There's more to discovery than text search, especially when it comes to food. People often choose meals based on appearance rather than descriptions and ingredients. Let Qdrant help your users find their next delicious meal using visual search, even if they don't know the dish's name.Check ...
There's more to discovery than text search, especially when it comes to food. People often choose meals based on appearance rather than descriptions and ingredients. Let Qdrant help your users find their next delicious meal using visual search, even if they don't know the dish's name.Check ...
It deploys as an API service providing search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications for matching, searching, recommending, and much more! php php-client qdrant qdrant-vector-database Updated Nov ...
Overwrite payload Replace full payload of points with new one Query points Universally query points. This endpoint covers all capabilities of search, recommend, discover, filters. But also enables hybrid and multi-stage queries. Query points, grouped by a given payload field Universally query po...
Overwrite payload Replace full payload of points with new one Query points Universally query points. This endpoint covers all capabilities of search, recommend, discover, filters. But also enables hybrid and multi-stage queries. Query points, grouped by a given payload field Universally query po...
Adds minimal full-text search capabilities into all filterable APIs. Specify ngram-prefix or word tokenizer to make queries only among records which contain specified words. Improvements :mortar_board: https://github.com/qdrant/qdrant/pull/1000- Multi-platform builds forx86_64andaarch64- allows ...
Qdrant is an open source vector search engine with high performance. It can process and store a large amount of high-dimensional vector data and supports similarity-based quick search. For more information, seeQdrant documentation. Limitations ...
record = client.search( collection_name="text_embeddings", query_vector=query_vector, limit=1, ) return record[0].payload['itemtype'] 第二个函数将 PIL 图像作为输入,并通过对图像嵌入集合执行相似性搜索,以文件路径列表的形式返回前十个图像(来自库存)。
Unlock the full potential of your AI agents with Qdrant’s powerful vector search and scalable infrastructure, allowing them to handle complex tasks, adapt in real time, and drive smarter, data-driven outcomes across any environment. Learn More ...
load_qa_chain(llm, chain_type="stuff", prompt=qa_prompt) ## Retrieve docs from Qdrant Vector DB based upon user prompt docs = qdrant.similarity_search(user_prompt) answer = chain({"input_documents": docs, "question": question,"context": docs}, return_only_outputs=True)['output_text'...