cr

langchain/chains/llm_math/examples.json

@@ -0,0 +1,6 @@
+[
+  {
+    "question": "What is 37593 * 67?",
+    "answer": "```python\nprint(37593 * 67)\n```\n```output\n2518731\n```\nAnswer: 2518731"
+  }
+]

langchain/chains/llm_math/prompt.py

@@ -1,7 +1,7 @@
 # flake8: noqa
 from langchain.prompts.prompt import Prompt
 
-_PROMPT_TEMPLATE = """You are GPT-3, and you can't do math.
+_PREFIX = """You are GPT-3, and you can't do math.
 
 You can do basic math, and your memorization abilities are impressive, but you can't do any complex calculations that a human could not do in their head. You also have an annoying tendency to just make up highly specific, but wrong, answers.
 
@@ -21,18 +21,29 @@ Otherwise, use this simpler format:
 Question: ${{Question without hard calculation}}
 Answer: ${{Answer}}
 
-Begin.
+Begin."""
 
-Question: What is 37593 * 67?
+from pathlib import Path
+
+from langchain.prompts.data import BaseExample
+
+example_path = Path(__file__).parent / "examples.json"
+import json
+
+
+class LLMMathExample(BaseExample):
+    question: str
+    answer: str
+
+    @property
+    def formatted(self) -> str:
+        return f"Question: {self.question}\n\n{self.answer}"
 
-```python
-print(37593 * 67)
-```
-```output
-2518731
-```
-Answer: 2518731
 
-Question: {question}"""
+with open(example_path) as f:
+    raw_examples = json.load(f)
+    examples = [LLMMathExample(**example) for example in raw_examples]
 
-PROMPT = Prompt(input_variables=["question"], template=_PROMPT_TEMPLATE)
+PROMPT = Prompt.from_examples(
+    examples, "Question: {question}", ["question"], prefix=_PREFIX
+)

