azure-cognitive-serviceslangchainazure-openaicontent-based-retrieval

Langchain and Azure cognitive search - ImportError - cannot import name 'Vector' from 'azure.search.documents.models'


I am seeing a "cannot import name 'Vector' from azure.search.documents.models" error when I invoke my chain. Origin of my error is line 434 in lanchain/vectorstores/azuresearch.py (from azure.search.documents.models import Vector)

this is the relevant code snippet, I get the import error when I execute rag_chain.invoke(question)

from langchain.schema.runnable import RunnablePassthrough
from langchain.prompts import ChatPromptTemplate
from langchain.chat_models.azure_openai import AzureChatOpenAI

question = "my question.."

# vector_store is initialized using AzureSearch(), not including that snippet here
retriever = vector_store.as_retriever()

template = ''' 
Answer the question based on the following context: 
{context}

Question: {question} 
'''

prompt = ChatPromptTemplate.from_template(template=template)

llm = AzureChatOpenAI( deployment_name='MY_DEPLOYMENT_NAME', model_name='MY_MODEL', openai_api_base=MY_AZURE_OPENAI_ENDPOINT, openai_api_key=MY_AZURE_OPENAI_KEY, openai_api_version='2023-05-15', openai_api_type='azure' )

rag_chain = {'context' : retriever, 'question' : RunnablePassthrough} | prompt | llm 
rag_chain.invoke(question)

my package versions


Solution

  • "cannot import name 'Vector' from azure.search.documents.models" error when I invoke my chain. Origin of my error is line 434 in lanchain/vectorstores/azuresearch.py (from azure.search.documents.models import Vector)

    According to this document you need to install the azure-search-documents==11.4.0b8 for vector stores of azure search.

    Now you can use the below code that I tested in my environment:

    Code:

    from langchain.prompts import ChatPromptTemplate
    from langchain.chat_models.azure_openai import AzureChatOpenAI
    from langchain.vectorstores.azuresearch import AzureSearch
    from langchain.embeddings import OpenAIEmbeddings
    from langchain.schema import StrOutputParser
    from langchain.schema.runnable import RunnablePassthrough
    import os
    
    
    model = "xxxxx"
    chunk_size = 1
    
    os.environ["OPENAI_API_TYPE"] = "azure"
    os.environ["OPENAI_API_BASE"] = "xxxx"
    os.environ["OPENAI_API_KEY"] = "xxxx"
    os.environ["OPENAI_API_VERSION"] = "2023-05-15"
    
    MY_AZURE_OPENAI_ENDPOINT="xxxx"
    OPENAIKEY="xxxxx"
    vector_store_address = "xxxx"
    vector_store_password = "xxxxx"
    index_name = "sample-index"
    embeddings = OpenAIEmbeddings(deployment=model, chunk_size=chunk_size)
    vector_store = AzureSearch(
        azure_search_endpoint=vector_store_address,
        azure_search_key=vector_store_password,
        index_name=index_name,
        embedding_function=embeddings.embed_query,
    )
    
    retriever = vector_store.as_retriever()
    
    
    template = """Answer the question based only on the following context:
    
    {context}
    
    Question: {question}
    """
    prompt = ChatPromptTemplate.from_template(template)
    
    llm = AzureChatOpenAI( deployment_name='gpt-35-turbo', openai_api_base=MY_AZURE_OPENAI_ENDPOINT, openai_api_key=OPENAIKEY, openai_api_version='2023-05-15', openai_api_type='azure' )
    
    def format_docs(docs):
        return "\n\n".join([d.page_content for d in docs])
    
    
    chain = (
        {"context": retriever | format_docs, "question": RunnablePassthrough()}
        | prompt
        | llm
        | StrOutputParser()
    )
    print(chain.invoke("What did the president say about technology?"))
    

    Output:

    > The president mentioned the importance of investing in emerging
    > technologies and American manufacturing to compete with China and
    > other competitors. He also mentioned the role of computer chips in
    > powering everyday technology and the potential for Intel to increase
    > its investment in manufacturing from $20 billion to $100 billion.
    

    enter image description here

    Reference: Langchain-Full-Course/langchain_expressions.ipynb at main · Coding-Crashkurse/Langchain-Full-Course · GitHub