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

from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnablePassthrough

skeleton_generator_template = """[User:] You’re 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 3∼5 words. \
Generally, the skeleton should have 3∼10 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:] You’re 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 1∼2 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)
-												add skeleton of thought (#13883)


											
										
										
											10 months ago
+								from langchain.chat_models import ChatOpenAI
 								from langchain.prompts import ChatPromptTemplate
-												docs[patch], templates[patch]: Import from core (#14575)

Update imports to use core for the low-hanging fruit changes. Ran
following

```bash
git grep -l 'langchain.schema.runnable' {docs,templates,cookbook}  | xargs sed -i '' 's/langchain\.schema\.runnable/langchain_core.runnables/g'
git grep -l 'langchain.schema.output_parser' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.output_parser/langchain_core.output_parsers/g'
git grep -l 'langchain.schema.messages' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.messages/langchain_core.messages/g'
git grep -l 'langchain.schema.chat_histry' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.chat_history/langchain_core.chat_history/g'
git grep -l 'langchain.schema.prompt_template' {docs,templates,cookbook} | xargs sed -i '' 's/langchain\.schema\.prompt_template/langchain_core.prompts/g'
git grep -l 'from langchain.pydantic_v1' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.pydantic_v1/from langchain_core.pydantic_v1/g'
git grep -l 'from langchain.tools.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.tools\.base/from langchain_core.tools/g'
git grep -l 'from langchain.chat_models.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.chat_models.base/from langchain_core.language_models.chat_models/g'
git grep -l 'from langchain.llms.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.llms\.base\ /from langchain_core.language_models.llms\ /g'
git grep -l 'from langchain.embeddings.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.embeddings\.base/from langchain_core.embeddings/g'
git grep -l 'from langchain.vectorstores.base' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.vectorstores\.base/from langchain_core.vectorstores/g'
git grep -l 'from langchain.agents.tools' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.agents\.tools/from langchain_core.tools/g'
git grep -l 'from langchain.schema.output' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.output\ /from langchain_core.outputs\ /g'
git grep -l 'from langchain.schema.embeddings' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.embeddings/from langchain_core.embeddings/g'
git grep -l 'from langchain.schema.document' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.document/from langchain_core.documents/g'
git grep -l 'from langchain.schema.agent' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.agent/from langchain_core.agents/g'
git grep -l 'from langchain.schema.prompt ' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.prompt\ /from langchain_core.prompt_values /g'
git grep -l 'from langchain.schema.language_model' {docs,templates,cookbook} | xargs sed -i '' 's/from langchain\.schema\.language_model/from langchain_core.language_models/g'


```
											
										
										
											9 months ago
+								from langchain_core.output_parsers import StrOutputParser
 								from langchain_core.pydantic_v1 import BaseModel
 								from langchain_core.runnables import RunnablePassthrough
-												add skeleton of thought (#13883)


											
										
										
											10 months ago
 								skeleton_generator_template = """[User:] You’re 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 3∼5 words. \
 								Generally, the skeleton should have 3∼10 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:] You’re 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 1∼2 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)