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# https://python.langchain.com/docs/modules/chains/how_to/custom_chain
# Including reformulation of the question in the chain
import json
from langchain import PromptTemplate, LLMChain
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.chains import TransformChain, SequentialChain
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from climateqa.prompts import answer_prompt, reformulation_prompt,audience_prompts
from climateqa.custom_retrieval_chain import CustomRetrievalQAWithSourcesChain
def load_reformulation_chain(llm):
prompt = PromptTemplate(
template = reformulation_prompt,
input_variables=["query"],
)
reformulation_chain = LLMChain(llm = llm,prompt = prompt,output_key="json")
# Parse the output
def parse_output(output):
query = output["query"]
json_output = json.loads(output["json"])
question = json_output.get("question", query)
language = json_output.get("language", "English")
return {
"question": question,
"language": language,
}
transform_chain = TransformChain(
input_variables=["json"], output_variables=["question","language"], transform=parse_output
)
reformulation_chain = SequentialChain(chains = [reformulation_chain,transform_chain],input_variables=["query"],output_variables=["question","language"])
return reformulation_chain
def load_answer_chain(retriever,llm):
prompt = PromptTemplate(template=answer_prompt, input_variables=["summaries", "question","audience","language"])
qa_chain = load_qa_with_sources_chain(llm, chain_type="stuff",prompt = prompt)
# This could be improved by providing a document prompt to avoid modifying page_content in the docs
# See here https://github.com/langchain-ai/langchain/issues/3523
answer_chain = CustomRetrievalQAWithSourcesChain(
combine_documents_chain = qa_chain,
retriever=retriever,
return_source_documents = True,
verbose = True,
fallback_answer="**⚠️ No relevant passages found in the climate science reports (IPCC and IPBES), you may want to ask a more specific question (specifying your question on climate issues).**",
)
return answer_chain
def load_climateqa_chain(retriever,llm_reformulation,llm_answer):
reformulation_chain = load_reformulation_chain(llm_reformulation)
answer_chain = load_answer_chain(retriever,llm_answer)
climateqa_chain = SequentialChain(
chains = [reformulation_chain,answer_chain],
input_variables=["query","audience"],
output_variables=["answer","question","language","source_documents"],
return_all = True,
verbose = True,
)
return climateqa_chain
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