 12:04:17 2023 +0100", "change summary": "Rename columns in old-style continuous aggregates", "change details": "For continuous aggregates with the old-style partial aggregates renaming columns that are not in the group-by clause will generate an error when upgrading to a later version. The reason is that it is implicitly assumed that the name of the column is the same as for the direct view. This holds true for new-style continous aggregates, but is not always true for old-style continuous aggregates. In particular, columns that are not part of the `GROUP BY` clause can have an internally generated name. This commit fixes that by extracting the name of the column from the partial view and use that when renaming the partial view column and the materialized table column. "}', metadata={'id': 'a49ace80-b757-11ed-8138-2390fd44ffd9', 'date': '2023-02-28 12:04:17+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 294, 'author_name': 'Mats Kindahl', 'commit_hash': ' a6ff7ba6cc15b280a275e5acd315741ec9c86acc', 'author_email': 'mats@timescale.com'}),
Document(page_content='{"commit": " 5bba74a2ec083728f8e93e09d03d102568fd72b5", "author": "Fabr\\u00edzio de Royes Mello<fabriziomello@gmail.com>", "date": "Mon Aug 7 19:49:47 2023 -0300", "change summary": "Relax strong table lock when refreshing a CAGG", "change details": "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 "}', metadata={'id': 'b5583780-3574-11ee-a5ba-2e305874a58f', 'date': '2023-08-7 19:49:47+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 27, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 5bba74a2ec083728f8e93e09d03d102568fd72b5', 'author_email': 'fabriziomello@gmail.com'})]
# This example specifies a filter
retriever.invoke("What commits did Sven Klemm add?")
query=' ' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='author_name', value='Sven Klemm') limit=None
[Document(page_content='{"commit": " e2e7ae304521b74ac6b3f157a207da047d44ab06", "author": "Sven Klemm<sven@timescale.com>", "date": "Fri Mar 3 11:22:06 2023 +0100", "change summary": "Don\'t run sanitizer test on individual PRs", "change details": "Sanitizer tests take a long time to run so we don\'t want to run them on individual PRs but instead run them nightly and on commits to master. "}', metadata={'id': '3f401b00-b9ad-11ed-b5ea-a3fd40b9ac16', 'date': '2023-03-3 11:22:06+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 295, 'author_name': 'Sven Klemm', 'commit_hash': ' e2e7ae304521b74ac6b3f157a207da047d44ab06', 'author_email': 'sven@timescale.com'}),
Document(page_content='{"commit": " d8f19e57a04d17593df5f2c694eae8775faddbc7", "author": "Sven Klemm<sven@timescale.com>", "date": "Wed Feb 1 08:34:20 2023 +0100", "change summary": "Bump version of setup-wsl github action", "change details": "The currently used version pulls in Node.js 12 which is deprecated on github. https://github.blog/changelog/2022-09-22-github-actions-all-actions-will-begin-running-on-node16-instead-of-node12/ "}', metadata={'id': 'd70de600-a202-11ed-85d6-30b6df240f49', 'date': '2023-02-1 08:34:20+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 350, 'author_name': 'Sven Klemm', 'commit_hash': ' d8f19e57a04d17593df5f2c694eae8775faddbc7', 'author_email': 'sven@timescale.com'}),
Document(page_content='{"commit": " 83b13cf6f73a74656dde9cc6ec6cf76740cddd3c", "author": "Sven Klemm<sven@timescale.com>", "date": "Fri Nov 25 08:27:45 2022 +0100", "change summary": "Use packaged postgres for sqlsmith and coverity CI", "change details": "The sqlsmith and coverity workflows used the cache postgres build but could not produce a build by themselves and therefore relied on other workflows to produce the cached binaries. This patch changes those workflows to use normal postgres packages instead of custom built postgres to remove that dependency. "}', metadata={'id': 'a786ae80-6c92-11ed-bd6c-a57bd3348b97', 'date': '2022-11-25 08:27:45+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 447, 'author_name': 'Sven Klemm', 'commit_hash': ' 83b13cf6f73a74656dde9cc6ec6cf76740cddd3c', 'author_email': 'sven@timescale.com'}),
Document(page_content='{"commit": " b1314e63f2ff6151ab5becfb105afa3682286a4d", "author": "Sven Klemm<sven@timescale.com>", "date": "Thu Dec 22 12:03:35 2022 +0100", "change summary": "Fix RPM package test for PG15 on centos 7", "change details": "Installing PG15 on Centos 7 requires the EPEL repository to satisfy the dependencies. "}', metadata={'id': '477b1d80-81e8-11ed-9c8c-9b5abbd67c98', 'date': '2022-12-22 12:03:35+0140', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 408, 'author_name': 'Sven Klemm', 'commit_hash': ' b1314e63f2ff6151ab5becfb105afa3682286a4d', 'author_email': 'sven@timescale.com'})]
# This example specifies a query and filter
retriever.invoke("What commits about timescaledb_functions did Sven Klemm add?")