langchain/chains/react/examples.json

@@ -0,0 +1,26 @@
+[
+  {
+    "question": "What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?",
+    "answer": "Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of that area.\nAction 1: Search[Colorado orogeny]\nObservation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas.\nThought 2: It does not mention the eastern sector. So I need to look up eastern sector.\nAction 2: Lookup[eastern sector]\nObservation 2: (Result 1 / 1) The eastern sector extends into the High Plains and is called the Central Plains orogeny.\nThought 3: The eastern sector of Colorado orogeny extends into the High Plains. So I need to search High Plains and find its elevation range.\nAction 3: Search[High Plains]\nObservation 3: High Plains refers to one of two distinct land regions\nThought 4: I need to instead search High Plains (United States).\nAction 4: Search[High Plains (United States)]\nObservation 4: The High Plains are a subregion of the Great Plains. From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130 m).[3]\nThought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.\nAction 5: Finish[1,800 to 7,000 ft]"
+  },
+  {
+    "question": "Musician and satirist Allie Goertz wrote a song about the \"The Simpsons\" character Milhouse, who Matt Groening named after who?",
+    "answer": "Thought 1: The question simplifies to \"The Simpsons\" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.\nAction 1: Search[Milhouse]\nObservation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.\nThought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up \"named after\".\nAction 2: Lookup[named after]\nObservation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.\nThought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.\nAction 3: Finish[Richard Nixon]"
+  },
+  {
+    "question": "Which documentary is about Finnish rock groups, Adam Clayton Powell or The Saimaa Gesture?",
+    "answer": "Thought 1: I need to search Adam Clayton Powell and The Saimaa Gesture, and find which documentary is about Finnish rock groups.\nAction 1: Search[Adam Clayton Powell]\nObservation 1 Could not find [Adam Clayton Powell]. Similar: [’Adam Clayton Powell III’, ’Seventh Avenue (Manhattan)’, ’Adam Clayton Powell Jr. State Office Building’, ’Isabel Washington Powell’, ’Adam Powell’, ’Adam Clayton Powell (film)’, ’Giancarlo Esposito’].\nThought 2: To find the documentary, I can search Adam Clayton Powell (film).\nAction 2: Search[Adam Clayton Powell (film)]\nObservation 2: Adam Clayton Powell is a 1989 American documentary film directed by Richard Kilberg. The film is about the rise and fall of influential African-American politician Adam Clayton Powell Jr.[3][4] It was later aired as part of the PBS series The American Experience.\nThought 3: Adam Clayton Powell (film) is a documentary about an African-American politician, not Finnish rock groups. So the documentary about Finnish rock groups must instead be The Saimaa Gesture.\nAction 3: Finish[The Saimaa Gesture]"
+  },
+  {
+    "question": "What profession does Nicholas Ray and Elia Kazan have in common?",
+    "answer": "Thought 1: I need to search Nicholas Ray and Elia Kazan, find their professions, then find the profession they have in common.\nAction 1: Search[Nicholas Ray]\nObservation 1: Nicholas Ray (born Raymond Nicholas Kienzle Jr., August 7, 1911 - June 16, 1979) was an American film director, screenwriter, and actor best known for the 1955 film Rebel Without a Cause.\nThought 2: Professions of Nicholas Ray are director, screenwriter, and actor. I need to search Elia Kazan next and find his professions.\nAction 2: Search[Elia Kazan]\nObservation 2: Elia Kazan was an American film and theatre director, producer, screenwriter and actor.\nThought 3: Professions of Elia Kazan are director, producer, screenwriter, and actor. So profession Nicholas Ray and Elia Kazan have in common is director, screenwriter, and actor.\nAction 3: Finish[director, screenwriter, actor]"
+  },
+  {
+    "question": "Which magazine was started first Arthur’s Magazine or First for Women?",
+    "answer": "Thought 1: I need to search Arthur’s Magazine and First for Women, and find which was started first.\nAction 1: Search[Arthur’s Magazine]\nObservation 1: Arthur’s Magazine (1844-1846) was an American literary periodical published in Philadelphia in the 19th century.\nThought 2: Arthur’s Magazine was started in 1844. I need to search First for Women next.\nAction 2: Search[First for Women]\nObservation 2: First for Women is a woman’s magazine published by Bauer Media Group in the USA.[1] The magazine was started in 1989.\nThought 3: First for Women was started in 1989. 1844 (Arthur’s Magazine) < 1989 (First for Women), so Arthur’s Magazine was started first.\nAction 3: Finish[Arthur’s Magazine]"
+  },
+  {
+    "question": "Were Pavel Urysohn and Leonid Levin known for the same type of work?",
+    "answer": "Thought 1: I need to search Pavel Urysohn and Leonid Levin, find their types of work, then find if they are the same.\nAction 1: Search[Pavel Urysohn]\nObservation 1: Pavel Samuilovich Urysohn (February 3, 1898 - August 17, 1924) was a Soviet mathematician who is best known for his contributions in dimension theory.\nThought 2: Pavel Urysohn is a mathematician. I need to search Leonid Levin next and find its type of work.\nAction 2: Search[Leonid Levin]\nObservation 2: Leonid Anatolievich Levin is a Soviet-American mathematician and computer scientist.\nThought 3: Leonid Levin is a mathematician and computer scientist. So Pavel Urysohn and Leonid Levin have the same type of work.\nAction 3: Finish[yes]"
+  }
+]

langchain/chains/react/prompt.py

@@ -1,116 +1,30 @@
 # flake8: noqa
+from pathlib import Path
+
+from langchain.prompts.data import BaseExample
 from langchain.prompts.prompt import Prompt
 
