From c28d5ec3ba1d88816d75f70a720e79354f265818 Mon Sep 17 00:00:00 2001 From: Harrison Chase Date: Mon, 14 Nov 2022 20:58:34 -0800 Subject: [PATCH] update prompts --- langchain/prompts/dynamic.py | 20 +++++++++- langchain/prompts/optimized.py | 68 +++++++++++++++++++++++++++++++--- langchain/prompts/prompt.py | 28 ++++++++++++++ 3 files changed, 110 insertions(+), 6 deletions(-) diff --git a/langchain/prompts/dynamic.py b/langchain/prompts/dynamic.py index fbf0c351351..7cdddb9bdf4 100644 --- a/langchain/prompts/dynamic.py +++ b/langchain/prompts/dynamic.py @@ -5,6 +5,7 @@ from typing import Any, Callable, Dict, List from pydantic import BaseModel, Extra, root_validator from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, BasePrompt +from langchain.prompts.prompt import Prompt class DynamicPrompt(BaseModel, BasePrompt): @@ -18,7 +19,7 @@ class DynamicPrompt(BaseModel, BasePrompt): examples=["Say hi. Hi", "Say ho. Ho"], example_separator="\n\n", prefix="", - suffix="\n\nSay {foo}" + suffix="Say {foo}" input_variables=["foo"], max_length=200, get_text_length=word_count @@ -110,3 +111,20 @@ class DynamicPrompt(BaseModel, BasePrompt): except KeyError: raise ValueError("Invalid prompt schema.") return values + + @classmethod + def from_structured_examples( + cls, examples: List[dict], example_prompt: Prompt, **kwargs: Any + ) -> "DynamicPrompt": + """Create prompt from structured examples. + + Args: + examples: List of structured examples to use in the prompt. + example_prompt: Prompt used to format the examples. + **kwargs: Key-word arguments to passed through to init. + + Returns: + The final prompt generated. + """ + string_examples = [example_prompt.format(**example) for example in examples] + return cls(examples=string_examples, **kwargs) diff --git a/langchain/prompts/optimized.py b/langchain/prompts/optimized.py index c8240671bbd..df71ff2c856 100644 --- a/langchain/prompts/optimized.py +++ b/langchain/prompts/optimized.py @@ -1,11 +1,12 @@ """Optimized prompt schema definition.""" import re -from typing import Any, Callable, Dict, List +from typing import Any, Callable, Dict, List, Optional from pydantic import BaseModel, Extra, root_validator from langchain.embeddings.base import Embeddings from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING +from langchain.prompts.prompt import Prompt from langchain.vectorstores.base import VectorStore @@ -28,6 +29,9 @@ class OptimizedPrompt(BaseModel): ) """ + vectorstore: VectorStore + """Vectorstore to use for storing the embeddings.""" + example_separator: str = "\n\n" """Example separator, e.g. \n\n, for the dynamic prompt creation.""" @@ -49,9 +53,6 @@ class OptimizedPrompt(BaseModel): max_length: int = 2048 """Max length for the prompt, beyond which examples are cut.""" - vectorstore: VectorStore - """Vectorstore to use for storing the embeddings.""" - class Config: """Configuration for this pydantic object.""" @@ -154,8 +155,65 @@ class OptimizedPrompt(BaseModel): Returns: The OptimizedPrompt instantiated, backed by a vector store. """ + dict_examples = [{"text": example} for example in examples] + example_prompt = Prompt(input_variables=["text"], template="{text}") + return cls.from_structured_examples( + dict_examples, + example_prompt, + suffix, + input_variables, + embeddings, + vectorstore_cls=vectorstore_cls, + example_separator=example_separator, + prefix=prefix, + **vectorstore_cls_kwargs, + ) + + @classmethod + def from_structured_examples( + cls, + examples: List[dict], + example_prompt: Prompt, + suffix: str, + input_variables: List[str], + embeddings: Embeddings, + vectorstore_cls: VectorStore, + example_separator: str = "\n\n", + prefix: str = "", + example_key: Optional[str] = None, + **vectorstore_cls_kwargs: Any, + ) -> "OptimizedPrompt": + """Create k-shot prompt optimizer using example list and embeddings. + + Reshuffles examples for the prompt dynamically based on query similarity. + + Args: + examples: List of structured examples to use in the prompt. + example_prompt: Prompt used to format the examples. + suffix: String to go after the list of examples. Should generally + set up the user's input. + input_variables: A list of variable names the final prompt template + will expect. + embeddings: An initialized embedding API interface, e.g. OpenAIEmbeddings(). + vectorstore_cls: A vector store DB interface class, e.g. FAISS. + example_separator: The seperator to use in between examples. Defaults + to two new line characters. + prefix: String that should go before any examples. Generally includes + examples. Default to an empty string. + example_key: Optional string pointing to the key in the example to + vectorized. If None, will format the example in the example_prompt, + and then vectorize that whole string. + vectorstore_cls_kwargs: optional kwargs containing url for vector store + + Returns: + The OptimizedPrompt instantiated, backed by a vector store. + """ + if example_key is None: + string_examples = [example_prompt.format(**example) for example in examples] + else: + string_examples = [example[example_key] for example in examples] vectorstore = vectorstore_cls.from_texts( - examples, embeddings, **vectorstore_cls_kwargs + string_examples, embeddings, **vectorstore_cls_kwargs ) return cls( suffix=suffix, diff --git a/langchain/prompts/prompt.py b/langchain/prompts/prompt.py index b84b7718149..4053412e568 100644 --- a/langchain/prompts/prompt.py +++ b/langchain/prompts/prompt.py @@ -98,6 +98,34 @@ class Prompt(BaseModel, BasePrompt): template = prefix + example_str + suffix return cls(input_variables=input_variables, template=template) + @classmethod + def from_structured_examples( + cls, + examples: List[dict], + example_prompt: "Prompt", + suffix: str, + input_variables: List[str], + **kwargs: Any, + ) -> "Prompt": + """Take examples in list format with prefix and suffix to create a prompt. + + Intended be used as a way to dynamically create a prompt from examples. + + Args: + examples: List of structured examples to use in the prompt. + example_prompt: Prompt used to format each example. + suffix: String to go after the list of examples. Should generally + set up the user's input. + input_variables: A list of variable names the final prompt template + will expect. + **kwargs: Key-word arguments to be passed through to init. + + Returns: + The final prompt generated. + """ + string_examples = [example_prompt.format(**example) for example in examples] + return cls.from_examples(string_examples, suffix, input_variables, **kwargs) + @classmethod def from_file(cls, template_file: str, input_variables: List[str]) -> "Prompt": """Load a prompt from a file.