update prompts

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)

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,

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.