-EXAMPLES = [
+example_path = Path(__file__).parent / "examples.json"
-    """Question: What is the elevation range for the area that the eastern sector of the
+import json
-Colorado orogeny extends into?
+
-Thought 1: I need to search Colorado orogeny, find the area that the eastern sector
+
-of the Colorado orogeny extends into, then find the elevation range of the
+class ReActExample(BaseExample):
-area.
+    question: str
-Action 1: Search[Colorado orogeny]
+    answer: str
-Observation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in
+
-Colorado and surrounding areas.
+    @property
-Thought 2: It does not mention the eastern sector. So I need to look up eastern
+    def formatted(self) -> str:
-sector.
+        return f"Question: {self.question}\n{self.answer}"
-Action 2: Lookup[eastern sector]
+
-Observation 2: (Result 1 / 1) The eastern sector extends into the High Plains and is called
+
-the Central Plains orogeny.
+with open(example_path) as f:
-Thought 3: The eastern sector of Colorado orogeny extends into the High Plains. So I
+    raw_examples = json.load(f)
-need to search High Plains and find its elevation range.
+    examples = [ReActExample(**example) for example in raw_examples]
-Action 3: Search[High Plains]
+
-Observation 3: High Plains refers to one of two distinct land regions
+SUFFIX = """Question: {input}"""
-Thought 4: I need to instead search High Plains (United States).
-Action 4: Search[High Plains (United States)]
-Observation 4: The High Plains are a subregion of the Great Plains. From east to west, the
-High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130
-m).[3]
-Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer
-is 1,800 to 7,000 ft.
-Action 5: Finish[1,800 to 7,000 ft]""",
-    """Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons"
-character Milhouse, who Matt Groening named after who?
-Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after
-who. I only need to search Milhouse and find who it is named after.
-Action 1: Search[Milhouse]
-Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated
-television series The Simpsons voiced by Pamela Hayden and created by Matt
-Groening.
-Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up
-"named after".
-Action 2: Lookup[named after]
-Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose
-middle name was Milhous.
-Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is
-Richard Nixon.
-Action 3: Finish[Richard Nixon]""",
-    """Question: Which documentary is about Finnish rock groups, Adam Clayton Powell or The
-Saimaa Gesture?
-Thought 1: I need to search Adam Clayton Powell and The Saimaa Gesture, and find which
-documentary is about Finnish rock groups.
-Action 1: Search[Adam Clayton Powell]
-Observation 1 Could not find [Adam Clayton Powell]. Similar: [’Adam Clayton Powell
-III’, ’Seventh Avenue (Manhattan)’, ’Adam Clayton Powell Jr. State Office
-Building’, ’Isabel Washington Powell’, ’Adam Powell’, ’Adam Clayton Powell
-(film)’, ’Giancarlo Esposito’].
-Thought 2: To find the documentary, I can search Adam Clayton Powell (film).
-Action 2: Search[Adam Clayton Powell (film)]
-Observation 2: Adam Clayton Powell is a 1989 American documentary film directed by
-Richard Kilberg. The film is about the rise and fall of influential
-African-American politician Adam Clayton Powell Jr.[3][4] It was later aired
-as part of the PBS series The American Experience.
-Thought 3: Adam Clayton Powell (film) is a documentary about an African-American
-politician, not Finnish rock groups. So the documentary about Finnish rock
-groups must instead be The Saimaa Gesture.
-Action 3: Finish[The Saimaa Gesture]""",
-    """Question: What profession does Nicholas Ray and Elia Kazan have in common?
-Thought 1: I need to search Nicholas Ray and Elia Kazan, find their professions, then
-find the profession they have in common.
-Action 1: Search[Nicholas Ray]
-Observation 1: Nicholas Ray (born Raymond Nicholas Kienzle Jr., August 7, 1911 - June 16,
-1979) was an American film director, screenwriter, and actor best known for
-the 1955 film Rebel Without a Cause.
-Thought 2: Professions of Nicholas Ray are director, screenwriter, and actor. I need
-to search Elia Kazan next and find his professions.
-Action 2: Search[Elia Kazan]
-Observation 2: Elia Kazan was an American film and theatre director, producer, screenwriter
-and actor.
-Thought 3: Professions of Elia Kazan are director, producer, screenwriter, and actor.
-So profession Nicholas Ray and Elia Kazan have in common is director,
-screenwriter, and actor.
-Action 3: Finish[director, screenwriter, actor]""",
-    """Question: Which magazine was started first Arthur’s Magazine or First for Women?
-Thought 1: I need to search Arthur’s Magazine and First for Women, and find which was
-started first.
-Action 1: Search[Arthur’s Magazine]
-Observation 1: Arthur’s Magazine (1844-1846) was an American literary periodical published
-in Philadelphia in the 19th century.
-Thought 2: Arthur’s Magazine was started in 1844. I need to search First for Women
-next.
-Action 2: Search[First for Women]
-Observation 2: First for Women is a woman’s magazine published by Bauer Media Group in the
-USA.[1] The magazine was started in 1989.
-Thought 3: First for Women was started in 1989. 1844 (Arthur’s Magazine) < 1989 (First
-for Women), so Arthur’s Magazine was started first.
-Action 3: Finish[Arthur’s Magazine]""",
-    """Question: Were Pavel Urysohn and Leonid Levin known for the same type of work?
-Thought 1: I need to search Pavel Urysohn and Leonid Levin, find their types of work,
-then find if they are the same.
-Action 1: Search[Pavel Urysohn]
-Observation 1: Pavel Samuilovich Urysohn (February 3, 1898 - August 17, 1924) was a Soviet
-mathematician who is best known for his contributions in dimension theory.
-Thought 2: Pavel Urysohn is a mathematician. I need to search Leonid Levin next and
-find its type of work.
-Action 2: Search[Leonid Levin]
-Observation 2: Leonid Anatolievich Levin is a Soviet-American mathematician and computer
-scientist.
-Thought 3: Leonid Levin is a mathematician and computer scientist. So Pavel Urysohn
-and Leonid Levin have the same type of work.
-Action 3: Finish[yes]""",
-]
-SUFFIX = """\n\nQuestion: {input}"""
 
