qdrant_client.http.exceptions.UnexpectedResponse: Unexpected Response: 400 (Bad Request)

[settur1409]

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

[agourlay]

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

I guess it comes from

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

openai uses 1536 dimensions embeddings.

[settur1409]

Hey @agourlay, I didn't used openAI embeddings. I tried other alternative,

q_engine = index.as_query_engine()
q_engine.query(query)

this also giving same error. I don't have any llm here.
let me know your inputs.

[agourlay]

I believe this is a fastembed specific issue so I took the liberty to transfer it directly on the repository.

[agourlay]

dear @NirantK, any idea what it is going on there?

[NirantK]

@agourlay @settur1409 will reproduce and get back on findings — in the meantime @Anush008 maintains the Llama Index bindings for all Qdrant packages including FastEmbed

[Anush008]

@settur1409, could you share your entire code. Would need that to reproduce.

Maybe as a Gist or file?

[settur1409]

qdrant_main_file.txt --> I compiled my code into single file. Please check.
Below is the log that I got,

Fetching 7 files: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:00<?, ?it/s]
8
Parsing nodes: 100%|█████████████████████████████████████████████████████████████████████████████████████████████████| 131/131 [00:00<00:00, 131.09it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 177/177 [01:40<00:00, 1.76it/s]
100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 177/177 [01:40<00:00, 1.76it/s]
Generating embeddings: 100%|██████████████████████████████████████████████████████████████████████████████████████████| 177/177 [01:03<00:00, 2.79it/s]
9

this is the error I got.
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":0.001277349}'

let me know if you need some other information.

[settur1409]

from the UI,

Attached few snashots from qdrant GUI.

[Anush008]

@settur1409, what version of LLamaIndex are you on? There have been quite a lot changes in the recent versions.

[settur1409]

Below are the list of llama-index related packages I see in my env

llama-index 0.10.16
llama-index-agent-openai 0.1.5
llama-index-cli 0.1.7
llama-index-core 0.10.16.post1
llama-index-embeddings-fastembed 0.1.4
llama-index-embeddings-huggingface 0.1.4
llama-index-embeddings-openai 0.1.6
llama-index-indices-managed-llama-cloud 0.1.3
llama-index-legacy 0.9.48
llama-index-llms-anyscale 0.1.3
llama-index-llms-langchain 0.1.3
llama-index-llms-openai 0.1.7
llama-index-multi-modal-llms-openai 0.1.4
llama-index-program-openai 0.1.4
llama-index-question-gen-openai 0.1.3
llama-index-readers-file 0.1.8
llama-index-readers-llama-parse 0.1.3
llama-index-vector-stores-chroma 0.1.5
llama-index-vector-stores-qdrant 0.1.4
llama-parse 0.3.6
llamaindex-py-client 0.1.13

qdrant-client version:
qdrant-client 1.8.0

[Anush008]

@settur1409, you have to move the following lines to the top(below the imports).

from llama_index.core import Settings

embed_model = FastEmbedEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
Settings.embed_model = embed_model