b'{"status":{"error":"Wrong input: Default vector params are not specified in config"},"time":0.00036507}'
Labels: name: area: embeddings, name: area: vector store, name: auto:bug, name: area: models

### System Info

langchain==0.0.348
wsl
python 3.10.2

### Who can help?

@hwchase17
@agola11

### Information

- [X] The official example notebooks/scripts
- [X] My own modified scripts

### Related Components

- [X] LLMs/Chat Models
- [ ] Embedding Models
- [ ] Prompts / Prompt Templates / Prompt Selectors
- [ ] Output Parsers
- [ ] Document Loaders
- [X] Vector Stores / Retrievers
- [ ] Memory
- [ ] Agents / Agent Executors
- [ ] Tools / Toolkits
- [ ] Chains
- [ ] Callbacks/Tracing
- [ ] Async

### Reproduction

I'm looking to run a simple RAG using Qdrant pre instantiated with data (something like the [link from the docs](https://colab.research.google.com/drive/19RxxkZdnq_YqBH5kBV10Rt0Rax-kminD?usp=sharing#scrollTo=fvCHMT73SmKi)) but its giving me the error in the title.

I'm posting it here since the error seems to be on langchain side. not qdrant side.

Ive tried various ways of using langchain to connect to qdrant but it always ends up with this error.

This happens even if i use the deprecated VectorDBQA or RetrievalQA

```
from qdrant_client import QdrantClient
from langchain.chat_models import AzureChatOpenAI
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import Qdrant


QDRANT_ENDPOINT = "localhost"
MODEL_TO_USE = 'BAAI/bge-base-en-v1.5' 
collection_name = "collection"


client = QdrantClient(QDRANT_ENDPOINT, port=6333, timeout=120)
embeddings = HuggingFaceEmbeddings(model_name=MODEL_TO_USE)
qdrant = Qdrant(client, collection_name, embeddings)

qdrant.similarity_search("middle east") 

```
gives

```
>>> qdrant.similarity_search("middle east")
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/user/.local/lib/python3.10/site-packages/langchain/vectorstores/qdrant.py", line 274, in similarity_search
    results = self.similarity_search_with_score(
  File "/home/user/.local/lib/python3.10/site-packages/langchain/vectorstores/qdrant.py", line 350, in similarity_search_with_score
    return self.similarity_search_with_score_by_vector(
  File "/home/user/.local/lib/python3.10/site-packages/langchain/vectorstores/qdrant.py", line 595, in similarity_search_with_score_by_vector
    results = self.client.search(
  File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/qdrant_client.py", line 340, in search
    return self._client.search(
  File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/qdrant_remote.py", line 472, in search
    search_result = self.http.points_api.search_points(
  File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/http/api/points_api.py", line 1388, in search_points
    return self._build_for_search_points(
  File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/http/api/points_api.py", line 636, in _build_for_search_points
    return self.api_client.request(
  File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/http/api_client.py", line 74, in request
    return self.send(request, type_)
  File "/home/user/.local/lib/python3.10/site-packages/qdrant_client/http/api_client.py", line 97, in send
    raise UnexpectedResponse.for_response(response)
qdrant_client.http.exceptions.UnexpectedResponse: Unexpected Response: 400 (Bad Request)
Raw response content:
b'{"status":{"error":"Wrong input: Default vector params are not specified in config"},"time":0.000118885}'
```
Using this as a retriever also fails, which is what i need it for.

Testing with qdrant alone works.

```
client.query(collection_name,"middle east") # using qdrant itself works
```





### Expected behavior

For the queries to be fetched and not throw this error