refactor langchain.prompts.example_selector (#15369)

The `langchain.prompts.example_selector` [still holds several
artifacts](https://api.python.langchain.com/en/latest/langchain_api_reference.html#module-langchain.prompts)
that belongs to `community`. If they moved to
`langchain_community.example_selectors`, the `langchain.prompts`
namespace would be effectively removed which is great.
- moved a class and afunction to `langchain_community`

Note:
- Previously, the `langchain.prompts.example_selector` artifacts were
moved into the `langchain_core.exampe_selectors`. See the flattened
namespace (`.prompts` was removed)!
Similar flattening was implemented for the `langchain_core` as the
`langchain_core.exampe_selectors`.

---------

Co-authored-by: Erick Friis <erick@langchain.dev>

libs/community/langchain_community/example_selectors/__init__.py

@@ -0,0 +1,10 @@
+"""Logic for selecting examples to include in prompts."""
+from langchain_community.example_selectors.ngram_overlap import (
+    NGramOverlapExampleSelector,
+    ngram_overlap_score,
+)
+
+__all__ = [
+    "NGramOverlapExampleSelector",
+    "ngram_overlap_score",
+]

libs/community/langchain_community/example_selectors/ngram_overlap.py

@@ -0,0 +1,114 @@
+"""Select and order examples based on ngram overlap score (sentence_bleu score).
+
+https://www.nltk.org/_modules/nltk/translate/bleu_score.html
+https://aclanthology.org/P02-1040.pdf
+"""
+from typing import Dict, List
+
+import numpy as np
+from langchain_core.example_selectors import BaseExampleSelector
+from langchain_core.prompts import PromptTemplate
+from langchain_core.pydantic_v1 import BaseModel, root_validator
+
+
+def ngram_overlap_score(source: List[str], example: List[str]) -> float:
+    """Compute ngram overlap score of source and example as sentence_bleu score
+    from NLTK package.
+
+    Use sentence_bleu with method1 smoothing function and auto reweighting.
+    Return float value between 0.0 and 1.0 inclusive.
+    https://www.nltk.org/_modules/nltk/translate/bleu_score.html
+    https://aclanthology.org/P02-1040.pdf
+    """
+    from nltk.translate.bleu_score import (
+        SmoothingFunction,  # type: ignore
+        sentence_bleu,
+    )
+
+    hypotheses = source[0].split()
+    references = [s.split() for s in example]
+
+    return float(
+        sentence_bleu(
+            references,
+            hypotheses,
+            smoothing_function=SmoothingFunction().method1,
+            auto_reweigh=True,
+        )
+    )
+
+
+class NGramOverlapExampleSelector(BaseExampleSelector, BaseModel):
+    """Select and order examples based on ngram overlap score (sentence_bleu score
+    from NLTK package).
+
+    https://www.nltk.org/_modules/nltk/translate/bleu_score.html
+    https://aclanthology.org/P02-1040.pdf
+    """
+
+    examples: List[dict]
+    """A list of the examples that the prompt template expects."""
+
+    example_prompt: PromptTemplate
+    """Prompt template used to format the examples."""
+
+    threshold: float = -1.0
+    """Threshold at which algorithm stops. Set to -1.0 by default.
+
+    For negative threshold:
+    select_examples sorts examples by ngram_overlap_score, but excludes none.
+    For threshold greater than 1.0:
+    select_examples excludes all examples, and returns an empty list.
+    For threshold equal to 0.0:
+    select_examples sorts examples by ngram_overlap_score,
+    and excludes examples with no ngram overlap with input.
+    """
+
+    @root_validator(pre=True)
+    def check_dependencies(cls, values: Dict) -> Dict:
+        """Check that valid dependencies exist."""
+        try:
+            from nltk.translate.bleu_score import (  # noqa: F401
+                SmoothingFunction,
+                sentence_bleu,
+            )
+        except ImportError as e:
+            raise ImportError(
+                "Not all the correct dependencies for this ExampleSelect exist."
+                "Please install nltk with `pip install nltk`."
+            ) from e
+
+        return values
+
+    def add_example(self, example: Dict[str, str]) -> None:
+        """Add new example to list."""
+        self.examples.append(example)
+
+    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
+        """Return list of examples sorted by ngram_overlap_score with input.
+
+        Descending order.
+        Excludes any examples with ngram_overlap_score less than or equal to threshold.
+        """
+        inputs = list(input_variables.values())
+        examples = []
+        k = len(self.examples)
+        score = [0.0] * k
+        first_prompt_template_key = self.example_prompt.input_variables[0]
+
+        for i in range(k):
+            score[i] = ngram_overlap_score(
+                inputs, [self.examples[i][first_prompt_template_key]]
+            )
+
+        while True:
+            arg_max = np.argmax(score)
+            if (score[arg_max] < self.threshold) or abs(
+                score[arg_max] - self.threshold
+            ) < 1e-9:
+                break
+
+            examples.append(self.examples[arg_max])
+            score[arg_max] = self.threshold - 1.0
+
+        return examples