 PROMPT = Prompt.from_examples(
-    EXAMPLES,
+    examples,
     SUFFIX,
     ["input"],
 )

langchain/chains/self_ask_with_search/examples.json

@@ -0,0 +1,18 @@
+[
+  {
+    "question": "Who lived longer, Muhammad Ali or Alan Turing?",
+    "answer": "Are follow up questions needed here: Yes.\nFollow up: How old was Muhammad Ali when he died?\nIntermediate answer: Muhammad Ali was 74 years old when he died.\nFollow up: How old was Alan Turing when he died?\nIntermediate answer: Alan Turing was 41 years old when he died.\nSo the final answer is: Muhammad Ali"
+  },
+  {
+    "question": "When was the founder of craigslist born?",
+    "answer": "Are follow up questions needed here: Yes.\nFollow up: Who was the founder of craigslist?\nIntermediate answer: Craigslist was founded by Craig Newmark.\nFollow up: When was Craig Newmark born?\nIntermediate answer: Craig Newmark was born on December 6, 1952.\nSo the final answer is: December 6, 1952"
+  },
+  {
+    "question": "Who was the maternal grandfather of George Washington?",
+    "answer": "Are follow up questions needed here: Yes.\nFollow up: Who was the mother of George Washington?\nIntermediate answer: The mother of George Washington was Mary Ball Washington.\nFollow up: Who was the father of Mary Ball Washington?\nIntermediate answer: The father of Mary Ball Washington was Joseph Ball.\nSo the final answer is: Joseph Ball"
+  },
+  {
+    "question": "Are both the directors of Jaws and Casino Royale from the same country?",
+    "answer": "Are follow up questions needed here: Yes.\nFollow up: Who is the director of Jaws?\nIntermediate Answer: The director of Jaws is Steven Spielberg.\nFollow up: Where is Steven Spielberg from?\nIntermediate Answer: The United States.\nFollow up: Who is the director of Casino Royale?\nIntermediate Answer: The director of Casino Royale is Martin Campbell.\nFollow up: Where is Martin Campbell from?\nIntermediate Answer: New Zealand.\nSo the final answer is: No"
+  }
+]

langchain/chains/self_ask_with_search/prompt.py

@@ -1,44 +1,28 @@
 # flake8: noqa
+from pathlib import Path
+
+from langchain.prompts.data import BaseExample
 from langchain.prompts.prompt import Prompt
 
