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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 QAWithSourcesChain
from langchain.chains import TransformChain, SequentialChain
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from anyqa.prompts import answer_prompt, reformulation_prompt
from anyqa.custom_retrieval_chain import CustomRetrievalQAWithSourcesChain
def load_qa_chain_with_docs(llm):
"""Load a QA chain with documents.
Useful when you already have retrieved docs
To be called with this input
```
output = chain({
"question":query,
"audience":"experts scientists",
"docs":docs,
"language":"English",
})
```
"""
qa_chain = load_combine_documents_chain(llm)
chain = QAWithSourcesChain(
input_docs_key="docs",
combine_documents_chain=qa_chain,
return_source_documents=True,
)
return chain
def load_combine_documents_chain(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)
return qa_chain
def load_qa_chain_with_text(llm):
prompt = PromptTemplate(
template=answer_prompt,
input_variables=["question", "audience", "language", "summaries"],
)
qa_chain = LLMChain(llm=llm, prompt=prompt)
return qa_chain
def load_qa_chain(retriever, llm_reformulation, llm_answer):
reformulation_chain = load_reformulation_chain(llm_reformulation)
answer_chain = load_qa_chain_with_retriever(retriever, llm_answer)
qa_chain = SequentialChain(
chains=[reformulation_chain, answer_chain],
input_variables=["query", "audience"],
output_variables=["answer", "question", "language", "source_documents"],
return_all=True,
verbose=True,
)
return qa_chain
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"]
print("output", output)
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_qa_chain_with_retriever(retriever, llm):
qa_chain = load_combine_documents_chain(llm)
# 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 sources, you may want to ask a more specific question.**",
)
return answer_chain
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