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import opik
from src.api.models.api_models import SearchResult
from src.api.models.provider_models import ModelConfig
config = ModelConfig()
PROMPT = """
You are a skilled research assistant specialized in analyzing Substack, Medium and other newsletters.
Respond to the user’s query using the provided context from these articles,
that is retrieved from a vector database without relying on outside knowledge or assumptions.
### Output Rules:
- Write a detailed, structured answer using **Markdown** (headings, bullet points,
short or long paragraphs as appropriate).
- Use up to **{tokens} tokens** without exceeding this limit.
- Only include facts from the provided context from the articles.
- Attribute each fact to the correct author(s) and source, and include **clickable links**.
- If the article author and feed author differ, mention both.
- There is no need to mention that you based your answer on the provided context.
- But if no relevant information exists, clearly state this and provide a fallback suggestion.
- At the very end, include a **funny quote** and wish the user a great day.
### Query:
{query}
### Context Articles:
{context_texts}
### Final Answer:
"""
# Create a new prompt
prompt = opik.Prompt(
name="newsletter_research_assistant", prompt=PROMPT, metadata={"environment": "development"}
)
def build_research_prompt(
contexts: list[SearchResult],
query: str = "",
tokens: int = config.max_completion_tokens,
) -> str:
"""Construct a research-focused LLM prompt using the given query
and supporting context documents.
The prompt enforces Markdown formatting, citations, and strict length guidance.
Args:
contexts (list[SearchResult]): List of context documents with metadata.
query (str): The user's research query.
tokens (int): Maximum number of tokens for the LLM response.
Returns:
str: The formatted prompt ready for LLM consumption.
"""
# Join all retrieved contexts into a readable format
context_texts = "\n\n".join(
(
f"- Feed Name: {r.feed_name}\n"
f" Article Title: {r.title}\n"
f" Article Author(s): {r.article_author}\n"
f" Feed Author: {r.feed_author}\n"
f" URL: {r.url}\n"
f" Snippet: {r.chunk_text}"
)
for r in contexts
)
return PROMPT.format(
query=query,
context_texts=context_texts,
tokens=tokens,
)
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