LLMChain with critique using Q&A

[alexmarinho1986]

Hello team !

I cant find any doc about this scenario:

I have a pdf file which i can do some queries using RetrievalQA.

I was wondering if its possible to use llmchain with RetrievalQA. My goal is when the qa answers the query, another llm validates or improve the answer based on the same pdf.

Piece of code:

loader = PyPDFLoader(url)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
texts = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
docsearch = FAISS.from_documents(texts, embeddings)

llm = ChatOpenAI(
     model_name="gpt-3.5-turbo", 
#    model_name="gpt-4",
    temperature=0,
    max_tokens=1900,
    openai_api_key="XXX"
)

qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=docsearch.as_retriever(), chain_type_kwargs=chain_type_kwargs)

[dosubot[bot]]

🤖

Hello,

Yes, it is indeed possible to use LangChain with RetrievalQA in a way that when the QA answers a query, another language model validates or improves the answer based on the same PDF file.

The LangChain framework is designed to be flexible and customizable, allowing developers to chain together different language models to perform complex tasks. In your case, you can use one language model to answer a query using the RetrievalQA method, and then pass the answer to another language model for validation or improvement.

Here's a simplified example of how you might do this:

from langchain.chains.qa_with_sources.map_reduce_prompt import QUESTION_PROMPT, COMBINE_PROMPT, EXAMPLE_PROMPT

# First, use RetrievalQA to answer the query
context = "your PDF content here"
question = "your question here"
prompt = QUESTION_PROMPT.render(context=context, question=question)
answer = RetrievalQA_model(prompt)

# Then, pass the answer to another language model for validation or improvement
improved_answer = Another_model(answer)

# Finally, combine the improved answer with the original sources
summaries = "your extracted parts of the document here"
final_prompt = COMBINE_PROMPT.render(summaries=summaries, question=question)
final_answer = Final_model(final_prompt)

Please note that this is a simplified example and the actual implementation may vary depending on the specific models you are using and how you want to structure your application.

The LangChain framework handles the chaining of different language models through the concept of "Chains". Chains are reusable components that encode a sequence of calls to components like models, document retrievers, other Chains, etc., and provide a simple interface to this sequence.

Different types of Chains are defined in the LangChain framework, each representing a different sequence of calls to language models or other components. For example, LLMChain, MapReduceChain, RouterChain, etc. are all different types of Chains. Each Chain type can be used to create a specific sequence of calls to language models or other components.

In your shared context, we see two additional types of Chains: SequentialChain and SimpleSequentialChain. The SequentialChain is a Chain where the outputs of one chain feed directly into the next. It validates that the correct inputs exist for all chains and calls each chain in sequence, updating the known values with the outputs of each chain.

The SimpleSequentialChain is a simpler version of the SequentialChain where the outputs of one step feed directly into the next. It validates that all chains have a single input and output and calls each chain in sequence, updating the input with the output of each chain.

Here is an example of how you might use a Chain in LangChain:

from langchain.chains.llm import LLMChain

# Create an instance of the LLMChain
chain = LLMChain()

# Use the chain to process some data
result = chain.process(data)

In this example, the LLMChain might represent a sequence of calls to a language model, a document retriever, and another language model. The exact sequence of calls would be defined in the implementation of the LLMChain class. Similarly, the SequentialChain and SimpleSequentialChain would have their own specific sequences of calls defined in their implementations.

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

Best,
Dosu

Sources

• libs/langchain/langchain/chains/qa_with_sources/map_reduce_prompt.py
• libs/langchain/langchain/chains/init.py
• libs/langchain/langchain/chains/sequential.py

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