query='timescaledb_functions' filter=Comparison(comparator=<Comparator.EQ: 'eq'>, attribute='author_name', value='Sven Klemm') limit=None
[Document(page_content='{"commit": " 04f43335dea11e9c467ee558ad8edfc00c1a45ed", "author": "Sven Klemm<sven@timescale.com>", "date": "Thu Apr 6 13:00:00 2023 +0200", "change summary": "Move aggregate support function into _timescaledb_functions", "change details": "This patch moves the support functions for histogram, first and last into the _timescaledb_functions schema. Since we alter the schema of the existing functions in upgrade scripts and do not change the aggregates this should work completely transparently for any user objects using those aggregates. "}', metadata={'id': '2cb47800-d46a-11ed-8f0e-2b624245c561', 'date': '2023-04-6 13:00:00+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 233, 'author_name': 'Sven Klemm', 'commit_hash': ' 04f43335dea11e9c467ee558ad8edfc00c1a45ed', 'author_email': 'sven@timescale.com'}),
Document(page_content='{"commit": " feef9206facc5c5f506661de4a81d96ef059b095", "author": "Sven Klemm<sven@timescale.com>", "date": "Fri Mar 31 08:22:57 2023 +0200", "change summary": "Add _timescaledb_functions schema", "change details": "Currently internal user objects like chunks and our functions live in the same schema making locking down that schema hard. This patch adds a new schema _timescaledb_functions that is meant to be the schema used for timescaledb internal functions to allow separation of code and chunks or other user objects. "}', metadata={'id': '7a257680-cf8c-11ed-848c-a515e8687479', 'date': '2023-03-31 08:22:57+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 239, 'author_name': 'Sven Klemm', 'commit_hash': ' feef9206facc5c5f506661de4a81d96ef059b095', 'author_email': 'sven@timescale.com'}),
Document(page_content='{"commit": " 0a66bdb8d36a1879246bd652e4c28500c4b951ab", "author": "Sven Klemm<sven@timescale.com>", "date": "Sun Aug 20 22:47:10 2023 +0200", "change summary": "Move functions to _timescaledb_functions schema", "change details": "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: - to_unix_microseconds(timestamptz) - to_timestamp(bigint) - to_timestamp_without_timezone(bigint) - to_date(bigint) - to_interval(bigint) - interval_to_usec(interval) - time_to_internal(anyelement) - subtract_integer_from_now(regclass, bigint) "}', metadata={'id': 'bb99db00-3f9a-11ee-a8dc-0b9c1a5a37c4', 'date': '2023-08-20 22:47:10+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 41, 'author_name': 'Sven Klemm', 'commit_hash': ' 0a66bdb8d36a1879246bd652e4c28500c4b951ab', 'author_email': 'sven@timescale.com'}),
Document(page_content='{"commit": " 56ea8b4de93cefc38e002202d8ac96947dcbaa77", "author": "Sven Klemm<sven@timescale.com>", "date": "Thu Apr 13 13:16:14 2023 +0200", "change summary": "Move trigger functions to _timescaledb_functions schema", "change details": "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 our trigger functions. "}', metadata={'id': '9a255300-d9ec-11ed-988f-7086c8ca463a', 'date': '2023-04-13 13:16:14+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 44, 'author_name': 'Sven Klemm', 'commit_hash': ' 56ea8b4de93cefc38e002202d8ac96947dcbaa77', 'author_email': 'sven@timescale.com'})]
# This example specifies a time-based filter
retriever.invoke("What commits were added in July 2023?")
