Current Behavior

Getting qdrant_client.http.exceptions.UnexpectedResponse: Unexpected Response: 400 (Bad Request) when performing
retriever.retrieve(query)

Steps to Reproduce

1. using FastEmbedEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2") from from llama_index.embeddings.fastembed (https://qdrant.github.io/fastembed/examples/Supported_Models/)
   Settings.embed_model = embed_model

2. self.client = QdrantClient(url=url, api_key=qdrant_api_key)

   self.client.recreate_collection(
       collection_name=collection_name,
       vectors_config=VectorParams(size=384, distance=Distance.COSINE),
   )
   
   self.vector_store = QdrantVectorStore(client=self.client, collection_name=collection_name, )
   self.index = VectorStoreIndex.from_vector_store(vector_store=self.vector_store)
   

3. ch_engine = index.as_chat_engine(llm=llm, chat_mode='openai')
    retriever = index.as_retriever()
   

4.  retriever.retrieve(query) --> sending query as str.
   

I am getting below error,
qdrant_client.http.exceptions.UnexpectedResponse: Unexpected Response: 400 (Bad Request)
Raw response content:
b'{"status":{"error":"Wrong input: Vector inserting error: expected dim: 384, got 1536"},"time":0.00039087}'

the embedding model supports only 384 dim, not sure from where 1536 is coming into picture.

Expected Behavior

Embedding seems not applied to query and I see there is no way to input embedding algo while creating index

Possible Solution

Context (Environment)

Detailed Description

Possible Implementation