Ragbits
Ragbit 是一个 Python 包，提供了构建强大的检索增强生成（RAG）应用程序所需的基本“组件”。它通过提供简单直观的 API 来优先考虑开发者体验。它还包括一套全面的工具，可帮助您无缝、高效地构建、测试和部署 RAG 应用程序。
Qdrant 在 Ragbits 中作为向量存储可用，用于从集合中摄取和搜索文档。
安装
安装捆绑了 Qdrant 集成的 Python 包。
pip install ragbits
用法
Ragbits 和 Qdrant 的使用示例如下所示
import asyncio
from qdrant_client import AsyncQdrantClient
from ragbits.core.embeddings.litellm import LiteLLMEmbeddings
from ragbits.core.vector_stores.qdrant import QdrantVectorStore
from ragbits.document_search import DocumentSearch, SearchConfig
from ragbits.document_search.documents.document import DocumentMeta
documents = [
DocumentMeta.create_text_document_from_literal(
"RIP boiled water. You will be mist."
),
DocumentMeta.create_text_document_from_literal(
"Why programmers don't like to swim? Because they're scared of the floating points."
),
DocumentMeta.create_text_document_from_literal("This one is completely unrelated."),
]
async def main() -> None:
embedder = LiteLLMEmbeddings(
model="text-embedding-3-small",
)
vector_store = QdrantVectorStore(
client=AsyncQdrantClient(url="http://localhost:6333"),
collection_name="{collection_name}",
)
document_search = DocumentSearch(
embedder=embedder,
vector_store=vector_store,
)
await document_search.ingest(documents)
all_documents = await vector_store.list()
print([doc.metadata["content"] for doc in all_documents])
query = "I write computer software. Tell me something."
vector_store_kwargs = {
"k": 1,
"max_distance": None,
}
results = await document_search.search(
query,
config=SearchConfig(vector_store_kwargs=vector_store_kwargs),
)
print(f"Documents similar to: {query}")
print([element.get_key() for element in results])