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80 lines
3.1 KiB
Python
80 lines
3.1 KiB
Python
"""Example selector that selects examples based on SemanticSimilarity."""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from pydantic import BaseModel, Extra
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from langchain.embeddings.base import Embeddings
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from langchain.prompts.example_selector.base import BaseExampleSelector
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from langchain.vectorstores.base import VectorStore
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def sorted_values(values: Dict[str, str]) -> List[Any]:
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"""Return a list of values in dict sorted by key."""
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return [values[val] for val in sorted(values)]
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class SemanticSimilarityExampleSelector(BaseExampleSelector, BaseModel):
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"""Example selector that selects examples based on SemanticSimilarity."""
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vectorstore: VectorStore
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"""VectorStore than contains information about examples."""
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k: int = 4
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"""Number of examples to select."""
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example_keys: Optional[List[str]] = None
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"""Optional keys to filter examples to."""
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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def add_example(self, example: Dict[str, str]) -> None:
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"""Add new example to vectorstore."""
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string_example = " ".join(sorted_values(example))
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self.vectorstore.add_texts([string_example], metadatas=[example])
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def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
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"""Select which examples to use based on semantic similarity."""
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# Get the docs with the highest similarity.
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query = " ".join(sorted_values(input_variables))
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example_docs = self.vectorstore.similarity_search(query, k=self.k)
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# Get the examples from the metadata.
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# This assumes that examples are stored in metadata.
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examples = [dict(e.metadata) for e in example_docs]
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# If example keys are provided, filter examples to those keys.
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if self.example_keys:
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examples = [{k: eg[k] for k in self.example_keys} for eg in examples]
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return examples
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@classmethod
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def from_examples(
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cls,
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examples: List[dict],
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embeddings: Embeddings,
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vectorstore_cls: VectorStore,
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k: int = 4,
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**vectorstore_cls_kwargs: Any,
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) -> SemanticSimilarityExampleSelector:
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"""Create k-shot example selector using example list and embeddings.
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Reshuffles examples dynamically based on query similarity.
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Args:
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examples: List of examples to use in the prompt.
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embeddings: An iniialized embedding API interface, e.g. OpenAIEmbeddings().
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vectorstore_cls: A vector store DB interface class, e.g. FAISS.
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k: Number of examples to select
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vectorstore_cls_kwargs: optional kwargs containing url for vector store
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Returns:
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The ExampleSelector instantiated, backed by a vector store.
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"""
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string_examples = [" ".join(sorted_values(eg)) for eg in examples]
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vectorstore = vectorstore_cls.from_texts(
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string_examples, embeddings, metadatas=examples, **vectorstore_cls_kwargs
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)
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return cls(vectorstore=vectorstore, k=k)
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