-_DEFAULT_TEMPLATE = """Question: Who lived longer, Muhammad Ali or Alan Turing?
+example_path = Path(__file__).parent / "examples.json"
-Are follow up questions needed here: Yes.
+import json
-Follow up: How old was Muhammad Ali when he died?
+
-Intermediate answer: Muhammad Ali was 74 years old when he died.
+
-Follow up: How old was Alan Turing when he died?
+class SelfAskWithSearchExample(BaseExample):
-Intermediate answer: Alan Turing was 41 years old when he died.
+    question: str
-So the final answer is: Muhammad Ali
+    answer: str
-
+
-Question: When was the founder of craigslist born?
+    @property
-Are follow up questions needed here: Yes.
+    def formatted(self) -> str:
-Follow up: Who was the founder of craigslist?
+        return f"Question: {self.question}\n{self.answer}"
-Intermediate answer: Craigslist was founded by Craig Newmark.
+
-Follow up: When was Craig Newmark born?
+
-Intermediate answer: Craig Newmark was born on December 6, 1952.
+with open(example_path) as f:
-So the final answer is: December 6, 1952
+    raw_examples = json.load(f)
-
+    examples = [SelfAskWithSearchExample(**example) for example in raw_examples]
-Question: Who was the maternal grandfather of George Washington?
+
-Are follow up questions needed here: Yes.
+PROMPT = Prompt.from_examples(
-Follow up: Who was the mother of George Washington?
+    examples,
-Intermediate answer: The mother of George Washington was Mary Ball Washington.
+    "Question: {input}",
-Follow up: Who was the father of Mary Ball Washington?
+    ["input"],
-Intermediate answer: The father of Mary Ball Washington was Joseph Ball.
-So the final answer is: Joseph Ball
-
-Question: Are both the directors of Jaws and Casino Royale from the same country?
-Are follow up questions needed here: Yes.
-Follow up: Who is the director of Jaws?
-Intermediate Answer: The director of Jaws is Steven Spielberg.
-Follow up: Where is Steven Spielberg from?
-Intermediate Answer: The United States.
-Follow up: Who is the director of Casino Royale?
-Intermediate Answer: The director of Casino Royale is Martin Campbell.
-Follow up: Where is Martin Campbell from?
-Intermediate Answer: New Zealand.
-So the final answer is: No
-
-Question: {input}"""
-PROMPT = Prompt(
-    input_variables=["input"],
-    template=_DEFAULT_TEMPLATE,
 )

langchain/example_generator.py

@@ -1,16 +1,18 @@
 """Utility functions for working with prompts."""
-from typing import List
+from typing import Sequence, Union
 
 from langchain.chains.llm import LLMChain
 from langchain.llms.base import LLM
+from langchain.prompts.data import BaseExample, convert_to_examples
 from langchain.prompts.dynamic import DynamicPrompt
 
 TEST_GEN_TEMPLATE_SUFFIX = "Add another example."
 
 
-def generate_example(examples: List[str], llm: LLM) -> str:
+def generate_example(examples: Sequence[Union[str, BaseExample]], llm: LLM) -> str:
     """Return another example given a list of examples for a prompt."""
-    prompt = DynamicPrompt(examples=examples, suffix=TEST_GEN_TEMPLATE_SUFFIX)
+    full_examples = convert_to_examples(examples)
+    prompt = DynamicPrompt(examples=full_examples, suffix=TEST_GEN_TEMPLATE_SUFFIX)
     chain = LLMChain(llm=llm, prompt=prompt)
     return chain.predict()
 

langchain/prompts/base.py

@@ -3,6 +3,7 @@ from abc import ABC, abstractmethod
 from typing import Any, List
 
 from langchain.formatting import formatter
+from langchain.prompts.data import BaseExample
 
 DEFAULT_FORMATTER_MAPPING = {
     "f-string": formatter.format,

langchain/prompts/data.py

@@ -0,0 +1,36 @@
+from abc import ABC, abstractmethod
+
+from pydantic import BaseModel
+
+
+class BaseExample(BaseModel, ABC):
+    """Base class for examples."""
+
+    @property
+    @abstractmethod
+    def formatted(self) -> str:
+        """Returns a formatted example as a string."""
+
+
+class SimpleExample(BaseExample):
+
+    text: str
+
+    @property
+    def formatted(self) -> str:
+        return self.text
+
+
+from typing import Sequence, Union
+
+
+def convert_to_examples(
+    examples: Sequence[Union[str, BaseExample]]
+) -> Sequence[BaseExample]:
+    new_examples = [
+        example
+        if isinstance(example, BaseExample)
+        else SimpleExample(text=str(example))
+        for example in examples
+    ]
+    return new_examples

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.data import BaseExample, convert_to_examples
 
 
 class DynamicPrompt(BaseModel, BasePrompt):
@@ -25,7 +26,7 @@ class DynamicPrompt(BaseModel, BasePrompt):
             )
     """
 
-    examples: List[str]
+    examples: List[BaseExample]
     """A list of the examples that the prompt template expects."""
 