query=' ' filter=Operation(operator=<Operator.AND: 'and'>, arguments=[Comparison(comparator=<Comparator.GTE: 'gte'>, attribute='date', value='2023-07-01T00:00:00Z'), Comparison(comparator=<Comparator.LTE: 'lte'>, attribute='date', value='2023-07-31T23:59:59Z')]) limit=None
[Document(page_content='{"commit": " 5cf354e2469ee7e43248bed382a4b49fc7ccfecd", "author": "Markus Engel<engel@sero-systems.de>", "date": "Mon Jul 31 11:28:25 2023 +0200", "change summary": "Fix quoting owners in sql scripts.", "change details": "When referring to a role from a string type, it must be properly quoted using pg_catalog.quote_ident before it can be casted to regrole. Fixed this, especially in update scripts. "}', metadata={'id': '99590280-2f84-11ee-915b-5715b2447de4', 'date': '2023-07-31 11:28:25+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 76, 'author_name': 'Markus Engel', 'commit_hash': ' 5cf354e2469ee7e43248bed382a4b49fc7ccfecd', 'author_email': 'engel@sero-systems.de'}),
Document(page_content='{"commit": " 88aaf23ae37fe7f47252b87325eb570aa417c607", "author": "noctarius aka Christoph Engelbert<me@noctarius.com>", "date": "Wed Jul 12 14:53:40 2023 +0200", "change summary": "Allow Replica Identity (Alter Table) on CAGGs (#5868)", "change details": "This commit is a follow up of #5515, which added support for ALTER TABLE\\r ... REPLICA IDENTITY (FULL | INDEX) on hypertables.\\r \\r This commit allows the execution against materialized hypertables to\\r enable update / delete operations on continuous aggregates when logical\\r replication in enabled for them."}', metadata={'id': '1fcfa200-20b3-11ee-9a18-370561c7cb1a', 'date': '2023-07-12 14:53:40+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 96, 'author_name': 'noctarius aka Christoph Engelbert', 'commit_hash': ' 88aaf23ae37fe7f47252b87325eb570aa417c607', 'author_email': 'me@noctarius.com'}),
Document(page_content='{"commit": " d5268c36fbd23fa2a93c0371998286e8688247bb", "author": "Alexander Kuzmenkov<36882414+akuzm@users.noreply.github.com>", "date": "Fri Jul 28 13:35:05 2023 +0200", "change summary": "Fix SQLSmith workflow", "change details": "The build was failing because it was picking up the wrong version of Postgres. Remove it. "}', metadata={'id': 'cc0fba80-2d3a-11ee-ae7d-36dc25cad3b8', 'date': '2023-07-28 13:35:05+0320', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 82, 'author_name': 'Alexander Kuzmenkov', 'commit_hash': ' d5268c36fbd23fa2a93c0371998286e8688247bb', 'author_email': '36882414+akuzm@users.noreply.github.com'}),
Document(page_content='{"commit": " 61c288ec5eb966a9b4d8ed90cd026ffc5e3543c9", "author": "Lakshmi Narayanan Sreethar<lakshmi@timescale.com>", "date": "Tue Jul 25 16:11:35 2023 +0530", "change summary": "Fix broken CI after PG12 removal", "change details": "The commit cdea343cc updated the gh_matrix_builder.py script but failed to import PG_LATEST variable into the script thus breaking the CI. Import that variable to fix the CI tests. "}', metadata={'id': 'd3835980-2ad7-11ee-b98d-c4e3092e076e', 'date': '2023-07-25 16:11:35+0850', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 84, 'author_name': 'Lakshmi Narayanan Sreethar', 'commit_hash': ' 61c288ec5eb966a9b4d8ed90cd026ffc5e3543c9', 'author_email': 'lakshmi@timescale.com'})]
# This example specifies a query and a LIMIT value
retriever.invoke("What are two commits about hierarchical continuous aggregates?")
query='hierarchical continuous aggregates' filter=None limit=2
[Document(page_content='{"commit": " 35c91204987ccb0161d745af1a39b7eb91bc65a5", "author": "Fabr\\u00edzio de Royes Mello<fabriziomello@gmail.com>", "date": "Thu Nov 24 13:19:36 2022 -0300", "change summary": "Add Hierarchical Continuous Aggregates validations", "change details": "Commit 3749953e introduce Hierarchical Continuous Aggregates (aka Continuous Aggregate on top of another Continuous Aggregate) but it lacks of some basic validations. Validations added during the creation of a Hierarchical Continuous Aggregate: * Forbid create a continuous aggregate with fixed-width bucket on top of a continuous aggregate with variable-width bucket. * Forbid incompatible bucket widths: - should not be equal; - bucket width of the new continuous aggregate should be greater than the source continuous aggregate; - bucket width of the new continuous aggregate should be multiple of the source continuous aggregate. "}', metadata={'id': 'c98d1c00-6c13-11ed-9bbe-23925ce74d13', 'date': '2022-11-24 13:19:36+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 446, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 35c91204987ccb0161d745af1a39b7eb91bc65a5', 'author_email': 'fabriziomello@gmail.com'}),
Document(page_content='{"commit": " 3749953e9704e45df8f621607989ada0714ce28d", "author": "Fabr\\u00edzio de Royes Mello<fabriziomello@gmail.com>", "date": "Wed Oct 5 18:45:40 2022 -0300", "change summary": "Hierarchical Continuous Aggregates", "change details": "Enable users create Hierarchical Continuous Aggregates (aka Continuous Aggregates on top of another Continuous Aggregates). With this PR users can create levels of aggregation granularity in Continuous Aggregates making the refresh process even faster. A problem with this feature can be in upper levels we can end up with the \\"average of averages\\". But to get the \\"real average\\" we can rely on \\"stats_aggs\\" TimescaleDB Toolkit function that calculate and store the partials that can be finalized with other toolkit functions like \\"average\\" and \\"sum\\". Closes #1400 "}', metadata={'id': '0df31a00-44f7-11ed-9794-ebcc1227340f', 'date': '2022-10-5 18:45:40+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 470, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 3749953e9704e45df8f621607989ada0714ce28d', 'author_email': 'fabriziomello@gmail.com'})]
5. 使用现有的 TimescaleVector vectorstore
在上面的示例中，我们从文档集合创建了一个 vectorstore。但是，我们通常希望将数据插入到现有 vectorstore 中并从中查询数据。让我们看看如何在 TimescaleVector 向量存储中初始化、添加文档和查询现有文档集合。
要使用现有的 Timescale Vector 存储，我们需要知道要查询的表的名称 （COLLECTION_NAME） 和云 PostgreSQL 数据库的 URL （SERVICE_URL).
