# Trying to resolve the error ,here are my code and error

**URL:** <https://forum.weaviate.io/t/trying-to-resolve-the-error-here-are-my-code-and-error/9620>\
**Category:** Support\
**Tags:** technical\
**Created:** [January 8, 2025, 6:38am UTC](https://forum.weaviate.io/t/trying-to-resolve-the-error-here-are-my-code-and-error/9620 "2025-01-08T06:38:57Z")\
**Posts on this page:** 3\
**Page:** 1

<div class="post-metadata">

**Author:** ![DhanushKumar\_R](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.weaviate.io/dhanushkumar_r/32/3310_2.png) [@DhanushKumar\_R](https://forum.weaviate.io/u/DhanushKumar_R)\
**Post date:** [January 8, 2025, 6:38am UTC](https://forum.weaviate.io/t/trying-to-resolve-the-error-here-are-my-code-and-error/9620/1 "2025-01-08T06:38:57Z")

</div>

```python
import weaviate
from weaviate.classes.init import Auth
import google.generativeai as genai
from typing import List, Dict
import os
from typing import List, Dict
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader
from langchain_weaviate.vectorstores import WeaviateVectorStore
from langchain.embeddings import HuggingFaceEmbeddings
from weaviate.classes import query as wvc
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_google_genai import GoogleGenerativeAIEmbeddings

WEAVIATE_API_KEY = ""
WEAVIATE_URL = ""
gemini_api_key = ""
huggingface_api_key = ""

# Connect to Weaviate Cloud
client = weaviate.connect_to_weaviate_cloud(
    cluster_url= WEAVIATE_URL,
    auth_credentials=Auth.api_key(WEAVIATE_API_KEY),
)

print(client.is_ready())

huggingface_key = huggingface_api_key
headers = {
    "X-HuggingFace-Api-Key": huggingface_key,
}

client = weaviate.connect_to_weaviate_cloud(
    cluster_url=WEAVIATE_URL, # `weaviate_url`: Weaviate URL
    auth_credentials=Auth.api_key(WEAVIATE_API_KEY), # `weaviate_key`: Weaviate API key
    headers=headers
)

# Initialize Gemini
genai.configure(api_key=gemini_api_key)

print("Client is Ready?", client.is_ready())
from weaviate import classes as wvc

client.collections.delete("WikipediaLangChain")

from weaviate.classes.config import Configure

client.collections.create(
    "WikipediaLangChain",
    vectorizer_config=[
        Configure.NamedVectors.text2vec_huggingface(
            name="title_vector",
            source_properties=["title"],
            model="sentence-transformers/all-MiniLM-L6-v2",
        )
    ],

)

embeddings = GoogleGenerativeAIEmbeddings(
        model="models/embedding-001", # Google's text embedding model
        google_api_key= gemini_api_key
    )

text_splitter = RecursiveCharacterTextSplitter(chunk_size=400, chunk_overlap=50)

# import first article
loader = PyPDFLoader("brazil-wikipedia-article-text.pdf", extract_images=False)
docs = loader.load_and_split(text_splitter)
print(f"GOT {len(docs)} docs for Brazil")
db = WeaviateVectorStore.from_documents(docs, embeddings, client=client, index_name="WikipediaLangChain")

# import second article
loader = PyPDFLoader("netherlands-wikipedia-article-text.pdf", extract_images=False)
docs = loader.load_and_split(text_splitter)
print(f"GOT {len(docs)} docs for Netherlands")
db = WeaviateVectorStore.from_documents(docs, embeddings, client=client, index_name="WikipediaLangChain")

# Create vector store
vector_store = WeaviateVectorStore(
    client=client,
    index_name="WikipediaLangChain",
    text_key="text",
    embedding=embeddings,  
    attributes=["source"]
)

vector_store.add_documents(docs)
collection = client.collections.get("WikipediaLangChain")
# lets first get our collection
collection = client.collections.get("WikipediaLangChain")

response = collection.aggregate.over_all(total_count=True)
print(response)

# Group by source
response = collection.aggregate.over_all(group_by="source")
for group in response.groups:
    print(group.grouped_by.value, group.total_count)

# View object properties
object = collection.query.fetch_objects(limit=1).objects[0]
print(object.properties.keys())
print(object.properties.get("source"))
print(object.properties.get("page"))
print(object.properties.get("text"))

# Query in French using Gemini
generateTask = "Quelle est la nourriture traditionnelle de ce pays?"
source_file = "brazil-wikipedia-article-text.pdf"

model = ChatGoogleGenerativeAI(
    model="gemini-pro", 
    google_api_key= gemini_api_key
)

# lets do a RAG directly using only Weaviate

query = collection.generate.near_text(
    query="tradicional food",
  
    limit=10,
    grouped_task=generateTask
)
print(query.generated)

