Refactor prompts into module, add example generation utils (#64)

README.md

@@ -107,7 +107,7 @@ but full API docs can be found [here](https://langchain.readthedocs.io/en/latest
 ## 🤖 Developer Guide
 
 To begin developing on this project, first clone to the repo locally.
-To install requirements, run `pip install -r requirments.txt`.
+To install requirements, run `pip install -r requirements.txt`.
 This will install all requirements for running the package, examples, linting, formatting, and tests.
 
 Formatting for this project is a combination of [Black](https://black.readthedocs.io/en/stable/) and [isort](https://pycqa.github.io/isort/).
@@ -125,6 +125,8 @@ Integration tests cover logic that requires making calls to outside APIs (often
 To run integration tests, run `make integration_tests`.
 If you add support for a new external API, please add a new integration test.
 
+If you are adding a Jupyter notebook example, you can run `pip install -e .` to build the langchain package from your local changes, so your new logic can be imported into the notebook.
+
 Docs are largely autogenerated by [sphinx](https://www.sphinx-doc.org/en/master/) from the code.
 For that reason, we ask that you add good documentation to all classes and methods.
 Similar to linting, we recognize documentation can be annoying - if you do not want to do it, please contact a project maintainer and they can help you with it. We do not want this to be a blocker for good code getting contributed.

examples/generate_examples.ipynb

@@ -0,0 +1,121 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "1685fa2f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.chains.react.prompt import EXAMPLES\n",
+    "from langchain.llms.openai import OpenAI\n",
+    "from langchain.example_generator import generate_example, generate_example_from_dynamic_prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "334ef4f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Question: What is the elevation range for the area that the eastern sector of the\\nColorado orogeny extends into?\\nThought 1: I need to search Colorado orogeny, find the area that the eastern sector\\nof the Colorado orogeny extends into, then find the elevation range of the\\narea.\\nAction 1: Search[Colorado orogeny]\\nObservation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in\\nColorado and surrounding areas.\\nThought 2: It does not mention the eastern sector. So I need to look up eastern\\nsector.\\nAction 2: Lookup[eastern sector]\\nObservation 2: (Result 1 / 1) The eastern sector extends into the High Plains and is called\\nthe Central Plains orogeny.\\nThought 3: The eastern sector of Colorado orogeny extends into the High Plains. So I\\nneed 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\\nHigh Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130\\nm).[3]\\nThought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer\\nis 1,800 to 7,000 ft.\\nAction 5: Finish[1,800 to 7,000 ft]'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print initial example for visibility\n",
+    "EXAMPLES[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a7bd36bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_example = generate_example(EXAMPLES, OpenAI())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e1efb008",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['',\n",
+       " '',\n",
+       " 'Question: Is the Mount Everest taller than the Mount Kilimanjaro?',\n",
+       " '',\n",
+       " 'Thought 1: I need to search Mount Everest and Mount Kilimanjaro, find their',\n",
+       " 'heights, then compare them.',\n",
+       " '',\n",
+       " 'Action 1: Search[Mount Everest]',\n",
+       " '',\n",
+       " \"Observation 1: Mount Everest, at 8,848 metres (29,029 ft), is the world's highest mountain\",\n",
+       " 'and a particularly popular goal for mountaineers.',\n",
+       " '',\n",
+       " 'Thought 2: Mount Everest is 8,848 metres tall. I need to search Mount Kilimanjaro',\n",
+       " 'next.',\n",
+       " '',\n",
+       " 'Action 2: Search[Mount Kilimanjaro]',\n",
+       " '',\n",
+       " 'Observation 2: Mount Kilimanjaro, with its three volcanic cones, Kibo, Mawenzi, and',\n",
+       " 'Shira, is a freestanding mountain in Tanzania. It is the highest mountain in',\n",
+       " 'Africa, and rises approximately 4,900 metres (16,100 ft) from its base to 5,895',\n",
+       " 'metres (19,341 ft) above sea level.',\n",
+       " '',\n",
+       " 'Thought 3: Mount Kilimanjaro is 5,895 metres tall. 8,848 metres (Mount Everest) >',\n",
+       " '5,895 metres (Mount Kil']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_example.split('\\n')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d8843d7b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

langchain/__init__.py

@@ -18,7 +18,7 @@ from langchain.chains import (
 from langchain.docstore import Wikipedia
 from langchain.faiss import FAISS
 from langchain.llms import Cohere, HuggingFaceHub, OpenAI
-from langchain.prompt import BasePrompt, DynamicPrompt, Prompt
+from langchain.prompts import BasePrompt, DynamicPrompt, Prompt
 from langchain.sql_database import SQLDatabase
 