# Initialize the existing
COLLECTION_NAME = "timescale_commits"
embeddings = OpenAIEmbeddings()
vectorstore = TimescaleVector(
collection_name=COLLECTION_NAME,
service_url=SERVICE_URL,
embedding_function=embeddings,
)
要将新数据加载到表中，我们使用add_document()功能。此函数采用文档列表和元数据列表。元数据必须包含每个文档的唯一 ID。
如果您希望文档与当前日期和时间关联，则无需创建 ID 列表。将为每个文档自动生成一个 uuid。
如果您希望文档与过去的日期和时间相关联，则可以使用uuid_from_time函数中的timecale-vectorpython 库，如上面的第 2 节所示。此函数采用 datetime 对象并返回一个 uuid，其中日期和时间编码在 uuid 中。
# Add documents to a collection in TimescaleVector
ids = vectorstore.add_documents([Document(page_content="foo")])
ids
['a34f2b8a-53d7-11ee-8cc3-de1e4b2a0118']
# Query the vectorstore for similar documents
docs_with_score = vectorstore.similarity_search_with_score("foo")
docs_with_score[0]
(Document(page_content='foo', metadata={}), 5.006789860928507e-06)
docs_with_score[1]
(Document(page_content='{"commit": " 00b566dfe478c11134bcf1e7bcf38943e7fafe8f", "author": "Fabr\\u00edzio de Royes Mello<fabriziomello@gmail.com>", "date": "Mon Mar 6 15:51:03 2023 -0300", "change summary": "Remove unused functions", "change details": "We don\'t use `ts_catalog_delete[_only]` functions anywhere and instead we rely on `ts_catalog_delete_tid[_only]` functions so removing it from our code base. "}', metadata={'id': 'd7f5c580-bc4f-11ed-9712-ffa0126a201a', 'date': '2023-03-6 15:51:03+-500', 'source': '/Users/avtharsewrathan/sideprojects2023/timescaleai/tsv-langchain/langchain/docs/docs/modules/ts_git_log.json', 'seq_num': 285, 'author_name': 'Fabrízio de Royes Mello', 'commit_hash': ' 00b566dfe478c11134bcf1e7bcf38943e7fafe8f', 'author_email': 'fabriziomello@gmail.com'}),
0.23607668446580354)
删除数据
您可以通过 uuid 或元数据的筛选条件删除数据。
ids = vectorstore.add_documents([Document(page_content="Bar")])
vectorstore.delete(ids)
True
如果要定期更新从特定源、特定日期或某些其他元数据属性抓取的信息，则使用元数据删除特别有用。
vectorstore.add_documents(
[Document(page_content="Hello World", metadata={"source": "www.example.com/hello"})]
)
vectorstore.add_documents(
[Document(page_content="Adios", metadata={"source": "www.example.com/adios"})]
)
vectorstore.delete_by_metadata({"source": "www.example.com/adios"})
vectorstore.add_documents(
[
Document(
page_content="Adios, but newer!",
metadata={"source": "www.example.com/adios"},
)
]
)
['c6367004-53d7-11ee-8cc3-de1e4b2a0118']
重写 vectorstore
如果您已有集合，则通过执行from_documents和设置pre_delete_collection= 真
db = TimescaleVector.from_documents(
documents=docs,
embedding=embeddings,
collection_name=COLLECTION_NAME,
service_url=SERVICE_URL,
pre_delete_collection=True,
)
docs_with_score = db.similarity_search_with_score("foo")
docs_with_score[0]