mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
9ffca3b92a
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'
```
97 lines
3.2 KiB
Python
97 lines
3.2 KiB
Python
from langchain.chat_models import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnablePassthrough
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skeleton_generator_template = """[User:] You’re an organizer responsible for only \
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giving the skeleton (not the full content) for answering the question.
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Provide the skeleton in a list of points (numbered 1., 2., 3., etc.) to answer \
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the question. \
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Instead of writing a full sentence, each skeleton point should be very short \
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with only 3∼5 words. \
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Generally, the skeleton should have 3∼10 points. Now, please provide the skeleton \
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for the following question.
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{question}
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Skeleton:
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[Assistant:] 1."""
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skeleton_generator_prompt = ChatPromptTemplate.from_template(
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skeleton_generator_template
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)
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skeleton_generator_chain = (
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skeleton_generator_prompt | ChatOpenAI() | StrOutputParser() | (lambda x: "1. " + x)
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)
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point_expander_template = """[User:] You’re responsible for continuing \
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the writing of one and only one point in the overall answer to the following question.
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{question}
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The skeleton of the answer is
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{skeleton}
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Continue and only continue the writing of point {point_index}. \
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Write it **very shortly** in 1∼2 sentence and do not continue with other points!
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[Assistant:] {point_index}. {point_skeleton}"""
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point_expander_prompt = ChatPromptTemplate.from_template(point_expander_template)
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point_expander_chain = RunnablePassthrough.assign(
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continuation=point_expander_prompt | ChatOpenAI() | StrOutputParser()
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) | (lambda x: x["point_skeleton"].strip() + " " + x["continuation"])
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def parse_numbered_list(input_str):
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"""Parses a numbered list into a list of dictionaries
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Each element having two keys:
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'index' for the index in the numbered list, and 'point' for the content.
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"""
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# Split the input string into lines
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lines = input_str.split("\n")
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# Initialize an empty list to store the parsed items
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parsed_list = []
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for line in lines:
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# Split each line at the first period to separate the index from the content
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parts = line.split(". ", 1)
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if len(parts) == 2:
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# Convert the index part to an integer
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# and strip any whitespace from the content
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index = int(parts[0])
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point = parts[1].strip()
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# Add a dictionary to the parsed list
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parsed_list.append({"point_index": index, "point_skeleton": point})
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return parsed_list
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def create_list_elements(_input):
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skeleton = _input["skeleton"]
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numbered_list = parse_numbered_list(skeleton)
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for el in numbered_list:
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el["skeleton"] = skeleton
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el["question"] = _input["question"]
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return numbered_list
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def get_final_answer(expanded_list):
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final_answer_str = "Here's a comprehensive answer:\n\n"
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for i, el in enumerate(expanded_list):
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final_answer_str += f"{i+1}. {el}\n\n"
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return final_answer_str
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class ChainInput(BaseModel):
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question: str
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chain = (
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RunnablePassthrough.assign(skeleton=skeleton_generator_chain)
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| create_list_elements
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| point_expander_chain.map()
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| get_final_answer
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).with_types(input_type=ChainInput)
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