LanceDB 向量数据库¶

本笔记本将演示如何在 LlamaIndex 中使用 LanceDB 执行向量搜索

如果您在 Colab 上打开此 Notebook，可能需要安装 LlamaIndex 🦙。

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%pip install llama-index llama-index-vector-stores-lancedb

%pip install llama-index llama-index-vector-stores-lancedb

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%pip install lancedb==0.6.13 #Only required if the above cell installs an older version of lancedb (pypi package may not be released yet)

%pip install lancedb==0.6.13 #Only required if the above cell installs an older version of lancedb (pypi package may not be released yet)

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# Refresh vector store URI if restarting or re-using the same notebook
! rm -rf ./lancedb

# Refresh vector store URI if restarting or re-using the same notebook ! rm -rf ./lancedb

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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))


from llama_index.core import SimpleDirectoryReader, Document, StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.lancedb import LanceDBVectorStore
import textwrap

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)) from llama_index.core import SimpleDirectoryReader, Document, StorageContext from llama_index.core import VectorStoreIndex from llama_index.vector_stores.lancedb import LanceDBVectorStore import textwrap

配置 OpenAI¶

第一步是设置 OpenAI 密钥。该密钥将用于为载入索引的文档创建嵌入向量。

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import openai

openai.api_key = "sk-"

import openai openai.api_key = "sk-"

下载数据

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

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

--2024-06-11 16:42:37--  https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt
Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.110.133, 185.199.108.133, ...
Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 75042 (73K) [text/plain]
Saving to: ‘data/paul_graham/paul_graham_essay.txt’

data/paul_graham/pa 100%[===================>]  73.28K  --.-KB/s    in 0.02s   

2024-06-11 16:42:37 (3.97 MB/s) - ‘data/paul_graham/paul_graham_essay.txt’ saved [75042/75042]

加载文档¶

使用 SimpleDirectoryReader 加载存储在 data/paul_graham/ 路径下的文档

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documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
print("Document ID:", documents[0].doc_id, "Document Hash:", documents[0].hash)

documents = SimpleDirectoryReader("./data/paul_graham/").load_data() print("Document ID:", documents[0].doc_id, "Document Hash:", documents[0].hash)

Document ID: cac1ba78-5007-4cf8-89ba-280264790115 Document Hash: fe2d4d3ef3a860780f6c2599808caa587c8be6516fe0ba4ca53cf117044ba953

创建索引¶

此处我们基于先前加载的文档，创建一个由 LanceDB 支持的索引。LanceDBVectorStore 接收以下参数：

• uri (str, 必填): LanceDB 存储文件的路径位置

• table_name (str, 可选): 用于存储嵌入向量的表名称，默认为 "vectors"

• nprobes (int, 可选): 使用的探测数量。数值越高搜索精度越高但速度越慢，默认为 20

• refine_factor (int, 可选): 通过读取额外元素并在内存中重新排序来优化结果，默认为 None

• 更多细节详见 LanceDB 文档

针对 LanceDB 云服务：¶

vector_store = LanceDBVectorStore( 
    uri="db://db_name", # 您的远程数据库URI
    api_key="sk_..", # lancedb云服务API密钥
    region="your-region" # 您配置的区域
    ...
)

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vector_store = LanceDBVectorStore(
    uri="./lancedb", mode="overwrite", query_type="hybrid"
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)

index = VectorStoreIndex.from_documents(
    documents, storage_context=storage_context
)

vector_store = LanceDBVectorStore( uri="./lancedb", mode="overwrite", query_type="hybrid" ) storage_context = StorageContext.from_defaults(vector_store=vector_store) index = VectorStoreIndex.from_documents( documents, storage_context=storage_context )

