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from operator import itemgetter
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough, RunnableLambda
from langchain.prompts.prompt import PromptTemplate
from langchain.schema import format_document
from climateqa.engine.reformulation import make_reformulation_chain
from climateqa.engine.prompts import answer_prompt_template
from climateqa.engine.utils import pass_values, flatten_dict
DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template(template="{page_content}")
def _combine_documents(
docs, document_prompt=DEFAULT_DOCUMENT_PROMPT, sep="\n\n"
):
doc_strings = [f"Doc {i+1}: " + format_document(doc, document_prompt) for i,doc in enumerate(docs)]
return sep.join(doc_strings)
def make_rag_chain(retriever,llm):
# Construct the prompt
prompt = ChatPromptTemplate.from_template(answer_prompt_template)
# ------- CHAIN 0 - Reformulation
reformulation_chain = make_reformulation_chain(llm)
reformulation = (
{"reformulation":reformulation_chain,**pass_values(["audience","query"])}
| RunnablePassthrough()
| flatten_dict
)
# ------- CHAIN 1
# Retrieved documents
find_documents = {
"docs": itemgetter("question") | retriever,
**pass_values(["question","audience","language","query"])
} | RunnablePassthrough()
# ------- CHAIN 2
# Construct inputs for the llm
input_documents = {
"context":lambda x : _combine_documents(x["docs"]),
**pass_values(["question","audience","language"])
}
# Generate the answer
answer = {
"answer": input_documents | prompt | llm | StrOutputParser(),
**pass_values(["question","audience","language","query","docs"])
}
# ------- FINAL CHAIN
# Build the final chain
rag_chain = reformulation | find_documents | answer
return rag_chain
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