 __all__ = [

langchain/chains/llm.py

@@ -5,7 +5,7 @@ from pydantic import BaseModel, Extra
 
 from langchain.chains.base import Chain
 from langchain.llms.base import LLM
-from langchain.prompt import BasePrompt
+from langchain.prompts.base import BasePrompt
 
 
 class LLMChain(Chain, BaseModel):

langchain/chains/llm_math/prompt.py

@@ -1,5 +1,5 @@
 # flake8: noqa
-from langchain.prompt import Prompt
+from langchain.prompts.prompt import Prompt
 
 _PROMPT_TEMPLATE = """You are GPT-3, and you can't do math.
 

langchain/chains/mapreduce.py

@@ -11,7 +11,7 @@ from pydantic import BaseModel, Extra
 from langchain.chains.base import Chain
 from langchain.chains.llm import LLMChain
 from langchain.llms.base import LLM
-from langchain.prompt import BasePrompt
+from langchain.prompts.base import BasePrompt
 from langchain.text_splitter import TextSplitter
 
 

langchain/chains/natbot/prompt.py

@@ -1,5 +1,5 @@
 # flake8: noqa
-from langchain.prompt import Prompt
+from langchain.prompts.prompt import Prompt
 
 _PROMPT_TEMPLATE = """
 You are an agent controlling a browser. You are given:
@@ -30,7 +30,7 @@ Based on your given objective, issue whatever command you believe will get you c
 You always start on Google; you should submit a search query to Google that will take you to the best page for
 achieving your objective. And then interact with that page to achieve your objective.
 
-If you find yourself on Google and there are no search results displayed yet, you should probably issue a command 
+If you find yourself on Google and there are no search results displayed yet, you should probably issue a command
 like "TYPESUBMIT 7 "search query"" to get to a more useful page.
 
 Then, if you find yourself on a Google search results page, you might issue the command "CLICK 24" to click
@@ -66,7 +66,7 @@ CURRENT BROWSER CONTENT:
 ------------------
 OBJECTIVE: Find a 2 bedroom house for sale in Anchorage AK for under $750k
 CURRENT URL: https://www.google.com/
-YOUR COMMAND: 
+YOUR COMMAND:
 TYPESUBMIT 8 "anchorage redfin"
 ==================================================
 
@@ -95,7 +95,7 @@ CURRENT BROWSER CONTENT:
 ------------------
 OBJECTIVE: Make a reservation for 4 at Dorsia at 8pm
 CURRENT URL: https://www.google.com/
-YOUR COMMAND: 
+YOUR COMMAND:
 TYPESUBMIT 8 "dorsia nyc opentable"
 ==================================================
 
@@ -114,15 +114,15 @@ CURRENT BROWSER CONTENT:
 <text id=9>Sep 28, 2022</text>
 <text id=10>7:00 PM</text>
 <text id=11>2 people</text>
-<input id=12 alt="Location, Restaurant, or Cuisine"></input> 
+<input id=12 alt="Location, Restaurant, or Cuisine"></input>
 <button id=13>Let’s go</button>
-<text id=14>It looks like you're in Peninsula. Not correct?</text> 
+<text id=14>It looks like you're in Peninsula. Not correct?</text>
 <button id=15>Get current location</button>
 <button id=16>Next</button>
 ------------------
 OBJECTIVE: Make a reservation for 4 for dinner at Dorsia in New York City at 8pm
 CURRENT URL: https://www.opentable.com/
-YOUR COMMAND: 
+YOUR COMMAND:
 TYPESUBMIT 12 "dorsia new york city"
 ==================================================
 

langchain/chains/react/prompt.py

@@ -1,5 +1,5 @@
 # flake8: noqa
-from langchain.prompt import Prompt
+from langchain.prompts.prompt import Prompt
 