```

* * *

AioRpcError Traceback (most recent call last)  
File c:\Users\dhanu.conda\envs\idk\_gpu\lib\site-packages\weaviate\collections\grpc\query.py:805, in \_QueryGRPC.\_\_call(self, request)  
804 assert self.\_connection.grpc\_stub is not None  
 → 805 res = await \_Retry(4).with\_exponential\_backoff(  
806 0,  
807 f"Searching in collection {request.collection}",  
808 self.\_connection.grpc\_stub.Search,  
809 request,  
810 metadata=self.\_connection.grpc\_headers(),  
811 timeout=self.\_connection.timeout\_config.query,  
812 )  
813 return cast(search\_get\_pb2.SearchReply, res)

File c:\Users\dhanu.conda\envs\idk\_gpu\lib\site-packages\weaviate\collections\grpc\retry.py:31, in \_Retry.with\_exponential\_backoff(self, count, error, f, \*args, **kwargs)  
30 if e.code() != StatusCode.UNAVAILABLE:  
—\> 31 raise e  
32 logger.info(  
33 f"{error} received exception: {e}. Retrying with exponential backoff in {2**count} seconds"  
34 )

File c:\Users\dhanu.conda\envs\idk\_gpu\lib\site-packages\weaviate\collections\grpc\retry.py:28, in \_Retry.with\_exponential\_backoff(self, count, error, f, \*args, \*\*kwargs)  
27 try:  
—\> 28 return await f(\*args, \*\*kwargs)  
29 except AioRpcError as e:

File c:\Users\dhanu.conda\envs\idk\_gpu\lib\site-packages\grpc\aio\_call.py:327, in \_UnaryResponseMixin. **await** (self)  
326 else:  
 → 327 raise \_create\_rpc\_error(  
328 self.\_cython\_call.\_initial\_metadata,  
329 self.\_cython\_call.\_status,  
330 )  
331 else:

AioRpcError: \<AioRpcError of RPC that terminated with:  
status = StatusCode.UNKNOWN  
details = “explorer: get class: concurrentTargetVectorSearch): explorer: get class: vector search: object vector search at index wikipedialangchain: shard wikipedialangchain\_mj30ETuKNGfK: vector search: knn search: distance between entrypoint and query node: 768 vs 384: vector lengths don’t match”  
debug\_error\_string = “UNKNOWN:Error received from peer {grpc\_message:“explorer: get class: concurrentTargetVectorSearch): explorer: get class: vector search: object vector search at index wikipedialangchain: shard wikipedialangchain\_mj30ETuKNGfK: vector search: knn search: distance between entrypoint and query node: 768 vs 384: vector lengths don't match”, grpc\_status:2, created\_time:“2025-01-08T06:29:39.4893321+00:00”}”

> 

During handling of the above exception, another exception occurred:

WeaviateQueryError Traceback (most recent call last)  
Cell In[59], line 5  
1 # lets do a RAG directly using only Weaviate  
----\> 5 query = collection.generate.near\_text(  
6 query=“tradicional food”,  
7  
8 limit=10,  
9 grouped\_task=generateTask  
10 )  
11 print(query.generated)

File c:\Users\dhanu.conda\envs\idk\_gpu\lib\site-packages\weaviate\syncify.py:23, in convert..sync\_method(self, \_\_new\_name, \*args, \*\*kwargs)  
20 @wraps(method) # type: ignore  
21 def sync\_method(self, \*args, \_\_new\_name=new\_name, \*\*kwargs):  
22 async\_func = getattr(cls, \_\_new\_name)  
—\> 23 return \_EventLoopSingleton.get\_instance().run\_until\_complete(  
24 async\_func, self, \*args, \*\*kwargs  
25 )

File c:\Users\dhanu.conda\envs\idk\_gpu\lib\site-packages\weaviate\event\_loop.py:42, in \_EventLoop.run\_until\_complete(self, f, \*args, \*\*kwargs)  
40 raise WeaviateClosedClientError()  
41 fut = asyncio.run\_coroutine\_threadsafe(f(\*args, \*\*kwargs), self.loop)  
—\> 42 return fut.result()

File c:\Users\dhanu.conda\envs\idk\_gpu\lib\concurrent\futures\_base.py:458, in Future.result(self, timeout)  
456 raise CancelledError()  
457 elif self.\_state == FINISHED:  
 → 458 return self.\_\_get\_result()  
459 else:  
460 raise TimeoutError()

File c:\Users\dhanu.conda\envs\idk\_gpu\lib\concurrent\futures\_base.py:403, in Future.\_\_get\_result(self)  
401 if self.\_exception:  
402 try:  
 → 403 raise self.\_exception  
404 finally:  
405 # Break a reference cycle with the exception in self.\_exception  
406 self = None

