# Stopiteration error

**URL:** <https://discuss.huggingface.co/t/stopiteration-error/155463>\
**Category:** 🤗Transformers\
**Created:** [May 16, 2025, 4:01am UTC](https://discuss.huggingface.co/t/stopiteration-error/155463 "2025-05-16T04:01:20Z")\
**Posts on this page:** 5\
**Page:** 1

<div class="post-metadata">

**Author:** ![coolsajan](https://avatars.discourse-cdn.com/v4/letter/c/e19b73/32.png) [@coolsajan](https://discuss.huggingface.co/u/coolsajan)\
**Post date:** [May 16, 2025, 4:01am UTC](https://discuss.huggingface.co/t/stopiteration-error/155463/1 "2025-05-16T04:01:20Z")

</div>

tags=[‘Chroma’, ‘HuggingFaceEmbeddings’] vectorstore=\<langchain\_chroma.vectorstores.Chroma object at 0x7f3e9ef40e80\> search\_kwargs={‘k’: 3}  
Using model: mistralai/Mistral-7B-v0.1  
/home/user/app/app/recommender.py:84: LangChainDeprecationWarning: The method `Chain. __call__ ` was deprecated in langchain 0.1.0 and will be removed in 1.0. Use :meth:`~invoke` instead.  
result = qa\_chain({“query”: question})  
Generation Error:  
Traceback (most recent call last):  
File “/home/user/app/app/recommender.py”, line 84, in retrieve\_and\_generate  
result = qa\_chain({“query”: question})  
File “/usr/local/lib/python3.10/site-packages/langchain\_core/\_api/deprecation.py”, line 191, in warning\_emitting\_wrapper  
return wrapped(\*args, \*\*kwargs)  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/base.py”, line 386, in **call**  
return self.invoke(  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/base.py”, line 167, in invoke  
raise e  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/base.py”, line 157, in invoke  
self.\_call(inputs, run\_manager=run\_manager)  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/retrieval\_qa/base.py”, line 154, in \_call  
answer = self.combine\_documents\_chain.run(  
File “/usr/local/lib/python3.10/site-packages/langchain\_core/\_api/deprecation.py”, line 191, in warning\_emitting\_wrapper  
return wrapped(\*args, \*\*kwargs)  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/base.py”, line 608, in run  
return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[  
File “/usr/local/lib/python3.10/site-packages/langchain\_core/\_api/deprecation.py”, line 191, in warning\_emitting\_wrapper  
return wrapped(\*args, \*\*kwargs)  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/base.py”, line 386, in **call**  
return self.invoke(  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/base.py”, line 167, in invoke  
raise e  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/base.py”, line 157, in invoke  
self.\_call(inputs, run\_manager=run\_manager)  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/combine\_documents/base.py”, line 138, in \_call  
output, extra\_return\_dict = self.combine\_docs(  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/combine\_documents/stuff.py”, line 259, in combine\_docs  
return self.llm\_chain.predict(callbacks=callbacks, \*\*inputs), {}  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/llm.py”, line 319, in predict  
return self(kwargs, callbacks=callbacks)[self.output\_key]  
File “/usr/local/lib/python3.10/site-packages/langchain\_core/\_api/deprecation.py”, line 191, in warning\_emitting\_wrapper  
return wrapped(\*args, \*\*kwargs)  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/base.py”, line 386, in **call**  
return self.invoke(  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/base.py”, line 167, in invoke  
raise e  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/base.py”, line 157, in invoke  
self.\_call(inputs, run\_manager=run\_manager)  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/llm.py”, line 127, in \_call  
response = self.generate([inputs], run\_manager=run\_manager)  
File “/usr/local/lib/python3.10/site-packages/langchain/chains/llm.py”, line 139, in generate  
return self.llm.generate\_prompt(  
File “/usr/local/lib/python3.10/site-packages/langchain\_core/language\_models/llms.py”, line 764, in generate\_prompt  
return self.generate(prompt\_strings, stop=stop, callbacks=callbacks, \*\*kwargs)  
File “/usr/local/lib/python3.10/site-packages/langchain\_core/language\_models/llms.py”, line 971, in generate  
return self.\_generate\_helper(  
File “/usr/local/lib/python3.10/site-packages/langchain\_core/language\_models/llms.py”, line 790, in \_generate\_helper  
self.\_generate(  
File “/usr/local/lib/python3.10/site-packages/langchain\_core/language\_models/llms.py”, line 1545, in \_generate  
self.\_call(prompt, stop=stop, run\_manager=run\_manager, \*\*kwargs)  
File “/usr/local/lib/python3.10/site-packages/langchain\_huggingface/llms/huggingface\_endpoint.py”, line 312, in \_call  
response\_text = self.client.text\_generation(  
File “/usr/local/lib/python3.10/site-packages/huggingface\_hub/inference/\_client.py”, line 2297, in text\_generation  
provider\_helper = get\_provider\_helper(self.provider, task=“text-generation”, model=model\_id)  
File “/usr/local/lib/python3.10/site-packages/huggingface\_hub/inference/\_providers/ **init**.py”, line 165, in get\_provider\_helper  
provider = next(iter(provider\_mapping))  
StopIteration

