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langchain/templates/skeleton-of-thought/skeleton_of_thought/chain.py

97 lines
3.2 KiB
Python

from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.pydantic_v1 import BaseModel
from langchain.schema.output_parser import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough
skeleton_generator_template = """[User:] Youre an organizer responsible for only \
giving the skeleton (not the full content) for answering the question.
Provide the skeleton in a list of points (numbered 1., 2., 3., etc.) to answer \
the question. \
Instead of writing a full sentence, each skeleton point should be very short \
with only 35 words. \
Generally, the skeleton should have 310 points. Now, please provide the skeleton \
for the following question.
{question}
Skeleton:
[Assistant:] 1."""
skeleton_generator_prompt = ChatPromptTemplate.from_template(
skeleton_generator_template
)
skeleton_generator_chain = (
skeleton_generator_prompt | ChatOpenAI() | StrOutputParser() | (lambda x: "1. " + x)
)
point_expander_template = """[User:] Youre responsible for continuing \
the writing of one and only one point in the overall answer to the following question.
{question}
The skeleton of the answer is
{skeleton}
Continue and only continue the writing of point {point_index}. \
Write it **very shortly** in 12 sentence and do not continue with other points!
[Assistant:] {point_index}. {point_skeleton}"""
point_expander_prompt = ChatPromptTemplate.from_template(point_expander_template)
point_expander_chain = RunnablePassthrough.assign(
continuation=point_expander_prompt | ChatOpenAI() | StrOutputParser()
) | (lambda x: x["point_skeleton"].strip() + " " + x["continuation"])
def parse_numbered_list(input_str):
"""Parses a numbered list into a list of dictionaries
Each element having two keys:
'index' for the index in the numbered list, and 'point' for the content.
"""
# Split the input string into lines
lines = input_str.split("\n")
# Initialize an empty list to store the parsed items
parsed_list = []
for line in lines:
# Split each line at the first period to separate the index from the content
parts = line.split(". ", 1)
if len(parts) == 2:
# Convert the index part to an integer
# and strip any whitespace from the content
index = int(parts[0])
point = parts[1].strip()
# Add a dictionary to the parsed list
parsed_list.append({"point_index": index, "point_skeleton": point})
return parsed_list
def create_list_elements(_input):
skeleton = _input["skeleton"]
numbered_list = parse_numbered_list(skeleton)
for el in numbered_list:
el["skeleton"] = skeleton
el["question"] = _input["question"]
return numbered_list
def get_final_answer(expanded_list):
final_answer_str = "Here's a comprehensive answer:\n\n"
for i, el in enumerate(expanded_list):
final_answer_str += f"{i+1}. {el}\n\n"
return final_answer_str
class ChainInput(BaseModel):
question: str
chain = (
RunnablePassthrough.assign(skeleton=skeleton_generator_chain)
| create_list_elements
| point_expander_chain.map()
| get_final_answer
).with_types(input_type=ChainInput)