TXTAI 6.0带来了许多主要功能增强功能。亮点包括：
-
嵌入
- 稀疏/关键字索引
- 混合搜索
- Subindexes
- 精简方法
-
大语言模型（LLMS）
- 自动实例化最佳的基础模型
- 通过参数可以立即支持，因为在上游释放功能
这些只是很大的高水平变化。也有许多改进和错误修复。
本笔记本将用示例涵盖所有更改。
标准升级免责声明
6.0是迄今为止最大的，即使不是最大的发行版之一！虽然几乎所有内容都向后兼容，但在部署之前升级和测试之前，备份生产索引是谨慎的。
安装依赖项
安装txtai和所有依赖项。
# Install txtai
pip install txtai datasets
稀疏索引
虽然密集矢量索引是迄今为止语义搜索系统的最佳选择，但稀疏关键字索引仍然可以添加值。在某些情况下，找到确切匹配很重要，或者我们只希望快速索引快速进行数据集的初始扫描。
不幸的是，对于本地基于Python的关键字索引库，没有很多很棒的选择。大多数可用的选项都不扩展，并且效率很低，仅专为简单情况而设计。 TXTAI使用6.0，在与Apache Lucene相当的情况下添加了具有速度和准确性的性能稀疏索引组件。未来的文章将讨论此背后的工程。
让我们看看。我们将使用prompt dataset on the Hugging Face Hub用于所有示例。
from datasets import load_dataset
import txtai
# Load dataset
ds = load_dataset("fka/awesome-chatgpt-prompts", split="train")
def stream():
for row in ds:
yield f"{row['act']} {row['prompt']}"
# Build sparse keyword index
embeddings = txtai.Embeddings(keyword=True, content=True)
embeddings.index(stream())
embeddings.search("Linux terminal", 1)
[{'id': '0',
'text': 'Linux Terminal I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd',
'score': 0.5932681465337526}]
，它是一个关键字索引！
几件事要在这里打开包装。首先，对于那些与TXTAI的人，请注意，stream方法中仅产生了文本字段。使用6.0，当没有提供ID时，它们会自动生成。
下一个通知分数。那些与关键字分数（TF-IDF，BM25）相关的人会注意到该分数似乎很低。那是因为使用关键字索引，默认分数在0和1之间归一化。
。稍后再到这些项目。
混合搜索
稀疏索引的添加可以启用混合搜索。混合搜索结合了稀疏和密集的矢量指数的结果。
# Build hybrid index
embeddings = txtai.Embeddings(hybrid=True, content=True)
embeddings.index(stream())
embeddings.search("Linux terminal", 1)
[{'id': '0',
'text': 'Linux Terminal I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd',
'score': 0.6078515601252442}]
简单的变化，影响很大。现在，该新索引具有稀疏和密集（使用默认的sentence-transformers/all-MiniLM-L6-v2模型）索引。这些分数合并为单个分数，如上所述。
评分权重（也称为alpha）控制稀疏和致密索引之间的加权。
embeddings.search("Linux terminal", 1, weights=1)
[{'id': '0',
'text': 'Linux Terminal I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd',
'score': 0.6224349737167358}]
embeddings.search("Linux terminal", 1, weights=0)
[{'id': '0',
'text': 'Linux Terminal I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd',
'score': 0.5932681465337526}]
1的重量仅使用密集索引，而0仅使用稀疏索引。请注意，使用weight = 0的分数与之前的稀疏索引查询相同。
子索引
虽然稀疏和混合索引是很棒的新功能，但该版本的奖励是添加子索引。子索引将添加许多新方法来构建TXTAI嵌入实例。让我们在这里简要介绍。
# Build index with subindexes
embeddings = txtai.Embeddings(
content=True,
defaults=False,
indexes={
"sparse": {
"keyword": True
},
"dense": {
}
}
)
embeddings.index(stream())
# Run search
embeddings.search("select id, text, score from txtai where similar('Linux terminal', 'sparse') and similar('Linux terminal', 'dense')", 1)
[{'id': '0',
'text': 'Linux Terminal I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd',
'score': 0.6078515601252442}]
embeddings.search("select id, text, score from txtai where similar('Linux terminal', 'dense')", 1)
[{'id': '0',
'text': 'Linux Terminal I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd',
'score': 0.6224349737167358}]
embeddings.search("select id, text, score from txtai where similar('Linux terminal', 'sparse')", 1)
[{'id': '0',
'text': 'Linux Terminal I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd',
'score': 0.5932681465337526}]
请注意分数与上面的相同。上面的三个搜索进行了混合搜索，密集和稀疏的搜索。这次虽然使用子指数。顶级嵌入仅具有关联的数据库。
indexes中的每个部分都是一个完整的嵌入式索引，支持所有可用选项。例如，让我们添加图形子指数。
# Build index with graph subindex
embeddings = txtai.Embeddings(
content=True,
defaults=False,
functions=[
{"name": "graph", "function": "indexes.act.graph.attribute"}
],
expressions=[
{"name": "topic", "expression": "graph(indexid, 'topic')"},
],
indexes={
"act": {
"keyword": True,
"columns": {
"text": "act"
},
"graph": {
"topics": {}
}
},
"prompt":{
"columns": {
"text": "prompt"
}
}
}
)
embeddings.index(ds)
# Run search
embeddings.search("select id, act, prompt, score, topic from txtai where similar('Linux terminal')", 1)
[{'id': '0',
'act': 'Linux Terminal',
