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Use system instructions to steer the behavior of a model

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This commit is contained in:
ajaysi
2024-08-18 17:13:00 +05:30
parent f35649f129
commit b97ad5eb2b
7 changed files with 90 additions and 61 deletions
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20
lib/ai_writers/blog_from_google_serp.py
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@@ -15,26 +15,9 @@ from ..gpt_providers.text_generation.main_text_generation import llm_text_gen
def write_blog_google_serp(search_keyword, search_results):
"""Combine the given online research and GPT blog content"""
try:
config_path = Path(os.environ["ALWRITY_CONFIG"])
with open(config_path, 'r', encoding='utf-8') as file:
config = json.load(file)
except Exception as err:
logger.error(f"Error: Failed to read values from config: {err}")
exit(1)
blog_characteristics = config['Blog Content Characteristics']
prompt = f"""
As expert Creative Content writer,
I want you to write {blog_characteristics['Blog Type']} blog post,
that explores {search_keyword} and also include 5 FAQs.
Below are the guidelines to follow:
1). You must respond in {blog_characteristics['Blog Language']} language.
2). Tone and Brand Alignment: Adjust your tone, voice, personality for {blog_characteristics['Blog Tone']} audience.
3). Make sure your response content length is of {blog_characteristics['Blog Length']} words.
4). Include FAQs from 'People also Ask' section of provided context 'google search result'.
I want you to write blog post, that explores {search_keyword} and also include 5 FAQs.
I want the post to offer unique insights, relatable examples, and a fresh perspective on the topic.
Here are some Google search results to spark your creativity on {search_keyword}:
@@ -51,6 +34,7 @@ def write_blog_google_serp(search_keyword, search_results):
logger.error(f"Exit: Failed to get response from LLM: {err}")
exit(1)
def improve_blog_intro(blog_content, blog_intro):
"""Combine the given online research and gpt blog content"""
prompt = f"""

91
lib/ai_writers/twitter_ai_writer.py
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@@ -13,59 +13,84 @@ def tweet_writer():
col1, col2 = st.columns([5, 5])
with col1:
hook = st.text_input(
label="What's the tweet about:(Hook)",
placeholder="e.g., Discover the future of tech today!",
help="Provide a compelling opening statement or question to grab attention."
label="**What's the tweet about? (Hook)**",
placeholder="e.g., Discover the future of tech today!",
help="Provide a compelling opening statement or question to grab attention."
)
with col2:
# Collect user inputs with placeholders and help text
target_audience = st.text_input(
label="Target Audience",
label="**Target Audience**",
placeholder="e.g., technology enthusiasts, travel lovers",
help="Describe the audience you want to target with this tweet."
)
col3, col4 = st.columns([5, 5])
with col3:
tweet_tone = st.selectbox(
label="**Tweet Tone**",
options=["Humorous", "Informative", "Inspirational", "Serious", "Casual"],
help="Choose the tone you'd like the tweet to have."
)
with col4:
cta = st.text_input(
label="**Call to Action (Optional)**",
placeholder="e.g., Retweet this if you agree! (Leave blank if not applicable)",
help="Provide a call to action if you'd like to include one."
)
col5, col6 = st.columns([5, 5])
with col5:
keywords_hashtags = st.text_input(
label="**Keywords/Hashtags**",
placeholder="e.g., #AI #Innovation",
help="Provide 2-3 relevant keywords or hashtags."
)
with col6:
tweet_length = st.selectbox(
"Tweet Length (Optional)",
options=["Short (under 100 characters)", "Medium (100-200 characters)", "Long (200+ characters)"],
help="Choose the desired tweet length.",
)
if st.button('**Write Tweets**'):
if not target_audience or not hook:
st.error("🚫 Please provide all required inputs.")
else:
with st.status("Assigning AI professional to write your Google Ads copy..", expanded=True) as status:
response = tweet_generator(target_audience, hook)
with st.status("Assigning AI professional to write your tweets...", expanded=True) as status:
response = tweet_generator(target_audience, hook, tweet_tone, cta, keywords_hashtags, tweet_length)
if response:
st.subheader(f'**🧕👩: Your Tweets!**')
st.markdown(response)
else:
st.error("💥**Failed to write Letter. Please try again!**")
st.error("💥**Failed to generate tweets. Please try again!**")
def tweet_generator(target_audience, hook):
""" Email project_update_writer """
def tweet_generator(target_audience, hook, tone_style, cta, keywords_hashtags, tweet_length):
""" Tweet Generator """
prompt = f"""
You are a social media expert creating tweets for an audience interested in {target_audience}.
Write 5 engaging, concise, and visually appealing tweets that each:
1. Start with a compelling hook based on the following keywords: "{hook}"
2. Include a compelling call to action.
3. Use 2-3 relevant hashtags.
4. Adopt a tone that matches the following options:
- Humorous
- Informative
- Inspirational
- Serious
- Casual
5. Be under 100 characters (including spaces and punctuation).
Here are some examples of call-to-actions to include:
- Retweet this if you agree!
- Share your thoughts in the comments!
- Learn more at [link]
- Follow for more [topic] content
- Like if you're excited about [topic]
Output each tweet separated by a newline.
"""
You are a social media expert creating tweets for an audience interested in {target_audience}.
Write 5 engaging, concise, and visually appealing tweets that each:
1. Start with a compelling hook based on the following input: "{hook}"
2. Include the following call to action: "{cta}"
3. Use 2-3 relevant keywords/hashtags, including: "{keywords_hashtags}"
4. Adopt the following tone/style: "{tone_style}"
5. Adhere to the following length requirement: {tweet_length}
Make sure to keep the tone consistent with the selected style and platform context.
Here are some examples of call-to-actions to include (if no specific CTA was provided):
- Retweet this if you agree!
- Share your thoughts in the comments!
- Learn more at [link]
- Follow for more {target_audience} content.
Output each tweet separated by a newline.
"""
try:
response = llm_text_gen(prompt)