libs/langchain/langchain/prompts/example_selector/ngram_overlap.py

@@ -1,112 +1,9 @@
-"""Select and order examples based on ngram overlap score (sentence_bleu score).
+from langchain_community.example_selectors.ngram_overlap import (
-
+    NGramOverlapExampleSelector,
-https://www.nltk.org/_modules/nltk/translate/bleu_score.html
+    ngram_overlap_score,
-https://aclanthology.org/P02-1040.pdf
-"""
-from typing import Dict, List
-
-import numpy as np
-from langchain_core.example_selectors.base import BaseExampleSelector
-from langchain_core.prompts import PromptTemplate
-from langchain_core.pydantic_v1 import BaseModel, root_validator
-
-
-def ngram_overlap_score(source: List[str], example: List[str]) -> float:
-    """Compute ngram overlap score of source and example as sentence_bleu score.
-
-    Use sentence_bleu with method1 smoothing function and auto reweighting.
-    Return float value between 0.0 and 1.0 inclusive.
-    https://www.nltk.org/_modules/nltk/translate/bleu_score.html
-    https://aclanthology.org/P02-1040.pdf
-    """
-    from nltk.translate.bleu_score import (
-        SmoothingFunction,  # type: ignore
-        sentence_bleu,
 )
 
-    hypotheses = source[0].split()
+__all__ = [
-    references = [s.split() for s in example]
+    "NGramOverlapExampleSelector",
-
+    "ngram_overlap_score",
-    return float(
+]
-        sentence_bleu(
-            references,
-            hypotheses,
-            smoothing_function=SmoothingFunction().method1,
-            auto_reweigh=True,
-        )
-    )
-
-
-class NGramOverlapExampleSelector(BaseExampleSelector, BaseModel):
-    """Select and order examples based on ngram overlap score (sentence_bleu score).
-
-    https://www.nltk.org/_modules/nltk/translate/bleu_score.html
-    https://aclanthology.org/P02-1040.pdf
-    """
-
-    examples: List[dict]
-    """A list of the examples that the prompt template expects."""
-
-    example_prompt: PromptTemplate
-    """Prompt template used to format the examples."""
-
-    threshold: float = -1.0
-    """Threshold at which algorithm stops. Set to -1.0 by default.
-
-    For negative threshold:
-    select_examples sorts examples by ngram_overlap_score, but excludes none.
-    For threshold greater than 1.0:
-    select_examples excludes all examples, and returns an empty list.
-    For threshold equal to 0.0:
-    select_examples sorts examples by ngram_overlap_score,
-    and excludes examples with no ngram overlap with input.
-    """
-
-    @root_validator(pre=True)
-    def check_dependencies(cls, values: Dict) -> Dict:
-        """Check that valid dependencies exist."""
-        try:
-            from nltk.translate.bleu_score import (  # noqa: F401
-                SmoothingFunction,
-                sentence_bleu,
-            )
-        except ImportError as e:
-            raise ImportError(
-                "Not all the correct dependencies for this ExampleSelect exist."
-                "Please install nltk with `pip install nltk`."
-            ) from e
-
-        return values
-
-    def add_example(self, example: Dict[str, str]) -> None:
-        """Add new example to list."""
-        self.examples.append(example)
-
-    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
-        """Return list of examples sorted by ngram_overlap_score with input.
-
-        Descending order.
-        Excludes any examples with ngram_overlap_score less than or equal to threshold.
-        """
-        inputs = list(input_variables.values())
-        examples = []
-        k = len(self.examples)
-        score = [0.0] * k
-        first_prompt_template_key = self.example_prompt.input_variables[0]
-
-        for i in range(k):
-            score[i] = ngram_overlap_score(
-                inputs, [self.examples[i][first_prompt_template_key]]
-            )
-
-        while True:
-            arg_max = np.argmax(score)
-            if (score[arg_max] < self.threshold) or abs(
-                score[arg_max] - self.threshold
-            ) < 1e-9:
-                break
-
-            examples.append(self.examples[arg_max])
-            score[arg_max] = self.threshold - 1.0
-
-        return examples