 EXAMPLES = [
     """Question: What is the elevation range for the area that the eastern sector of the

langchain/chains/self_ask_with_search/prompt.py

@@ -1,5 +1,5 @@
 # flake8: noqa
-from langchain.prompt import Prompt
+from langchain.prompts.prompt import Prompt
 
 _DEFAULT_TEMPLATE = """Question: Who lived longer, Muhammad Ali or Alan Turing?
 Are follow up questions needed here: Yes.

langchain/chains/sql_database/prompt.py

@@ -1,7 +1,7 @@
 # flake8: noqa
-from langchain.prompt import Prompt
+from langchain.prompts.prompt import Prompt
 
-_DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. 
+_DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer.
 Use the following format:
 
 Question: "Question here"

langchain/example_generator.py

@@ -0,0 +1,20 @@
+"""Utility functions for working with prompts."""
+from typing import List
+
+from langchain.chains.llm import LLMChain
+from langchain.llms.base import LLM
+from langchain.prompts.dynamic import DynamicPrompt
+
+TEST_GEN_TEMPLATE_SUFFIX = "Add another example."
+
+
+def generate_example(examples: List[str], llm: LLM) -> str:
+    """Return another example given a list of examples for a prompt."""
+    prompt = DynamicPrompt(examples=examples, suffix=TEST_GEN_TEMPLATE_SUFFIX)
+    chain = LLMChain(llm=llm, prompt=prompt)
+    return chain.predict()
+
+
+def generate_example_from_dynamic_prompt(prompt: DynamicPrompt, llm: LLM) -> str:
+    """Return another example given a DynamicPrompt object."""
+    return generate_example(prompt.examples, llm)