'prompt': 'I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd',
'score': 0.6382951796072414,
'topic': 'terminal_linux_sql'}]
请注意，在此查询中添加了新的topic字段。这是来自运行主题建模的图形索引。另请注意，添加了两个不同列的两个索引。
请注意，图索引不同，因为它们取决于可用的稀疏或致密索引。这就是图形自动构造的方式。为了良好的措施，让我们将图形添加到密集的索引中。
# Build index with graph subindex
embeddings = txtai.Embeddings(
content=True,
defaults=False,
functions=[
{"name": "graph", "function": "indexes.act.graph.attribute"}
],
expressions=[
{"name": "topic", "expression": "graph(indexid, 'topic')"},
],
indexes={
"act": {
"path": "intfloat/e5-small-v2",
"columns": {
"text": "act"
},
"graph": {
"topics": {}
}
},
"prompt":{
"path": "sentence-transformers/all-MiniLM-L6-v2",
"columns": {
"text": "prompt"
}
}
}
)
embeddings.index(ds)
# Run search
embeddings.search("select id, act, prompt, score, topic from txtai where similar('Linux terminal')", 1)
[{'id': '0',
'act': 'Linux Terminal',
'prompt': 'I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd',
'score': 1.0,
'topic': 'linux_terminal'}]
除了主题不同，
几乎相同。这是由于向量指数的分组。请注意，act列和prompt列是向量索引，但指定不同的向量模型。这打开了加权不仅稀疏与矢量的另一种可能性，而且开辟了不同的向量模型。
embeddings.search("select id, act, prompt, score from txtai where similar('Linux terminal', 'act') and similar('Linux terminal', 'prompt')", 1)
[{'id': '0',
'act': 'Linux Terminal',
'prompt': 'I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd',
'score': 0.7881423830986023}]
与往常一样，到目前为止讨论的一切也得到了TXTAI应用程序实例的支持。
# Build index with graph subindex
app = txtai.Application("""
writable: True
embeddings:
content: True
defaults: False
functions:
- name: graph
function: indexes.act.graph.attribute
expressions:
- name: topic
expression: graph(indexid, 'topic')
indexes:
act:
path: intfloat/e5-small-v2
columns:
text: act
graph:
topics:
prompt:
path: sentence-transformers/all-MiniLM-L6-v2
columns:
text: prompt
""")
app.add(ds)
app.index()
app.search("select id, act, prompt, topic, score from txtai where similar('Linux terminal', 'act') and similar('Linux terminal', 'prompt')", 1)
[{'id': '0',
'act': 'Linux Terminal',
'prompt': 'I want you to act as a linux terminal. I will type commands and you will reply with what the terminal should show. I want you to only reply with the terminal output inside one unique code block, and nothing else. do not write explanations. do not type commands unless I instruct you to do so. when i need to tell you something in english, i will do so by putting text inside curly brackets {like this}. my first command is pwd',
'topic': 'linux_terminal',
'score': 0.7881423830986023}]
简化的方法
已经涵盖了其中的大部分内容，但是添加了许多更改以使其更容易搜索和索引数据。现有的接口都得到了支持，这都是关于易于使用的。
请参阅下面的代码说明。
# Top-level import includes Application and Embeddings
import txtai
app = txtai.Application("""writable: False""")
embeddings = txtai.Embeddings()
# Ids are automatically generated when omitted
embeddings.index(["test"])
print(embeddings.search("test"))
# UUID ids are also supported - use any of the methods in https://docs.python.org/3/library/uuid.html
embeddings = txtai.Embeddings(autoid="uuid5")
embeddings.index(["test"])
embeddings.search("test")
[(0, 0.9999998807907104)]
[('4be0643f-1d98-573b-97cd-ca98a65347dd', 0.9999998807907104)]
大语言模型（LLM）
虽然本发行版的大部分更改都带有嵌入式软件包，但LLMS也具有重要的更改，使其更容易使用。
import torch
from txtai.pipeline import LLM
# Create model and set dtype to use 16-bit floats
llm = LLM("tiiuae/falcon-rw-1b", torch_dtype=torch.bfloat16)
print(llm("Write a short list of things to do in Paris", maxlength=55))
- Visit the Eiffel Tower.
- Visit the Louvre.
- Visit the Arc de Triomphe.
- Visit the Notre Dame Cathedral.
- Visit the Sacre Coeur Basilica.
新的LLM管道自动检测模型的类型并使用最佳可用方法加载。
现在，管道框架通过关键字参数到基础方法，该方法在发布时自动增加了对新的拥抱面部功能的支持。
包起来
本笔记本给TXTAI 6.0提供了快速概述。更新的文档和更多示例将即将到来。有很多要覆盖的东西，可以建立很多！
有关更多信息，请参见以下链接。