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language cleanup for integration testing

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armistace 2025-06-02 12:32:21 +10:00
parent 9a9228bc07
commit 6e117e3ce9
1 changed files with 5 additions and 5 deletions
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10
src/ai_generators/ollama_md_generator.py
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@ -20,7 +20,7 @@ class OllamaGenerator:
self.llm = ChatOllama(model=self.ollama_model, temperature=0.6, top_p=0.5) #This is the level head in the room
self.prompt_inject = f"""
You are a journalist, Software Developer and DevOps expert
writing a 1000 word draft blog for other tech enthusiasts.
writing a 3000 word draft blog for other tech enthusiasts.
You like to use almost no code examples and prefer to talk
in a light comedic tone. You are also Australian
As this person write this blog as a markdown document.
@ -116,9 +116,9 @@ class OllamaGenerator:
prompt_system = f"""
You are an editor taking information from {len(self.agent_models)} Software
Developers and Data experts
writing a 3000 word blog for other tech enthusiasts.
You like when they use almost no code examples and the
voice is in a light comedic tone. You are also Australian
writing a 3000 word blog. You like when they use almost no code examples.
You are also Australian. The content may have light comedic elements,
you are more professional and will attempt to tone these down
As this person produce and an amalgamtion of this blog as a markdown document.
The title for the blog is {self.inner_title}.
Do not output the title in the markdown. Avoid repeated sentences
@ -132,7 +132,7 @@ class OllamaGenerator:
print("Showing pertinent info from drafts used in final edited edition")
pertinent_draft_info = '\n\n'.join(collection.query(query_embeddings=query_embed, n_results=100)['documents'][0])
#print(pertinent_draft_info)
prompt_human = f"Generate the final document using this information from the drafts: {pertinent_draft_info} - ONLY OUTPUT THE MARKDOWN"
prompt_human = f"Generate the final document using this information from the drafts: {pertinent_draft_info} - Only output in markdown, do not wrap in markdown tags"
print("Generating final document")
messages = [("system", prompt_system), ("human", prompt_human),]
self.response = self.llm.invoke(messages).text()