how to improve question answering application using LLM

[IamExperimenting]

Hi,

I have built Question Answering application using Llama2 model, sometimes model gives wrong answer or provide answer on it own. I also create prompt template not to make up answer on its own. But its still doing the same.

Has anyone face such similar issue? can you someone guide me how to solve this?

how to improve it?

Also, can someone recommend anyways to improve "search" because sometime when I ask question it is giving wrong index from FAISS index vector database.

[dosubot[bot]]

Answer generated by a 🤖

Answer

Hi,

Based on your description, it seems like you're facing two main issues: improving the accuracy of your Question Answering application using the Llama2 model and enhancing the search results from the FAISS index vector database. Here are some suggestions that might help:

1. Customize the prompt: You can customize the prompt in the RetrievalQA chain to guide the LLM in generating more accurate answers. Here's how you can do it:

from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate

template = """Use the following pieces of context to answer the question at the end. 
If you don't know the answer, just say that you don't know, don't try to make up an answer. 
Use three sentences maximum and keep the answer as concise as possible. 
Always say "thanks for asking!" at the end of the answer. 
{context}
Question: {question}
Helpful Answer:"""
QA_CHAIN_PROMPT = PromptTemplate.from_template(template)

llm = Ollama(model="llama2", temperature=0)
qa_chain = RetrievalQA.from_chain_type(
    llm,
    retriever=vectorstore.as_retriever(),
    chain_type_kwargs={"prompt": QA_CHAIN_PROMPT}
)
result = qa_chain({"query": question})
result["result"]

2. Return source documents: You can return the full set of retrieved documents used for answer distillation. This can help you understand the basis of the generated answer and improve the retrieval process if needed.

from langchain.chains import RetrievalQA
qa_chain = RetrievalQA.from_chain_type(llm,retriever=vectorstore.as_retriever(),
                                       return_source_documents=True)
result = qa_chain({"query": question})
print(len(result['source_documents']))
result['source_documents'][0]

3. Customize retrieved document processing: You can customize how retrieved documents are fed to an LLM for answer distillation. For example, you can use the stuff chain type which simply "stuffs" all retrieved documents into the prompt.

qa_chain = RetrievalQA.from_chain_type(llm,retriever=vectorstore.as_retriever(),
                                       chain_type="stuff")
result = qa_chain({"query": question})

4. Use a Conversation Buffer Memory: To hold a conversation and refer to past interactions, you can use a Memory buffer. This can help in resolving pronouns or references in the questions.

from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationalRetrievalChain

memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
retriever = vectorstore.as_retriever()
chat = ConversationalRetrievalChain.from_llm(llm, retriever=retriever, memory=memory)

5. Use MultiQueryRetriever: This generates variants of the input question to improve retrieval.

from langchain.chat_models import ChatOpenAI
from langchain.retrievers.multi_query import MultiQueryRetriever

logging.basicConfig()
logging.getLogger('langchain.retrievers.multi_query').setLevel(logging.INFO)

retriever_from_llm = MultiQueryRetriever.from_llm(retriever=vectorstore.as_retriever(),
                                                  llm=Ollama(temperature=0))
unique_docs = retriever_from_llm.get_relevant_documents(query=question)
len(unique_docs)

6. Use different chain types: In the context shared, different chain types like stuff, map_reduce, refine, and map_rerank are mentioned. Each chain type has a different approach to answer distillation and can be used based on the specific requirements of your application. For example, the stuff chain type simply "stuffs" all retrieved documents into the prompt, the map_reduce chain type maps the question to each document and reduces the answers to a single answer, the refine chain type refines the answer iteratively, and the map_rerank chain type maps the question to each document, reranks the answers, and reduces them to a single answer.

7. Tune the Llama2 model parameters: In the context shared, the Llama2 model has several parameters that can be tuned to improve the accuracy of the model. These include mirostat, mirostat_eta, mirostat_tau, num_ctx, num_gpu, num_thread, repeat_last_n, repeat_penalty, temperature, stop, tfs_z, top_k, and top_p. Each of these parameters can be adjusted to optimize the performance of the model for your specific application.

Remember to replace Ollama with your Llama2 model and vectorstore with your FAISS index vector database.

I hope this helps! If you have any other questions or need further clarification, feel free to ask.

Best,
Dosu

Sources

• docs/extras/use_cases/question_answering/index.mdx
• docs/snippets/modules/chains/additional/question_answering.mdx
• libs/langchain/langchain/llms/ollama.py
• libs/langchain/tests/integration_tests/chains/test_retrieval_qa.py
• libs/langchain/langchain/chains/retrieval_qa/base.py
• libs/langchain/langchain/chains/qa_with_sources/retrieval.py

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