本指南展示如何在 LlamaIndex 中使用 Vectara 执行自动检索。

通过自动检索，我们在将检索查询提交给 Vectara 之前对其进行解释，以识别可能的查询重写（将其改写为更短的查询）以及一些元数据过滤。

例如，像“2022 年的收入是多少”这样的查询可能会被重写为“收入是多少”，并附带“doc.year = 2022”的过滤条件。让我们通过一个示例来看看它是如何工作的。

设置

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

!pip install llama_index llama-index-llms-openai llama-index-indices-managed-vectara

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import logging
import sys

logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))

from llama_index.core.schema import TextNode
from llama_index.core.indices.managed.types import ManagedIndexQueryMode
from llama_index.indices.managed.vectara import VectaraIndex
from llama_index.indices.managed.vectara import VectaraAutoRetriever

from llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo

from llama_index.llms.openai import OpenAI

定义一些示例数据

我们首先定义一个电影数据集：

1. 每个节点描述一部电影。
2. text 描述电影，而 metadata 定义某些元数据字段，如年份、导演、评分或类型。

在 Vectara 中，你需要在语料库中将这些元数据字段定义为可过滤属性，以便可以使用它们进行过滤。

nodes = [
    TextNode(
        text=(
            "A pragmatic paleontologist touring an almost complete theme park on an island "
            + "in Central America is tasked with protecting a couple of kids after a power "
            + "failure causes the park's cloned dinosaurs to run loose."
        ),
        metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},
    ),
    TextNode(
        text=(
            "A thief who steals corporate secrets through the use of dream-sharing technology "
            + "is given the inverse task of planting an idea into the mind of a C.E.O., "
            + "but his tragic past may doom the project and his team to disaster."
        ),
        metadata={
            "year": 2010,
            "director": "Christopher Nolan",
            "rating": 8.2,
        },
    ),
    TextNode(
        text="Barbie suffers a crisis that leads her to question her world and her existence.",
        metadata={
            "year": 2023,
            "director": "Greta Gerwig",
            "genre": "fantasy",
            "rating": 9.5,
        },
    ),
    TextNode(
        text=(
            "A cowboy doll is profoundly threatened and jealous when a new spaceman action "
            + "figure supplants him as top toy in a boy's bedroom."
        ),
        metadata={"year": 1995, "genre": "animated", "rating": 8.3},
    ),
    TextNode(
        text=(
            "When Woody is stolen by a toy collector, Buzz and his friends set out on a "
            + "rescue mission to save Woody before he becomes a museum toy property with his "
            + "roundup gang Jessie, Prospector, and Bullseye. "
        ),
        metadata={"year": 1999, "genre": "animated", "rating": 7.9},
    ),
    TextNode(
        text=(
            "The toys are mistakenly delivered to a day-care center instead of the attic "
            + "right before Andy leaves for college, and it's up to Woody to convince the "
            + "other toys that they weren't abandoned and to return home."
        ),
        metadata={"year": 2010, "genre": "animated", "rating": 8.3},
    ),
]

然后，我们将示例数据加载到 Vectara 索引中。

import os

os.environ["VECTARA_API_KEY"] = "<YOUR_VECTARA_API_KEY>"
os.environ["VECTARA_CORPUS_ID"] = "<YOUR_VECTARA_CORPUS_ID>"
os.environ["VECTARA_CUSTOMER_ID"] = "<YOUR_VECTARA_CUSTOMER_ID>"

index = VectaraIndex(nodes=nodes)

定义 VectorStoreInfo

我们定义一个 VectorStoreInfo 对象，其中包含 Vectara 索引支持的元数据过滤器的结构化描述。该信息随后用于自动检索提示词中，使 LLM 能够推断出特定查询应使用的元数据过滤器。

vector_store_info = VectorStoreInfo(
    content_info="information about a movie",
    metadata_info=[
        MetadataInfo(
            name="genre",
            description="""
                The genre of the movie.
                One of ['science fiction', 'fantasy', 'comedy', 'drama', 'thriller', 'romance', 'action', 'animated']
            """,
            type="string",
        ),
        MetadataInfo(
            name="year",
            description="The year the movie was released",
            type="integer",
        ),
        MetadataInfo(
            name="director",
            description="The name of the movie director",
            type="string",
        ),
        MetadataInfo(
            name="rating",
            description="A 1-10 rating for the movie",
            type="float",
        ),
    ],
)

