Archives/langchain
2
0
Fork 1
mirror of https://github.com/hwchase17/langchain synced 2024-11-08 07:10:35 +00:00
Code Issues Packages Projects Releases Wiki Activity
21e0df937f
langchain/templates/skeleton-of-thought/skeleton_of_thought/chain.py

97 lines
3.2 KiB
Python
Raw Normal View History

docs, community[patch], experimental[patch], langchain[patch], cli[pa… (#15412) …tch]: import models from community ran ```bash git grep -l 'from langchain\.chat_models' | xargs -L 1 sed -i '' "s/from\ langchain\.chat_models/from\ langchain_community.chat_models/g" git grep -l 'from langchain\.llms' | xargs -L 1 sed -i '' "s/from\ langchain\.llms/from\ langchain_community.llms/g" git grep -l 'from langchain\.embeddings' | xargs -L 1 sed -i '' "s/from\ langchain\.embeddings/from\ langchain_community.embeddings/g" git checkout master libs/langchain/tests/unit_tests/llms git checkout master libs/langchain/tests/unit_tests/chat_models git checkout master libs/langchain/tests/unit_tests/embeddings/test_imports.py make format cd libs/langchain; make format cd ../experimental; make format cd ../core; make format ```
2024-01-02 20:32:16 +00:00
from langchain_community.chat_models import ChatOpenAI
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' ```
2023-12-12 00:49:10 +00:00
from langchain_core.output_parsers import StrOutputParser
templates: fix deps (#15439)
2024-01-03 21:28:05 +00:00
from langchain_core.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' ```
2023-12-12 00:49:10 +00:00
from langchain_core.pydantic_v1 import BaseModel
from langchain_core.runnables import RunnablePassthrough
add skeleton of thought (#13883)
2023-11-27 00:31:41 +00:00
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)
Reference in New Issue Copy Permalink