     example_separator: str = "\n\n"
@@ -76,7 +77,7 @@ class DynamicPrompt(BaseModel, BasePrompt):
 
             prompt.format(variable1="foo")
         """
-        curr_examples = self.examples
+        curr_examples = [example.formatted for example in self.examples]
         template = self.template(curr_examples, **kwargs)
         while self.get_text_length(template) > self.max_length and curr_examples:
             curr_examples = curr_examples[:-1]
@@ -96,6 +97,16 @@ class DynamicPrompt(BaseModel, BasePrompt):
                 f"Invalid template format. Got `{template_format}`;"
                 f" should be one of {valid_formats}"
             )
+        dummy_inputs = {input_variable: "foo" for input_variable in input_variables}
+        try:
+            formatter_func = DEFAULT_FORMATTER_MAPPING[template_format]
+            formatter_func(prefix + suffix, **dummy_inputs)
+        except KeyError:
+            raise ValueError("Invalid prompt schema.")
+        return values
+
+    @root_validator()
+    def get_text_length_is_valid(cls, values: Dict) -> Dict:
         try:
             result = values["get_text_length"]("foo")
             assert isinstance(result, int)
@@ -103,10 +114,10 @@ class DynamicPrompt(BaseModel, BasePrompt):
             raise ValueError(
                 "Invalid text length callable, must take string & return int;"
             )
-        dummy_inputs = {input_variable: "foo" for input_variable in input_variables}
+        return values
-        try:
+
-            formatter_func = DEFAULT_FORMATTER_MAPPING[template_format]
+    # Needs to be pre=True to convert to the right type.
-            formatter_func(prefix + suffix, **dummy_inputs)
+    @root_validator(pre=True)
-        except KeyError:
+    def convert_examples(cls, values: Dict) -> Dict:
-            raise ValueError("Invalid prompt schema.")
+        values["examples"] = convert_to_examples(values["examples"])
         return values

langchain/prompts/prompt.py

@@ -1,9 +1,10 @@
 """Prompt schema definition."""
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Sequence, Union
 
 from pydantic import BaseModel, Extra, root_validator
 
 from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, BasePrompt
+from langchain.prompts.data import BaseExample, convert_to_examples
 
 
 class Prompt(BaseModel, BasePrompt):
@@ -70,7 +71,7 @@ class Prompt(BaseModel, BasePrompt):
     @classmethod
     def from_examples(
         cls,
-        examples: List[str],
+        examples: Sequence[Union[BaseExample, str]],
         suffix: str,
         input_variables: List[str],
         example_separator: str = "\n\n",
@@ -94,6 +95,7 @@ class Prompt(BaseModel, BasePrompt):
         Returns:
             The final prompt generated.
         """
-        example_str = example_separator.join(examples)
+        full_examples = convert_to_examples(examples)
-        template = prefix + example_str + suffix
+        data = [prefix] + [example.formatted for example in full_examples] + [suffix]
+        template = example_separator.join(data)
         return cls(input_variables=input_variables, template=template)

tests/unit_tests/chains/test_llm_math.py

@@ -3,18 +3,18 @@
 import pytest
 
 from langchain.chains.llm_math.base import LLMMathChain
-from langchain.chains.llm_math.prompt import _PROMPT_TEMPLATE
+from langchain.chains.llm_math.prompt import PROMPT
 from tests.unit_tests.llms.fake_llm import FakeLLM
 
 
 @pytest.fixture
 def fake_llm_math_chain() -> LLMMathChain:
     """Fake LLM Math chain for testing."""
-    complex_question = _PROMPT_TEMPLATE.format(question="What is the square root of 2?")
+    complex_question = PROMPT.format(question="What is the square root of 2?")
     queries = {
-        _PROMPT_TEMPLATE.format(question="What is 1 plus 1?"): "Answer: 2",
+        PROMPT.format(question="What is 1 plus 1?"): "Answer: 2",
         complex_question: "```python\nprint(2**.5)\n```",
-        _PROMPT_TEMPLATE.format(question="foo"): "foo",
+        PROMPT.format(question="foo"): "foo",
     }
     fake_llm = FakeLLM(queries=queries)
     return LLMMathChain(llm=fake_llm, input_key="q", output_key="a")

tests/unit_tests/test_prompt.py

@@ -51,8 +51,8 @@ Question: {question}
 Answer:"""
     input_variables = ["question"]
     example_separator = "\n\n"
-    prefix = """Test Prompt:\n\n"""
+    prefix = """Test Prompt:"""
-    suffix = """\n\nQuestion: {question}\nAnswer:"""
+    suffix = """Question: {question}\nAnswer:"""
     examples = [
         """Question: who are you?\nAnswer: foo""",
         """Question: what are you?\nAnswer: bar""",