Camel
Camel is a Python framework to build and use LLM-based agents for real-world task solving.
Qdrant is available as a storage mechanism in Camel for ingesting and retrieving semantically similar data.
Usage With Qdrant
- Install Camel with the
vector-databasesextra.
pip install "camel[vector-databases]" - Configure the
QdrantStorageclass.
from camel.storages import QdrantStorage, VectorDBQuery, VectorRecord from camel.types import VectorDistance qdrant_storage = QdrantStorage( url_and_api_key=( "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333", "<provide-your-own-key>", ), collection_name="{collection_name}", distance=VectorDistance.COSINE, vector_dim=384, ) The QdrantStorage class implements methods to read and write to a Qdrant instance. An instance of this class can now be passed to retrievers for interfacing with your Qdrant collections.
qdrant_storage.add([VectorRecord( vector=[-0.1, 0.1, ...], payload={'key1': 'value1'}, ), VectorRecord( vector=[-0.1, 0.1, ...], payload={'key2': 'value2'}, ),]) query_results = qdrant_storage.query(VectorDBQuery(query_vector=[0.1, 0.2, ...], top_k=10)) for result in query_results: print(result.record.payload, result.similarity) qdrant_storage.clear() - Use the
QdrantStoragein Camel’s Vector Retriever.
from camel.embeddings import OpenAIEmbedding from camel.retrievers import VectorRetriever # Initialize the VectorRetriever with an embedding model vr = VectorRetriever(embedding_model=OpenAIEmbedding()) content_input_path = "<URL-TO-SOME-RESOURCE>" vr.process(content_input_path, qdrant_storage) # Execute the query and retrieve results results = vr.query("<SOME_USER_QUERY>", vector_storage) - Camel also provides an Auto Retriever implementation that handles both embedding and storing data and executing queries.
from camel.retrievers import AutoRetriever from camel.types import StorageType ar = AutoRetriever( url_and_api_key=( "https://xyz-example.eu-central.aws.cloud.qdrant.io:6333", "<provide-your-own-key>", ), storage_type=StorageType.QDRANT, ) retrieved_info = ar.run_vector_retriever( contents=["<URL-TO-SOME-RESOURCE>"], query=""<SOME_USER_QUERY>"", return_detailed_info=True, ) print(retrieved_info) You can refer to the Camel documentation for more information about the retrieval mechansims.