langchain/prompt.py

@@ -1,234 +0,0 @@
-"""Prompt schema definition."""
-import re
-from abc import ABC, abstractmethod
-from typing import Any, Callable, Dict, List
-
-from pydantic import BaseModel, Extra, root_validator
-
-from langchain.formatting import formatter
-
-_FORMATTER_MAPPING = {
-    "f-string": formatter.format,
-}
-
-
-class BasePrompt(ABC):
-    """Base prompt should expose the format method, returning a prompt."""
-
-    input_variables: List[str]
-    """A list of the names of the variables the prompt template expects."""
-
-    @abstractmethod
-    def format(self, **kwargs: Any) -> str:
-        """Format the prompt with the inputs.
-
-        Args:
-            kwargs: Any arguments to be passed to the prompt template.
-
-        Returns:
-            A formatted string.
-
-        Example:
-
-        .. code-block:: python
-
-            prompt.format(variable1="foo")
-        """
-
-
-class Prompt(BaseModel, BasePrompt):
-    """Schema to represent a prompt for an LLM.
-
-    Example:
-        .. code-block:: python
-
-            from langchain import Prompt
-            prompt = Prompt(input_variables=["foo"], template="Say {foo}")
-    """
-
-    input_variables: List[str]
-    """A list of the names of the variables the prompt template expects."""
-
-    template: str
-    """The prompt template."""
-
-    template_format: str = "f-string"
-    """The format of the prompt template. Options are: 'f-string'."""
-
-    class Config:
-        """Configuration for this pydantic object."""
-
-        extra = Extra.forbid
-
-    def format(self, **kwargs: Any) -> str:
-        """Format the prompt with the inputs.
-
-        Args:
-            kwargs: Any arguments to be passed to the prompt template.
-
-        Returns:
-            A formatted string.
-
-        Example:
-
-        .. code-block:: python
-
-            prompt.format(variable1="foo")
-        """
-        return _FORMATTER_MAPPING[self.template_format](self.template, **kwargs)
-
-    @root_validator()
-    def template_is_valid(cls, values: Dict) -> Dict:
-        """Check that template and input variables are consistent."""
-        input_variables = values["input_variables"]
-        template = values["template"]
-        template_format = values["template_format"]
-        if template_format not in _FORMATTER_MAPPING:
-            valid_formats = list(_FORMATTER_MAPPING)
-            raise ValueError(
-                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 = _FORMATTER_MAPPING[template_format]
-            formatter_func(template, **dummy_inputs)
-        except KeyError:
-            raise ValueError("Invalid prompt schema.")
-        return values
-
-    @classmethod
-    def from_examples(
-        cls,
-        examples: List[str],
-        suffix: str,
-        input_variables: List[str],
-        example_separator: str = "\n\n",
-        prefix: str = "",
-    ) -> "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 examples to use in the prompt.
-            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.
-            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.
-
-        Returns:
-            The final prompt generated.
-        """
-        example_str = example_separator.join(examples)
-        template = prefix + example_str + suffix
-        return cls(input_variables=input_variables, template=template)
-
-
-class DynamicPrompt(BaseModel, BasePrompt):
-    r"""Schema to represent a dynamic prompt for an LLM.
-
-    Example:
-        .. code-block:: python
-
-            from langchain import DynamicPrompt
-            dynamic_prompt = DynamicPrompt(
-                examples=["Say hi. Hi", "Say ho. Ho"],
-                example_separator="\n\n",
-                prefix="",
-                suffix="\n\nSay {foo}"
-                input_variables=["foo"],
-                max_length=200,
-                get_text_length=word_count
-            )
-    """
-
-    examples: List[str]
-    """A list of the examples that the prompt template expects."""
-
-    example_separator: str = "\n\n"
-    """Example separator, e.g. \n\n, for the dynamic prompt creation."""
-
-    input_variables: List[str]
-    """A list of the names of the variables the prompt template expects."""
-
-    prefix: str
-    """Prefix for the prompt."""
-
-    suffix: str
-    """Suffix for the prompt."""
-
-    template_format: str = "f-string"
-    """The format of the prompt template. Options are: 'f-string'."""
-
-    get_text_length: Callable[[str], int] = lambda x: len(re.split("\n| ", x))
-    """Function to measure prompt length. Defaults to word count."""
-
-    max_length: int = 2048
-    """Max length for the prompt, beyond which examples are cut."""
-
-    class Config:
-        """Configuration for this pydantic object."""
-
-        extra = Extra.forbid
-
-    def template(self, example_list: List[str], **kwargs: Any) -> str:
-        """Return template given example list."""
-        template = self.example_separator.join(
-            [self.prefix, *example_list, self.suffix]
-        )
-        return _FORMATTER_MAPPING[self.template_format](template, **kwargs)
-
-    def format(self, **kwargs: Any) -> str:
-        """Dynamically format the prompt with the inputs.
-
-        Args:
-            kwargs: Any arguments to be passed to the prompt template.
-
-        Returns:
-            A formatted string.
-
-        Example:
-
-        .. code-block:: python
-
-            prompt.format(variable1="foo")
-        """
-        curr_examples = 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]
-            template = self.template(curr_examples, **kwargs)
-        return template
-
-    @root_validator()
-    def template_is_valid(cls, values: Dict) -> Dict:
-        """Check that prefix, suffix and input variables are consistent."""
-        input_variables = values["input_variables"]
-        suffix = values["suffix"]
-        template_format = values["template_format"]
-        if template_format not in _FORMATTER_MAPPING:
-            valid_formats = list(_FORMATTER_MAPPING)
-            raise ValueError(
-                f"Invalid template format. Got `{template_format}`;"
-                f" should be one of {valid_formats}"
-            )
-        try:
-            result = values["get_text_length"]("foo")
-            assert isinstance(result, int)
-        except AssertionError:
-            raise ValueError(
-                "Invalid text length callable, must take string & return int;"
-            )
-        dummy_inputs = {input_variable: "foo" for input_variable in input_variables}
-        # TODO variables could be in prefix or suffix
-        try:
-            formatter_func = _FORMATTER_MAPPING[template_format]
-            formatter_func(suffix, **dummy_inputs)
-        except KeyError:
-            raise ValueError("Invalid prompt schema.")
-        return values

