ValidationError: 1 validation error for RetrievalQA retriever

[F-Choudhary]

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Example Code

#Initializing the Pinecone

from langchain_pinecone import PineconeVectorStore
from pinecone import Pinecone
from langchain.chains import RetrievalQA 

pc = Pinecone(api_key="1e094edb-8730-46a7-8178-615a08ca303b")
index = pc.Index("medical-chatbot")

# index_name="medical-chatbot"

#Creating Embeddings for Each of The Text Chunks & storing
docsearch=PineconeVectorStore.from_texts([t.page_content for t in text_chunks], embeddings, index_name='medical-chatbot')

#If we already have an index we can load it like this
index_name="medical-chatbot"
docsearch=PineconeVectorStore.from_existing_index(index_name, embeddings)

query = "What are Salivary Gland Disease"

docs=docsearch.similarity_search(query, k=3)

print("Result", docs)

prompt_template="""
Use the following pieces of information to answer the user's question.
If you don't know the answer, just say that you don't know, don't try to make up an answer.

Context: {context}
Question: {question}

Only return the helpful answer below and nothing else.
Helpful answer:
"""

PROMPT=PromptTemplate(template=prompt_template, input_variables=["context", "question"])
chain_type_kwargs={"prompt": PROMPT}

llm=CTransformers(model="../model/llama-2-7b-chat.ggmlv3.q4_0.bin",
                  model_type="llama",
                  config={'max_new_tokens':512,
                          'temperature':0.8})


# retriever = docsearch.as_retriever()


qa=RetrievalQA.from_chain_type(
    llm=llm, 
    chain_type="stuff", 
    retriever=docsearch.as_retriever(search_kwargs={'k': 2}),
    return_source_documents=True, 
    chain_type_kwargs=chain_type_kwargs)

Description

I am using pinecone vector database for storing the veactors of the text chunks, then I am imlementing similarity search to get similar document after that I am loading my llm model and using the retrieveralQA chain I am trying to use "from langchain.chains import RetrievalQA " for my chatbot but I am always getting a validation error in RetrieveralQA.from_chain_type.

this is the error Message

ValidationError Traceback (most recent call last)
Cell In[49], line 5
1 from langchain.chains import RetrievalQA
2 # retriever = docsearch.as_retriever()
----> 5 qa=RetrievalQA.from_chain_type(
6 llm=llm,
7 chain_type=“stuff”,
8 retriever=docsearch.as_retriever(search_kwargs={‘k’: 2}),
9 return_source_documents=True,
10 chain_type_kwargs=chain_type_kwargs)

File c:\End-to-end-Medical-Chatbot-using-Llama2-main\myenv\lib\site-packages\langchain\chains\retrieval_qa\base.py:95, in BaseRetrievalQA.from_chain_type(cls, llm, chain_type, chain_type_kwargs, **kwargs)
91 _chain_type_kwargs = chain_type_kwargs or {}
92 combine_documents_chain = load_qa_chain(
93 llm, chain_type=chain_type, **_chain_type_kwargs
94 )
—> 95 return cls(combine_documents_chain=combine_documents_chain, **kwargs)

File c:\End-to-end-Medical-Chatbot-using-Llama2-main\myenv\lib\site-packages\langchain\load\serializable.py:74, in Serializable.init(self, **kwargs)
73 def init(self, **kwargs: Any) → None:
—> 74 super().init(**kwargs)
75 self._lc_kwargs = kwargs

File c:\End-to-end-Medical-Chatbot-using-Llama2-main\myenv\lib\site-packages\pydantic\main.py:341, in pydantic.main.BaseModel.init()

ValidationError: 1 validation error for RetrievalQA
retriever
Can’t instantiate abstract class BaseRetriever with abstract methods _aget_relevant_documents, _get_relevant_documents (type=type_error)

System Info

langchain==0.1.16
langchain-chroma==0.1.0
langchain-community==0.0.34
langchain-core==0.1.46
langchain-pinecone==0.1.0
langchain-text-splitters==0.0.1
langchainplus-sdk==0.0.20
langsmith==0.1.51
sentence-transformers==2.2.2
sentencepiece==0.2.0
transformers==4.40.1

python 3.9.0

system windows 10

[dosubot[bot]]

The error you're encountering with RetrievalQA.from_chain_type is likely due to a mismatch or incorrect usage of parameters in your code. Specifically, the error message indicates an issue with instantiating the BaseRetriever class, which suggests that the docsearch.as_retriever() method might not be returning an object that meets the expected interface for a retriever in the RetrievalQA setup.

To address this, ensure that the object returned by docsearch.as_retriever() correctly implements the required methods (_aget_relevant_documents and _get_relevant_documents). This might involve checking the documentation or source code of the PineconeVectorStore class to confirm it's compatible with RetrievalQA's expectations.

Additionally, double-check that all parameters passed to RetrievalQA.from_chain_type are correct and that there are no typos or incorrect parameter names. The error message you've shared doesn't directly point to a specific parameter issue, but it's always a good practice to verify.

If you're following examples or documentation, ensure they are up to date with the version of langchain you're using, as APIs can change between versions. If the problem persists, consider reaching out to the langchain community or checking if there are similar issues reported that might offer a solution.

Lastly, ensure your environment is correctly set up with all necessary dependencies and that there are no conflicts between package versions that might cause unexpected behavior.

Details

Sources

• ValidationError: 1 validation error for RetrievalQA
• ValidationError: 1 validation error for RetrievalQA retriever
• Issue: Dynamically select from multiple retrievers
• DOC: Quickstart Code Fails for Retrieval Chain
• libs/langchain/langchain/chains/retrieval_qa/base.py

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[bushrashahzad]

Can you please send code?