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feat: generate_chapter flow
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sichoi42 committed Jul 4, 2024
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203 changes: 169 additions & 34 deletions .gitignore
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HELP.md
.gradle
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
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# Distribution / packaging
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MANIFEST

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8 changes: 8 additions & 0 deletions flows/generate_chapter/standard/.env.example
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# Azure OpenAI Config

# AZURE_OPENAI_API_KEY=<API_KEY>
# AZURE_OPENAI_API_BASE=<API_BASE>
# AZURE_OPENAI_API_TYPE=azure

# OpenAI API Key
AZURE_OPENAI_API_KEY=sk-blabla
5 changes: 5 additions & 0 deletions flows/generate_chapter/standard/.gitignore
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.env
__pycache__/
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!.promptflow/flow.tools.json
.runs/
63 changes: 63 additions & 0 deletions flows/generate_chapter/standard/call_llm.py
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from dotenv import load_dotenv
from promptflow.core import tool
from openai.version import VERSION as OPENAI_VERSION
import os
import json

def get_client():
api_key = os.environ["AZURE_OPENAI_API_KEY"]
conn = dict(
api_key=os.environ["AZURE_OPENAI_API_KEY"],
)
if api_key.startswith("sk-"):
from openai import OpenAI as Client
else:
from openai import AzureOpenAI as Client
conn.update(
azure_endpoint=os.environ.get("AZURE_OPENAI_API_BASE", "azure"),
api_version=os.environ.get("AZURE_OPENAI_API_VERSION", "2023-07-01-preview"),
)
return Client(**conn)

@tool
def call_llm(
chapter_timeline: str,
deployment_name: str,
max_tokens: int = 2048,
temperature: float = 0.5,
top_p: float = 1.0,
n: int = 1,
echo: bool = False,
presence_penalty: float = 0,
frequency_penalty: float = 0,
best_of: int = 1,
logit_bias: dict = {},
user: str = "",
**kwargs,
) -> str:
if "AZURE_OPENAI_API_KEY" not in os.environ or "AZURE_OPENAI_API_BASE" not in os.environ:
# load environment variables from .env file
load_dotenv()

if "AZURE_OPENAI_API_KEY" not in os.environ:
raise Exception("Please specify environment variables: AZURE_OPENAI_API_KEY")

response = get_client().completions.create(
prompt=chapter_timeline,
model=deployment_name,
max_tokens=int(max_tokens),
temperature=float(temperature),
top_p=float(top_p),
n=int(n),
echo=echo,
presence_penalty=float(presence_penalty),
frequency_penalty=float(frequency_penalty),
best_of=int(best_of),
# Logit bias must be a dict if we passed it to openai api.
logit_bias=logit_bias if logit_bias else {},
user=user,
)

chapter_timeline = response.choices[0].text
return json.dumps(chapter_timeline, ensure_ascii=False, indent=4)

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