运行自动检索

现在让我们创建一个 VectaraAutoRetriever 实例并尝试 retrieve()：

from llama_index.indices.managed.vectara import VectaraAutoRetriever
from llama_index.llms.openai import OpenAI

llm = OpenAI(model="gpt-3.5-turbo", temperature=0)

retriever = VectaraAutoRetriever(
    index,
    vector_store_info=vector_store_info,
    llm=llm,
    verbose=True,
)

retriever.retrieve("movie directed by Greta Gerwig")

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
Using query str: movie directed by Greta Gerwig
Using implicit filters: [('director', '==', 'Greta Gerwig')]
final filter string: (doc.director == 'Greta Gerwig')

[NodeWithScore(node=TextNode(id_='935c2319f66122e1c4189ccf32164b362d4b6bc2af4ede77fc47fd126546ba8d', embedding=None, metadata={'lang': 'eng', 'offset': '0', 'len': '79', 'year': '2023', 'director': 'Greta Gerwig', 'genre': 'fantasy', 'rating': '9.5'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='Barbie suffers a crisis that leads her to question her world and her existence.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.61976165)]

retriever.retrieve("a movie with a rating above 8")

INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
Using query str: movie with rating above 8
Using implicit filters: [('rating', '>', 8)]
final filter string: (doc.rating > '8')

[NodeWithScore(node=TextNode(id_='a55396c48ddb51e593676d37a18efa8da1cbab6fd206c9029ced924e386c12b5', embedding=None, metadata={'lang': 'eng', 'offset': '0', 'len': '129', 'year': '1995', 'genre': 'animated', 'rating': '8.3'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text="A cowboy doll is profoundly threatened and jealous when a new spaceman action figure supplants him as top toy in a boy's bedroom.", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.5414715),
 NodeWithScore(node=TextNode(id_='b1e87de59678f3f1831327948a716f6e5a217c9b8cee4c08d5555d337825fca8', embedding=None, metadata={'lang': 'eng', 'offset': '0', 'len': '220', 'year': '2010', 'director': 'Christopher Nolan', 'rating': '8.2'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='A thief who steals corporate secrets through the use of dream-sharing technology is given the inverse task of planting an idea into the mind of a C.E.O., but his tragic past may doom the project and his team to disaster.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.50233454),
 NodeWithScore(node=TextNode(id_='935c2319f66122e1c4189ccf32164b362d4b6bc2af4ede77fc47fd126546ba8d', embedding=None, metadata={'lang': 'eng', 'offset': '0', 'len': '79', 'year': '2023', 'director': 'Greta Gerwig', 'genre': 'fantasy', 'rating': '9.5'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text='Barbie suffers a crisis that leads her to question her world and her existence.', start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.500028),
 NodeWithScore(node=TextNode(id_='a860d7045527b4659a1a1171f922e30bee754ddcf83e0ec8423238f9f634f118', embedding=None, metadata={'lang': 'eng', 'offset': '0', 'len': '209', 'year': '2010', 'genre': 'animated', 'rating': '8.3'}, excluded_embed_metadata_keys=[], excluded_llm_metadata_keys=[], relationships={}, text="The toys are mistakenly delivered to a day-care center instead of the attic right before Andy leaves for college, and it's up to Woody to convince the other toys that they weren't abandoned and to return home.", start_char_idx=None, end_char_idx=None, text_template='{metadata_str}\n\n{content}', metadata_template='{key}: {value}', metadata_seperator='\n'), score=0.47051564)]

我们还可以在 VectaraAutoRetriever 中包含标准的 VectaraRetriever 参数。例如，如果我们想添加一个 filter，并将其附加到查询本身产生的任何额外过滤条件中，可以按如下方式操作：