Open gnanaprakash-ravi opened 3 weeks ago
I have tried with .invoke method from the retriever as well.
File "C:\Users\inrgna00\OneDrive - Ingram Micro\From Downloads\RAG\1mistral.py", line 143, in <module>
docs = retriever.invoke(user_input)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\inrgna00\AppData\Local\miniconda3\envs\rag\Lib\site-packages\langchain_core\retrievers.py", line 194, in invoke
return self.get_relevant_documents(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\inrgna00\AppData\Local\miniconda3\envs\rag\Lib\site-packages\langchain_core\_api\deprecation.py", line 148, in warning_emitting_wrapper
return wrapped(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\inrgna00\AppData\Local\miniconda3\envs\rag\Lib\site-packages\langchain_core\retrievers.py", line 323, in get_relevant_documents
raise e
File "C:\Users\inrgna00\AppData\Local\miniconda3\envs\rag\Lib\site-packages\langchain_core\retrievers.py", line 316, in get_relevant_documents
result = self._get_relevant_documents(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\inrgna00\AppData\Local\miniconda3\envs\rag\Lib\site-packages\langchain_core\vectorstores.py", line 696, in _get_relevant_documents
docs = self.vectorstore.similarity_search(query, **self.search_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\inrgna00\AppData\Local\miniconda3\envs\rag\Lib\site-packages\langchain_community\vectorstores\faiss.py", line 530, in similarity_search
docs_and_scores = self.similarity_search_with_score(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\inrgna00\AppData\Local\miniconda3\envs\rag\Lib\site-packages\langchain_community\vectorstores\faiss.py", line 402, in similarity_search_with_score
embedding = self._embed_query(query)
^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\inrgna00\AppData\Local\miniconda3\envs\rag\Lib\site-packages\langchain_community\vectorstores\faiss.py", line 154, in _embed_query
return self.embedding_function.embed_query(text)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\inrgna00\AppData\Local\miniconda3\envs\rag\Lib\site-packages\langchain_nvidia_ai_endpoints\embeddings.py", line 156, in embed_query
return self._embed([text], model_type=self.model_type or "query")[0]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:\Users\inrgna00\AppData\Local\miniconda3\envs\rag\Lib\site-packages\langchain_nvidia_ai_endpoints\embeddings.py", line 139, in _embed
if self.truncate:
^^^^^^^^^^^^^
AttributeError: 'NVIDIAEmbeddings' object has no attribute 'truncate'
Hi, I am working on 5_mins_rag_no_gpu. Facing this error: AttributeError: 'NVIDIAEmbeddings' object has no attribute 'truncate' on this version: langchain-nvidia-ai-endpoints==0.1.2
Detailed error:
I have tried the version: langchain-nvidia-ai-endpoints==0.0.19 I had this error: