When using the new .add method, unable to provide collection config

[bnkc]

Here is my example:

from qdrant_client.models import Distance, VectorParams
from qdrant_client import QdrantClient


# Initialize the client
client = QdrantClient(":memory:")  


client.create_collection(
    collection_name="test_collection",
    vectors_config=VectorParams(size=384, distance=Distance.COSINE),
)


# Prepare your documents, metadata, and IDs
docs = [
    "Qdrant has Langchain integrations",
    "Qdrant also has Llama Index integrations",
] * 5
metadata = [
    {"source": "Langchain-docs"},
    {"source": "Linkedin-docs"},
] * 5


client.add(collection_name="test_collection", documents=docs, metadata=metadata)

If I try to create_collection before applying the client.add, I get the following traceback:

Traceback (most recent call last):
  File "/Users/lev/Developer/poetry-demo/poetry_demo/qdrant.py", line 40, in <module>
    client.add(collection_name="test_collection", documents=docs, metadata=metadata)
  File "/Users/lev/.pyenv/versions/3.11.1/envs/testing-venv/lib/python3.11/site-packages/qdrant_client/qdrant_fastembed.py", line 496, in add
    self._validate_collection_info(collection_info)
  File "/Users/lev/.pyenv/versions/3.11.1/envs/testing-venv/lib/python3.11/site-packages/qdrant_client/qdrant_fastembed.py", line 348, in _validate_collection_info
    assert isinstance(
AssertionError: Collection have incompatible vector params: size=384 distance=<Distance.COSINE: 'Cosine'> hnsw_config=None quantization_config=None on_disk=None

This is an issue because if I want to use the batch_search method later on rather than the batch_query method, I can't because of a mismatching config. The reason I would rather use the batch_search is because I have already generated embedding for a large volume of text. It makes no sense to regenerate upon query. I would rather query back the original embedding and provide as a parameter to the one of the search methods:

search_result = client.search(
    collection_name="demo_collection", query_vector=[0.1, 0.2, 0.3, 0.4]
)

[joein]

Hello @bnkc

With pure qdrant-client API (without fastembed mixin's methods like add and query), you can have unnamed vectors, which means that you don't need to provide a name.

However, fastembed creates named vectors and operates with them. Names of the vectors are generated from the chosen model names.
Thus, to do search, you need to set query_vector to a some named vector, e.g.:

models.NamedVector(
    name=client.get_vector_field_name(), vector=query_vector
)

If collection was not created, then the first call to add creates it.
If you want to create it on your own, take a look at get_fastembed_vector_params method

[bnkc]

Hi @joein

Thanks! This worked for me :)