查询索引¶

现在我们可以使用索引进行提问查询。既可通过 MetadataFilters 实现过滤功能，也可使用原生的 lance where 子句。

In [ ]:

from llama_index.core.vector_stores import (
    MetadataFilters,
    FilterOperator,
    FilterCondition,
    MetadataFilter,
)

from datetime import datetime


query_filters = MetadataFilters(
    filters=[
        MetadataFilter(
            key="creation_date",
            operator=FilterOperator.EQ,
            value=datetime.now().strftime("%Y-%m-%d"),
        ),
        MetadataFilter(
            key="file_size", value=75040, operator=FilterOperator.GT
        ),
    ],
    condition=FilterCondition.AND,
)

from llama_index.core.vector_stores import ( MetadataFilters, FilterOperator, FilterCondition, MetadataFilter, ) from datetime import datetime query_filters = MetadataFilters( filters=[ MetadataFilter( key="creation_date", operator=FilterOperator.EQ, value=datetime.now().strftime("%Y-%m-%d"), ), MetadataFilter( key="file_size", value=75040, operator=FilterOperator.GT ), ], condition=FilterCondition.AND, )

混合搜索¶

LanceDB 提供具备重排序能力的混合搜索功能。完整文档请参阅此处。

本示例使用 colbert 重排序器。以下代码单元将安装 colbert 所需的依赖项。若选择其他重排序器，请确保相应调整依赖项。

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! pip install -U torch transformers tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985

! pip install -U torch transformers tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985

若要在向量存储初始化时添加重排序器，可通过如下参数传入：

from lancedb.rerankers import ColbertReranker
reranker = ColbertReranker()
vector_store = LanceDBVectorStore(uri="./lancedb", reranker=reranker, mode="overwrite")

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import lancedb

import lancedb

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from lancedb.rerankers import ColbertReranker

reranker = ColbertReranker()
vector_store._add_reranker(reranker)

query_engine = index.as_query_engine(
    filters=query_filters,
    # vector_store_kwargs={
    #     "query_type": "fts",
    # },
)

response = query_engine.query("How much did Viaweb charge per month?")

from lancedb.rerankers import ColbertReranker reranker = ColbertReranker() vector_store._add_reranker(reranker) query_engine = index.as_query_engine( filters=query_filters, # vector_store_kwargs={ # "query_type": "fts", # }, ) response = query_engine.query("How much did Viaweb charge per month?")

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print(response)
print("metadata -", response.metadata)

print(response) print("metadata -", response.metadata)

Viaweb charged $100 a month for a small store and $300 a month for a big one.
metadata - {'65ed5f07-5b8a-4143-a939-e8764884828e': {'file_path': '/Users/raghavdixit/Desktop/open_source/llama_index_lance/docs/docs/examples/vector_stores/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2024-06-11', 'last_modified_date': '2024-06-11'}, 'be231827-20b8-4988-ac75-94fa79b3c22e': {'file_path': '/Users/raghavdixit/Desktop/open_source/llama_index_lance/docs/docs/examples/vector_stores/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2024-06-11', 'last_modified_date': '2024-06-11'}}

通过 where 子句直接使用类 SQL 的 lance 过滤器：¶

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lance_filter = "metadata.file_name = 'paul_graham_essay.txt' "
retriever = index.as_retriever(vector_store_kwargs={"where": lance_filter})
response = retriever.retrieve("What did the author do growing up?")

lance_filter = "metadata.file_name = 'paul_graham_essay.txt' " retriever = index.as_retriever(vector_store_kwargs={"where": lance_filter}) response = retriever.retrieve("What did the author do growing up?")

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print(response[0].get_content())
print("metadata -", response[0].metadata)

print(response[0].get_content()) print("metadata -", response[0].metadata)

What I Worked On

February 2021

Before college the two main things I worked on, outside of school, were writing and programming. I didn't write essays. I wrote what beginning writers were supposed to write then, and probably still are: short stories. My stories were awful. They had hardly any plot, just characters with strong feelings, which I imagined made them deep.

The first programs I tried writing were on the IBM 1401 that our school district used for what was then called "data processing." This was in 9th grade, so I was 13 or 14. The school district's 1401 happened to be in the basement of our junior high school, and my friend Rich Draves and I got permission to use it. It was like a mini Bond villain's lair down there, with all these alien-looking machines — CPU, disk drives, printer, card reader — sitting up on a raised floor under bright fluorescent lights.

The language we used was an early version of Fortran. You had to type programs on punch cards, then stack them in the card reader and press a button to load the program into memory and run it. The result would ordinarily be to print something on the spectacularly loud printer.