def retrieve\_and\_generate(retriever, question, use\_hf=True):  
prompt = ChatPromptTemplate.from\_messages([  
SystemMessagePromptTemplate.from\_template(  
"Use the following pieces of context to answer the question at the end. "  
“If you don’t know the answer, just say you don’t know — don’t try to make up an answer.”  
),  
HumanMessagePromptTemplate.from\_template(  
“Context:\n{context}\n\nQuestion:\n{question}\n\nHelpful Answer:”  
)  
])

```
try:
    if use_hf:
        hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
        if not hf_token:
            raise EnvironmentError("Missing HUGGINGFACEHUB_API_TOKEN environment variable")

        llm = HuggingFaceEndpoint(
            repo_id="mistralai/Mistral-7B-v0.1",
            temperature=0.7,
            huggingfacehub_api_token=hf_token,
            task="text2text-generation"
        )
    else:
        llm = OllamaLLM(model="llama2", temperature=0.7)

    print(f"Using model: {getattr(llm, 'repo_id', 'local model')}")

    qa_chain = RetrievalQA.from_chain_type(
        llm=llm,
        retriever=retriever,
        chain_type="stuff",
        chain_type_kwargs={"prompt": prompt},
        return_source_documents=True
    )

    # Use 'query' key as per RetrievalQA interface
    result = qa_chain({"query": question})

    return question, result["result"]

except Exception as e:
    print(f"Generation Error: {e}")
    traceback.print_exc()
    return question, "Could not generate a response."

```

def load\_retriver(foodPlace:str,presist\_dir: str = “tmp/data/vector\_store”) → VectorStoreRetriever:  
“”"  
load ChromaDB vectore store and retrun langchain retriver.  
“”"  
try:  
logging.info(“Data retriver started..”)  
safe\_foodPlace = re.sub(r"[^\w.-]", “\_”, foodPlace)

```
    chroma_path=os.path.join(presist_dir,safe_foodPlace)
    print(chroma_path)

    if not os.path.join(chroma_path):
        raise ValueError(f"no vectore store found at {chroma_path}")
    
    embedding=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')

    vectoreDB=Chroma(
        collection_name=safe_foodPlace,
        persist_directory=chroma_path,
        embedding_function=embedding
    )

    retriver=vectoreDB.as_retriever(
        search_type="similarity",
        search_kwargs ={'k':3}
    )

    return retriver

except Exception as e:
    raise CustomException(e,sys)

```

def check\_dB\_data(foodPlace):  
# Data ingestion if vector store doesn’t exist  
vector\_path = get\_foodPlace\_vector\_path(foodPlace)  
if os.path.exists(vector\_path):  
print(f"\n[INFO] Vector store already exists for {foodPlace}. Skipping data pull.“)  
else:  
print(f”\n[INFO] Pulling data for {foodPlace}…")  
cleaner\_store = CleanAndSaveToChromaDBC()

```
    try:
        tripa = TripAdviserDataPull(foodPlace=foodPlace)
        tripa_path = tripa.initiate_tripadviser_data_pull()
        cleaner_store.initiate_clean_chromadb(foodPlace=foodPlace, filepath=[tripa_path])
    except:
        print("No tripa data..")
        
    
    try:
        google = GoogleMapsDataPull(foodPlace=foodPlace)
        google_path = google.initiate_google_maps_data_pull()
        cleaner_store.initiate_clean_chromadb(foodPlace=foodPlace, filepath=[google_path])
    except:
        print("No data in google review....")

# Load retriever
print("\n[INFO] Loading retriever and querying...")
retriever = load_retriver(foodPlace=foodPlace)
print(retriever)
return retriever

```

NEED HELP WITH THIS ERROR

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<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [May 16, 2025, 10:14am UTC](https://discuss.huggingface.co/t/stopiteration-error/155463/2 "2025-05-16T10:14:57Z")

</div>

Maybe same or similar case.

> [@Facing issue using a model hosted on HuggingFace Server and talking to it using API\_KEY](https://discuss.huggingface.co/t/facing-issue-using-a-model-hosted-on-huggingface-server-and-talking-to-it-using-api-key/154529):
>
> I am trying to create a simple langchain app on text-generation using API to communicate with models on HuggingFace servers. I created a “.env” file and stored by KEY in the variable: “HUGGINGFACEHUB\_API\_TOKEN” I also checked it, API token is valid. Post that, I tried running this code snippet: from langchain\_huggingface import ChatHuggingFace, HuggingFaceEndpoint from dotenv import load\_dotenv load\_dotenv() llm = HuggingFaceEndpoint( repo\_id="TinyLlama/TinyL…

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<div class="post-metadata">

**Author:** ![tejoess](https://avatars.discourse-cdn.com/v4/letter/t/7feea3/32.png) [@tejoess](https://discuss.huggingface.co/u/tejoess)\
**Post date:** [June 5, 2025, 1:49pm UTC](https://discuss.huggingface.co/t/stopiteration-error/155463/3 "2025-06-05T13:49:22Z")

</div>

Did you got any solution, I am facing same issue, when i am running simple HF chat model using Langchain, i am getting this error called stopIteration, i changed API key, configured permission, changed code logic did everything, still encountering same issue , help me out !

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<div class="post-metadata">

**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\
**Post date:** [June 6, 2025, 5:43am UTC](https://discuss.huggingface.co/t/stopiteration-error/155463/4 "2025-06-06T05:43:33Z")

</div>

How about this? [API Request issue · Issue #2966 · huggingface/huggingface\_hub · GitHub](https://github.com/huggingface/huggingface_hub/issues/2966#issuecomment-2880540838)

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<div class="post-metadata">

**Author:** ![coolsajan](https://avatars.discourse-cdn.com/v4/letter/c/e19b73/32.png) [@coolsajan](https://discuss.huggingface.co/u/coolsajan)\
**Post date:** [June 14, 2025, 12:36pm UTC](https://discuss.huggingface.co/t/stopiteration-error/155463/5 "2025-06-14T12:36:05Z")

</div>

Hey, i am the one that raised this erorr .. i tried multiple repo .. and found out “mistralai/Mistral-7B-Instruct-v0.3” this repo word for me .. you can try with this .. and i have tried and cleared my trial version else this workout for me