langchain/prompts/__init__.py

@@ -0,0 +1,6 @@
+"""Prompt template classes."""
+from langchain.prompts.base import BasePrompt
+from langchain.prompts.dynamic import DynamicPrompt
+from langchain.prompts.prompt import Prompt
+
+__all__ = ["BasePrompt", "Prompt", "DynamicPrompt"]

langchain/prompts/base.py

@@ -0,0 +1,33 @@
+"""BasePrompt schema definition."""
+from abc import ABC, abstractmethod
+from typing import Any, List
+
+from langchain.formatting import formatter
+
+DEFAULT_FORMATTER_MAPPING = {
+    "f-string": formatter.format,
+}
+
+
+class BasePrompt(ABC):
+    """Base prompt should expose the format method, returning a prompt."""
+
+    input_variables: List[str]
+    """A list of the names of the variables the prompt template expects."""
+
+    @abstractmethod
+    def format(self, **kwargs: Any) -> str:
+        """Format the prompt with the inputs.
+
+        Args:
+            kwargs: Any arguments to be passed to the prompt template.
+
+        Returns:
+            A formatted string.
+
+        Example:
+
+        .. code-block:: python
+
+            prompt.format(variable1="foo")
+        """

langchain/prompts/dynamic.py

@@ -0,0 +1,112 @@
+"""Dynamic prompt schema definition."""
+import re
+from typing import Any, Callable, Dict, List
+
+from pydantic import BaseModel, Extra, root_validator
+
+from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, BasePrompt
+
+
+class DynamicPrompt(BaseModel, BasePrompt):
+    r"""Schema to represent a dynamic prompt for an LLM.
+
+    Example:
+        .. code-block:: python
+
+            from langchain import DynamicPrompt
+            dynamic_prompt = DynamicPrompt(
+                examples=["Say hi. Hi", "Say ho. Ho"],
+                example_separator="\n\n",
+                prefix="",
+                suffix="\n\nSay {foo}"
+                input_variables=["foo"],
+                max_length=200,
+                get_text_length=word_count
+            )
+    """
+
+    examples: List[str]
+    """A list of the examples that the prompt template expects."""
+
+    example_separator: str = "\n\n"
+    """Example separator, e.g. \n\n, for the dynamic prompt creation."""
+
+    input_variables: List[str] = []
+    """A list of the names of the variables the prompt template expects."""
+
+    prefix: str = ""
+    """Prefix for the prompt."""
+
+    suffix: str = ""
+    """Suffix for the prompt."""
+
+    template_format: str = "f-string"
+    """The format of the prompt template. Options are: 'f-string'."""
+
+    get_text_length: Callable[[str], int] = lambda x: len(re.split("\n| ", x))
+    """Function to measure prompt length. Defaults to word count."""
+
+    max_length: int = 2048
+    """Max length for the prompt, beyond which examples are cut."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    def template(self, example_list: List[str], **kwargs: Any) -> str:
+        """Return template given example list."""
+        template = self.example_separator.join(
+            [self.prefix, *example_list, self.suffix]
+        )
+        return DEFAULT_FORMATTER_MAPPING[self.template_format](template, **kwargs)
+
+    def format(self, **kwargs: Any) -> str:
+        """Dynamically format the prompt with the inputs.
+
+        Args:
+            kwargs: Any arguments to be passed to the prompt template.
+
+        Returns:
+            A formatted string.
+
+        Example:
+
+        .. code-block:: python
+
+            prompt.format(variable1="foo")
+        """
+        curr_examples = 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]
+            template = self.template(curr_examples, **kwargs)
+        return template
+
+    @root_validator()
+    def template_is_valid(cls, values: Dict) -> Dict:
+        """Check that prefix, suffix and input variables are consistent."""
+        input_variables = values["input_variables"]
+        prefix = values["prefix"]
+        suffix = values["suffix"]
+        template_format = values["template_format"]
+        if template_format not in DEFAULT_FORMATTER_MAPPING:
+            valid_formats = list(DEFAULT_FORMATTER_MAPPING)
+            raise ValueError(
+                f"Invalid template format. Got `{template_format}`;"
+                f" should be one of {valid_formats}"
+            )
+        try:
+            result = values["get_text_length"]("foo")
+            assert isinstance(result, int)
+        except AssertionError:
+            raise ValueError(
+                "Invalid text length callable, must take string & return int;"
+            )
+        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