File c:\Users\dhanu.conda\envs\idk\_gpu\lib\site-packages\weaviate\collections\queries\near\_text\generate.py:101, in \_NearTextGenerateAsync.near\_text(self, query, single\_prompt, grouped\_task, grouped\_properties, certainty, distance, move\_to, move\_away, limit, offset, auto\_limit, filters, group\_by, rerank, target\_vector, include\_vector, return\_metadata, return\_properties, return\_references)  
28 async def near\_text(  
29 self,  
30 query: Union[List[str], str],  
(…)  
49 return\_references: Optional[ReturnReferences[TReferences]] = None,  
50 ) → GenerativeSearchReturnType[Properties, References, TProperties, TReferences]:  
51 “”“Perform retrieval-augmented generation (RaG) on the results of a by-image object search in this collection using the image-capable vectorization module and vector-based similarity search.  
52  
53 See the [docs](https://weaviate.io/developers/weaviate/api/graphql/search-operators#neartext) for a more detailed explanation.  
(…)  
99 If the request to the Weaviate server fails.  
100 “””  
 → 101 res = await self.\_query.near\_text(  
102 near\_text=query,  
103 certainty=certainty,  
104 distance=distance,  
105 move\_to=move\_to,  
106 move\_away=move\_away,  
107 limit=limit,  
108 offset=offset,  
109 autocut=auto\_limit,  
110 filters=filters,  
111 group\_by=\_GroupBy.from\_input(group\_by),  
112 rerank=rerank,  
113 target\_vector=target\_vector,  
114 generative=\_Generative(  
115 single=single\_prompt,  
116 grouped=grouped\_task,  
117 grouped\_properties=grouped\_properties,  
118 ),  
119 return\_metadata=self.\_parse\_return\_metadata(return\_metadata, include\_vector),  
120 return\_properties=self.\_parse\_return\_properties(return\_properties),  
121 return\_references=self.\_parse\_return\_references(return\_references),  
122 )  
123 return self.\_result\_to\_generative\_return(  
124 res,  
125 \_QueryOptions.from\_input(  
(…)  
135 return\_references,  
136 )

File c:\Users\dhanu.conda\envs\idk\_gpu\lib\site-packages\weaviate\collections\grpc\query.py:817, in \_QueryGRPC.\_\_call(self, request)  
815 if e.code().name == PERMISSION\_DENIED:  
816 raise InsufficientPermissionsError(e)  
 → 817 raise WeaviateQueryError(str(e), “GRPC search”) # pyright: ignore  
818 except WeaviateRetryError as e:  
819 raise WeaviateQueryError(str(e), “GRPC search”)

WeaviateQueryError: Query call with protocol GRPC search failed with message \<AioRpcError of RPC that terminated with:  
status = StatusCode.UNKNOWN  
details = “explorer: get class: concurrentTargetVectorSearch): explorer: get class: vector search: object vector search at index wikipedialangchain: shard wikipedialangchain\_mj30ETuKNGfK: vector search: knn search: distance between entrypoint and query node: 768 vs 384: vector lengths don’t match”  
debug\_error\_string = “UNKNOWN:Error received from peer {grpc\_message:“explorer: get class: concurrentTargetVectorSearch): explorer: get class: vector search: object vector search at index wikipedialangchain: shard wikipedialangchain\_mj30ETuKNGfK: vector search: knn search: distance between entrypoint and query node: 768 vs 384: vector lengths don't match”, grpc\_status:2, created\_time:“2025-01-08T06:29:39.4893321+00:00”}”

> .

---

<div class="post-metadata">

**Author:** ![DudaNogueira](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.weaviate.io/dudanogueira/32/7846_2.png) [@DudaNogueira](https://forum.weaviate.io/u/DudaNogueira)\
**Post date:** [January 8, 2025, 2:20pm UTC](https://forum.weaviate.io/t/trying-to-resolve-the-error-here-are-my-code-and-error/9620/2 "2025-01-08T14:20:18Z")

</div>

hi @DhanushKumar_R !!

Welcome to our community 🤗

Your error message indicates that there is a dimension mismatch.

So your store vectors have on dimensions length, and the query is being passed as a different dimension length:

> [@DhanushKumar\_R](#):
>
> vector search: knn search: distance between entrypoint and query node: 768 vs 384: vector lengths don’t match”, grpc\_status:2, created\_time:“2025-01-08T06:29:39.4893321+00:00”}”

I see you are using the recipe I have written: [recipes/integrations/llm-frameworks/langchain/loading-data at main · weaviate/recipes · GitHub](https://github.com/weaviate/recipes/tree/main/integrations/llm-frameworks/langchain/loading-data)

Nice!! 🙂

The root cause of your error is because you are defining one vectorizer to be used by Weaviate, while using a different one for Langchain here:

```python
client.collections.create(
    "WikipediaLangChain",
    vectorizer_config=[
        Configure.NamedVectors.text2vec_huggingface(
            name="title_vector",
            source_properties=["title"],
            model="sentence-transformers/all-MiniLM-L6-v2",
        )
    ],

)

embeddings = GoogleGenerativeAIEmbeddings(
        model="models/embedding-001", # Google's text embedding model
        google_api_key= gemini_api_key
    )

```

Those two must be configured for the same model.

Let me know if this helps!

Thanks!

---

<div class="post-metadata">

**Author:** ![DhanushKumar\_R](https://yyz1.discourse-cdn.com/flex027/user_avatar/forum.weaviate.io/dhanushkumar_r/32/3310_2.png) [@DhanushKumar\_R](https://forum.weaviate.io/u/DhanushKumar_R)\
**Post date:** [January 29, 2025, 10:12am UTC](https://forum.weaviate.io/t/trying-to-resolve-the-error-here-are-my-code-and-error/9620/3 "2025-01-29T10:12:22Z")

</div>

Thank you so much !!