Source code for langchain_prompty.parsers
import base64
import re
from typing import Dict, List, Type, Union
from langchain_core.messages import (
AIMessage,
BaseMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
)
from pydantic import BaseModel
from .core import Invoker, Prompty, SimpleModel
[docs]class RoleMap:
_ROLE_MAP: Dict[str, Type[BaseMessage]] = {
"system": SystemMessage,
"user": HumanMessage,
"human": HumanMessage,
"assistant": AIMessage,
"ai": AIMessage,
"function": FunctionMessage,
}
ROLES = _ROLE_MAP.keys()
[docs] @classmethod
def get_message_class(cls, role: str) -> Type[BaseMessage]:
return cls._ROLE_MAP[role]
[docs]class PromptyChatParser(Invoker):
"""Parse a chat prompt into a list of messages."""
[docs] def __init__(self, prompty: Prompty) -> None:
self.prompty = prompty
self.roles = RoleMap.ROLES
self.path = self.prompty.file.parent
[docs] def inline_image(self, image_item: str) -> str:
# pass through if it's a url or base64 encoded
if image_item.startswith("http") or image_item.startswith("data"):
return image_item
# otherwise, it's a local file - need to base64 encode it
else:
image_path = self.path / image_item
with open(image_path, "rb") as f:
base64_image = base64.b64encode(f.read()).decode("utf-8")
if image_path.suffix == ".png":
return f"data:image/png;base64,{base64_image}"
elif image_path.suffix == ".jpg":
return f"data:image/jpeg;base64,{base64_image}"
elif image_path.suffix == ".jpeg":
return f"data:image/jpeg;base64,{base64_image}"
else:
raise ValueError(
f"Invalid image format {image_path.suffix} - currently only .png "
"and .jpg / .jpeg are supported."
)
[docs] def parse_content(self, content: str) -> Union[str, List]:
"""for parsing inline images"""
# regular expression to parse markdown images
image = r"(?P<alt>!\[[^\]]*\])\((?P<filename>.*?)(?=\"|\))\)"
matches = re.findall(image, content, flags=re.MULTILINE)
if len(matches) > 0:
content_items = []
content_chunks = re.split(image, content, flags=re.MULTILINE)
current_chunk = 0
for i in range(len(content_chunks)):
# image entry
if (
current_chunk < len(matches)
and content_chunks[i] == matches[current_chunk][0]
):
content_items.append(
{
"type": "image_url",
"image_url": {
"url": self.inline_image(
matches[current_chunk][1].split(" ")[0].strip()
)
},
}
)
# second part of image entry
elif (
current_chunk < len(matches)
and content_chunks[i] == matches[current_chunk][1]
):
current_chunk += 1
# text entry
else:
if len(content_chunks[i].strip()) > 0:
content_items.append(
{"type": "text", "text": content_chunks[i].strip()}
)
return content_items
else:
return content
[docs] def invoke(self, data: BaseModel) -> BaseModel:
assert isinstance(data, SimpleModel)
messages = []
separator = r"(?i)^\s*#?\s*(" + "|".join(self.roles) + r")\s*:\s*\n"
# get valid chunks - remove empty items
chunks = [
item
for item in re.split(separator, data.item, flags=re.MULTILINE)
if len(item.strip()) > 0
]
# if no starter role, then inject system role
if chunks[0].strip().lower() not in self.roles:
chunks.insert(0, "system")
# if last chunk is role entry, then remove (no content?)
if chunks[-1].strip().lower() in self.roles:
chunks.pop()
if len(chunks) % 2 != 0:
raise ValueError("Invalid prompt format")
# create messages
for i in range(0, len(chunks), 2):
role = chunks[i].strip().lower()
content = chunks[i + 1].strip()
messages.append({"role": role, "content": self.parse_content(content)})
return SimpleModel[list](item=messages)