Langchain WeaviateHybridSearchRetriever with filters?

Hi @Just_Guide7361 !!

Thanks for pointing it out!!

I will take the opportunity and also write a recipe using the multi tenancy feature with langchain.

here is a working code using create_retrieval_chain (I will update the recipe later today):

# ...
from weaviate.classes.query import Filter

# client = weaviate.connect_to_weaviate_cloud(...)

embeddings = OpenAIEmbeddings()
db = WeaviateVectorStore.from_documents([], embeddings, client=client, index_name="WikipediaLangChain")

source_file = "brazil-wikipedia-article-text.pdf"
#source_file = "netherlands-wikipedia-article-text.pdf"
where_filter = Filter.by_property("source").equal(source_file)

# we want our retriever to filter the results
retriever = db.as_retriever(search_kwargs={"filters": where_filter})

system_prompt = (
    "You are an assistant for question-answering tasks. "
    "Use the following pieces of retrieved context to answer "
    "the question. If you don't know the answer, say that you "
    "don't know. Use three sentences maximum and keep the "
    "answer concise."
    "\n\n"
    "{context}"
)

prompt = ChatPromptTemplate.from_messages(
    [
        ("system", system_prompt),
        ("human", "{input}"),
    ]
)

llm = ChatOpenAI(model="gpt-4o-mini")
question_answer_chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(retriever, question_answer_chain)

response = rag_chain.invoke({"input": "What is he traditional food of this country?"})
print(response["answer"])

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Thanks and hope you are enjoying your “Weaviate journey”!!