Retrievers

from camel.embeddings import OpenAIEmbedding
from camel.retrievers import VectorRetriever
from camel.storages.vectordb_storages import QdrantStorage

# Set up vector DB for embeddings
vector_storage = QdrantStorage(
    vector_dim=OpenAIEmbedding().get_output_dim(),
    collection_name="my first collection",
    path="storage_customized_run",
)
vr = VectorRetriever(embedding_model=OpenAIEmbedding(), storage=vector_storage)

# Embed and store your data (URL or file)
content_input_path = "https://www.camel-ai.org/"
vr.process(content=content_input_path)

# Run a query for semantic search
query = "What is CAMEL"
results = vr.query(query=query, similarity_threshold=0)
print(results)

>>>  [{'similarity score': '0.81...', 'content path': 'https://www.camel-ai.org/', 'metadata': {...}, 'text': '...CAMEL-AI.org is an open-source community dedicated to the study of autonomous and communicative agents...'}]

from camel.retrievers import AutoRetriever
from camel.types import StorageType

ar = AutoRetriever(
    vector_storage_local_path="camel/retrievers",
    storage_type=StorageType.QDRANT,
)

retrieved_info = ar.run_vector_retriever(
    contents=["https://www.camel-ai.org/"],   # One or many URLs/files
    query="What is CAMEL-AI",
    return_detailed_info=True,
)
print(retrieved_info)

>>> Original Query: {What is CAMEL-AI}
>>> Retrieved Context:
>>> {'similarity score': '0.83...', 'content path': 'https://www.camel-ai.org/', 'metadata': {...}, 'text': 'Mission\n\nCAMEL-AI.org is an open-source community dedicated to the study of autonomous and communicative agents...'}