mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
90 lines
2.4 KiB
Python
90 lines
2.4 KiB
Python
import base64
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import json
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts import ChatPromptTemplate, SystemMessagePromptTemplate
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from langchain.pydantic_v1 import Field
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from langchain.schema.output_parser import StrOutputParser
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from langserve import CustomUserType
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from .prompts import (
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AI_REPONSE_DICT,
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FULL_PROMPT,
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USER_EXAMPLE_DICT,
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create_prompt,
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)
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from .utils import parse_llm_output
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llm = ChatOpenAI(temperature=0, model="gpt-4")
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prompt = ChatPromptTemplate.from_messages(
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[
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SystemMessagePromptTemplate.from_template(FULL_PROMPT),
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("human", "{user_example}"),
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("ai", "{ai_response}"),
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("human", "{input}"),
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],
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)
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# ATTENTION: Inherit from CustomUserType instead of BaseModel otherwise
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# the server will decode it into a dict instead of a pydantic model.
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class FileProcessingRequest(CustomUserType):
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"""Request including a base64 encoded file."""
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# The extra field is used to specify a widget for the playground UI.
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file: str = Field(..., extra={"widget": {"type": "base64file"}})
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num_plates: int = None
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num_rows: int = 8
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num_cols: int = 12
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def _load_file(request: FileProcessingRequest):
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return base64.b64decode(request.file.encode("utf-8")).decode("utf-8")
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def _load_prompt(request: FileProcessingRequest):
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return create_prompt(
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num_plates=request.num_plates,
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num_rows=request.num_rows,
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num_cols=request.num_cols,
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)
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def _get_col_range_str(request: FileProcessingRequest):
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if request.num_cols:
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return f"from 1 to {request.num_cols}"
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else:
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return ""
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def _get_json_format(request: FileProcessingRequest):
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return json.dumps(
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[
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{
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"row_start": 12,
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"row_end": 12 + request.num_rows - 1,
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"col_start": 1,
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"col_end": 1 + request.num_cols - 1,
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"contents": "Entity ID",
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}
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]
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)
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chain = (
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{
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# Should add validation to ensure numeric indices
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"input": _load_file,
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"hint": _load_prompt,
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"col_range_str": _get_col_range_str,
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"json_format": _get_json_format,
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"user_example": lambda x: USER_EXAMPLE_DICT[x.num_rows * x.num_cols],
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"ai_response": lambda x: AI_REPONSE_DICT[x.num_rows * x.num_cols],
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}
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| prompt
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| llm
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| StrOutputParser()
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| parse_llm_output
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).with_types(input_type=FileProcessingRequest)
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