I'm unable to use the pdf_bot using AWS claudev2
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Assessment
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Newbie friendliness
- 35/100
Research direction
Start with pdf_bot.py at lines 72 and 95, then inspect the AWS and embedding configuration used by the Neo4jVector call. Reproduce the PDF bot with the reported .env values and review the Windows docker-compose.yml changes mentioned in the issue. Done means the app can invoke the configured embedding model without the AccessDeniedException.
Written by the indexing model from the issue text.
Description
ValueError: Error raised by inference endpoint: An error occurred (AccessDeniedException) when calling the InvokeModel operation: You don't have access to the model with the specified model ID
File "/usr/local/lib/python3.11/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 535, in _run_script
exec(code, module.dict)
File "/app/pdf_bot.py", line 95, in
main()
File "/app/pdf_bot.py", line 72, in main
vectorstore = Neo4jVector.from_texts(
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/langchain_community/vectorstores/neo4j_vector.py", line 679, in from_texts
embeddings = embedding.embed_documents(list(texts))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/langchain_community/embeddings/bedrock.py", line 169, in embed_documents
response = self._embedding_func(text)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.11/site-packages/langchain_community/embeddings/bedrock.py", line 150, in _embedding_func
raise ValueError(f"Error raised by inference endpoint: {e}")
I'm facing this error when I'm running the pdf_bot app. I've got the access to the claudev2 model from AWS still it's showing the above mentioned error.
[P.S I'm using Windows, and as discussed in #123 I've made some changes in my docker-compose.yml]
Here's the copy of the .env file -
OLLAMA_BASE_URL=http://host.docker.internal:11434
NEO4J_URI=neo4j://database:7687
NEO4J_USERNAME=neo4j
NEO4J_PASSWORD=password
LLM=claudev2
EMBEDDING_MODEL=aws
AWS_ACCESS_KEY_ID=[I've have provided it but I can't share this]
AWS_SECRET_ACCESS_KEY=[I've have provided it but I can't share this]
AWS_DEFAULT_REGION=us-east-1
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