language cleanup for integration testing

Reviewed-on: #2

src/ai_generators/ollama_md_generator.py

@@ -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()