langchain/prompts/prompt.py

@@ -0,0 +1,99 @@
+"""Prompt schema definition."""
+from typing import Any, Dict, List
+
+from pydantic import BaseModel, Extra, root_validator
+
+from langchain.prompts.base import DEFAULT_FORMATTER_MAPPING, BasePrompt
+
+
+class Prompt(BaseModel, BasePrompt):
+    """Schema to represent a prompt for an LLM.
+
+    Example:
+        .. code-block:: python
+
+            from langchain import Prompt
+            prompt = Prompt(input_variables=["foo"], template="Say {foo}")
+    """
+
+    input_variables: List[str]
+    """A list of the names of the variables the prompt template expects."""
+
+    template: str
+    """The prompt template."""
+
+    template_format: str = "f-string"
+    """The format of the prompt template. Options are: 'f-string'."""
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    def format(self, **kwargs: Any) -> str:
+        """Format the prompt with the inputs.
+
+        Args:
+            kwargs: Any arguments to be passed to the prompt template.
+
+        Returns:
+            A formatted string.
+
+        Example:
+
+        .. code-block:: python
+
+            prompt.format(variable1="foo")
+        """
+        return DEFAULT_FORMATTER_MAPPING[self.template_format](self.template, **kwargs)
+
+    @root_validator()
+    def template_is_valid(cls, values: Dict) -> Dict:
+        """Check that template and input variables are consistent."""
+        input_variables = values["input_variables"]
+        template = values["template"]
+        template_format = values["template_format"]
+        if template_format not in DEFAULT_FORMATTER_MAPPING:
+            valid_formats = list(DEFAULT_FORMATTER_MAPPING)
+            raise ValueError(
+                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(template, **dummy_inputs)
+        except KeyError:
+            raise ValueError("Invalid prompt schema.")
+        return values
+
+    @classmethod
+    def from_examples(
+        cls,
+        examples: List[str],
+        suffix: str,
+        input_variables: List[str],
+        example_separator: str = "\n\n",
+        prefix: str = "",
+    ) -> "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 examples to use in the prompt.
+            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.
+            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.
+
+        Returns:
+            The final prompt generated.
+        """
+        example_str = example_separator.join(examples)
+        template = prefix + example_str + suffix
+        return cls(input_variables=input_variables, template=template)

tests/unit_tests/chains/test_llm.py

@@ -2,7 +2,7 @@
 import pytest
 
 from langchain.chains.llm import LLMChain
-from langchain.prompt import Prompt
+from langchain.prompts.prompt import Prompt
 from tests.unit_tests.llms.fake_llm import FakeLLM
 
 

tests/unit_tests/chains/test_react.py

@@ -9,7 +9,7 @@ from langchain.chains.react.base import ReActChain, predict_until_observation
 from langchain.docstore.base import Docstore
 from langchain.docstore.document import Document
 from langchain.llms.base import LLM
-from langchain.prompt import Prompt
+from langchain.prompts.prompt import Prompt
 
 _PAGE_CONTENT = """This is a page about LangChain.
 

tests/unit_tests/test_dynamic_prompt.py

@@ -1,5 +1,6 @@
 """Test functionality related to dynamic prompts."""
-from langchain.prompt import DynamicPrompt, Prompt
+from langchain.prompts.dynamic import DynamicPrompt
+from langchain.prompts.prompt import Prompt
 
 # FULL TEMPLATES
 LONGER_TEMPLATE = """Test Prompt:

tests/unit_tests/test_prompt.py

@@ -1,7 +1,7 @@
 """Test functionality related to prompts."""
 import pytest
 
-from langchain.prompt import Prompt
+from langchain.prompts.prompt import Prompt
 
 
 def test_prompt_valid() -> None: