Ragbits
Ragbit is a Python package that offers essential “bits” for building powerful Retrieval-Augmented Generation (RAG) applications. It prioritizes developer experience by providing a simple and intuitive API. It also includes a comprehensive set of tools for seamlessly building, testing, and deploying your RAG applications efficiently.
Qdrant is available as a vectorstore in Ragbits to ingest and search search documents from a collection.
Installation
Install the Python package that comes bundled with the Qdrant integration.
pip install ragbits
Usage
An example usage of Ragbits and Qdrant would look something like this:
The following example uses OpenAI embeddings via LiteLLM.
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])
📚 Further Reading
- Ragbits Documentation
- Source Code