8
lib/gpt_providers/text_generation/gemini_pro_text.py
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@@ -2,7 +2,6 @@
import os
import sys
from pathlib import Path
import streamlit as st
import google.generativeai as genai
from dotenv import load_dotenv
@@ -21,7 +20,7 @@ from tenacity import (
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
def gemini_text_response(prompt, temperature, top_p, n, max_tokens):
def gemini_text_response(prompt, temperature, top_p, n, max_tokens, system_prompt):
""" Common functiont to get response from gemini pro Text. """
#FIXME: Include : https://github.com/google-gemini/cookbook/blob/main/quickstarts/rest/System_instructions_REST.ipynb
try:
@@ -37,7 +36,9 @@ def gemini_text_response(prompt, temperature, top_p, n, max_tokens):
"max_output_tokens": max_tokens,
}
# FIXME: Expose model_name in main_config
model = genai.GenerativeModel(model_name="gemini-1.5-pro-latest", generation_config=generation_config)
model = genai.GenerativeModel(model_name="gemini-1.5-pro-latest",
generation_config=generation_config,
system_instruction=system_prompt)
try:
# text_response = []
response = model.generate_content(prompt, stream=True)
@@ -45,7 +46,6 @@ def gemini_text_response(prompt, temperature, top_p, n, max_tokens):
for chunk in response:
# text_response.append(chunk.text)
print(chunk.text)
#st.write(chunk.text)
else:
print(response)
logger.info(f"Number of Token in Prompt Sent: {model.count_tokens(prompt)}")

18
lib/gpt_providers/text_generation/main_text_generation.py
View File

@@ -27,7 +27,21 @@ def llm_text_gen(prompt):
str: Generated text based on the prompt.
"""
try:
# Read the config param to create system instruction for the LLM.
gpt_provider, model, temperature, max_tokens, top_p, n, fp = read_return_config_section('llm_config')
blog_tone, blog_demographic, blog_type, blog_language, \
blog_output_format, blog_length = read_return_config_section('blog_characteristics')
# Construct the system prompt with the sidebar config params.
system_instructions = f"""
Below are the guidelines to follow:
1). You must respond in {blog_language} language.
2). Tone and Brand Alignment: Adjust your tone, voice, personality for {blog_tone} audience.
3). Make sure your response content length is of {blog_length} words.
4). The type of blog is {blog_type}, write accordingly.
5). The demographic for this content is {blog_demographic}.
6). Your response should be in {blog_output_format} format.
"""
#gpt_provider = check_gpt_provider(gpt_provider)
# Check if API key is provided for the given gpt_provider
@@ -37,7 +51,7 @@ def llm_text_gen(prompt):
if 'google' in gpt_provider.lower():
try:
logger.info("Using Google Gemini Pro text generation model.")
response = gemini_text_response(prompt, temperature, top_p, n, max_tokens)
response = gemini_text_response(prompt, temperature, top_p, n, max_tokens, system_instructions)
return response
except Exception as err:
logger.error(f"Failed to get response from gemini: {err}")
@@ -45,7 +59,7 @@ def llm_text_gen(prompt):
elif 'openai' in gpt_provider.lower():
try:
logger.info(f"Using OpenAI Model: {model} for text Generation.")
response = openai_chatgpt(prompt, model, temperature, max_tokens, top_p, n, fp)
response = openai_chatgpt(prompt, model, temperature, max_tokens, top_p, n, fp, system_instructions)
return response
except Exception as err:
logger.error(f"Failed to get response from Openai: {err}")

5
lib/gpt_providers/text_generation/openai_text_gen.py
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@@ -14,7 +14,7 @@ from tenacity import (
@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6))
def openai_chatgpt(prompt, model, temperature, max_tokens, top_p, n, fp):
def openai_chatgpt(prompt, model, temperature, max_tokens, top_p, n, fp, system_prompt):
"""
Wrapper function for OpenAI's ChatGPT completion.
@@ -45,7 +45,8 @@ def openai_chatgpt(prompt, model, temperature, max_tokens, top_p, n, fp):
client = openai.OpenAI(api_key=os.getenv('OPENAI_API_KEY'))
response = client.chat.completions.create(
model=model,
messages=[{"role": "user", "content": prompt}],
messages=[{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt}],
max_tokens=max_tokens,
n=n,
top_p=top_p,

7
lib/utils/alwrity_utils.py
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@@ -131,8 +131,11 @@ def ai_seo_tools():
"Generate OpenGraph Tags",
"Optimize/Resize Image"
]
choice = st.selectbox("**👇Select AI SEO Tool:**", options, index=0, format_func=lambda x: f"📝 {x}")
# Using st.radio instead of st.selectbox
choice = st.radio("**👇 Select AI SEO Tool:**", options, index=0, format_func=lambda x: f"📝 {x}")
# Handle choices based on the selected option
if choice == "Generate Structured Data - Rich Snippet":
ai_structured_data()
elif choice == "Generate Meta Description for SEO":

2
lib/workspace/alwrity_prompts/alwrity_system_instruction.prompts Normal file
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@@ -0,0 +1,2 @@
alwrity_system_instruction: |