I was puzzled by the 1401. I couldn't figure out what to do with it. And in retrospect there's not much I could have done with it. The only form of input to programs was data stored on punched cards, and I didn't have any data stored on punched cards. The only other option was to do things that didn't rely on any input, like calculate approximations of pi, but I didn't know enough math to do anything interesting of that type. So I'm not surprised I can't remember any programs I wrote, because they can't have done much. My clearest memory is of the moment I learned it was possible for programs not to terminate, when one of mine didn't. On a machine without time-sharing, this was a social as well as a technical error, as the data center manager's expression made clear.

With microcomputers, everything changed. Now you could have a computer sitting right in front of you, on a desk, that could respond to your keystrokes as it was running instead of just churning through a stack of punch cards and then stopping. [1]

The first of my friends to get a microcomputer built it himself. It was sold as a kit by Heathkit. I remember vividly how impressed and envious I felt watching him sitting in front of it, typing programs right into the computer.

Computers were expensive in those days and it took me years of nagging before I convinced my father to buy one, a TRS-80, in about 1980. The gold standard then was the Apple II, but a TRS-80 was good enough. This was when I really started programming. I wrote simple games, a program to predict how high my model rockets would fly, and a word processor that my father used to write at least one book. There was only room in memory for about 2 pages of text, so he'd write 2 pages at a time and then print them out, but it was a lot better than a typewriter.

Though I liked programming, I didn't plan to study it in college. In college I was going to study philosophy, which sounded much more powerful. It seemed, to my naive high school self, to be the study of the ultimate truths, compared to which the things studied in other fields would be mere domain knowledge. What I discovered when I got to college was that the other fields took up so much of the space of ideas that there wasn't much left for these supposed ultimate truths. All that seemed left for philosophy were edge cases that people in other fields felt could safely be ignored.

I couldn't have put this into words when I was 18. All I knew at the time was that I kept taking philosophy courses and they kept being boring. So I decided to switch to AI.

AI was in the air in the mid 1980s, but there were two things especially that made me want to work on it: a novel by Heinlein called The Moon is a Harsh Mistress, which featured an intelligent computer called Mike, and a PBS documentary that showed Terry Winograd using SHRDLU. I haven't tried rereading The Moon is a Harsh Mistress, so I don't know how well it has aged, but when I read it I was drawn entirely into its world.
metadata - {'file_path': '/Users/raghavdixit/Desktop/open_source/llama_index_lance/docs/docs/examples/vector_stores/data/paul_graham/paul_graham_essay.txt', 'file_name': 'paul_graham_essay.txt', 'file_type': 'text/plain', 'file_size': 75042, 'creation_date': '2024-06-11', 'last_modified_date': '2024-06-11'}

追加数据¶

您还可以向现有索引中添加数据

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nodes = [node.node for node in response]

nodes = [node.node for node in response]

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del index

index = VectorStoreIndex.from_documents(
    [Document(text="The sky is purple in Portland, Maine")],
    uri="/tmp/new_dataset",
)

del index index = VectorStoreIndex.from_documents( [Document(text="The sky is purple in Portland, Maine")], uri="/tmp/new_dataset", )

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index.insert_nodes(nodes)

index.insert_nodes(nodes)

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query_engine = index.as_query_engine()
response = query_engine.query("Where is the sky purple?")
print(textwrap.fill(str(response), 100))

query_engine = index.as_query_engine() response = query_engine.query("Where is the sky purple?") print(textwrap.fill(str(response), 100))

Portland, Maine

您也可以基于现有表创建索引

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del index

vec_store = LanceDBVectorStore.from_table(vector_store._table)
index = VectorStoreIndex.from_vector_store(vec_store)

del index vec_store = LanceDBVectorStore.from_table(vector_store._table) index = VectorStoreIndex.from_vector_store(vec_store)

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query_engine = index.as_query_engine()
response = query_engine.query("What companies did the author start?")
print(textwrap.fill(str(response), 100))

query_engine = index.as_query_engine() response = query_engine.query("What companies did the author start?") print(textwrap.fill(str(response), 100))

The author started Viaweb and Aspra.