flip some prompting around

Reviewed-on: #16

.gitea/workflows/deploy.yml

@@ -1,7 +1,7 @@
 name: Create Blog Article if new notes exist
 on:
     schedule:
-        - cron: "15 3 * * *"
+        - cron: "15 18 * * *"
     push:
         branches:
             - master

src/ai_generators/ollama_md_generator.py

@@ -26,14 +26,14 @@ 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 3000 word draft blog article for other tech enthusiasts.
+            writing a 5000 word draft blog article 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.
             The title for the blog is {self.inner_title}.
             Do not output the title in the markdown.
             The basis for the content of the blog is:
-                {self.content}
+                <blog>{self.content}</blog>
             """
 
     def split_into_chunks(self, text, chunk_size=100):
@@ -71,8 +71,8 @@ class OllamaGenerator:
             top_k = int(random.uniform(30, 80))
             agent_llm = ChatOllama(model=model, temperature=temp, top_p=top_p, top_k=top_k)
             messages = [
-                ("system", self.prompt_inject),
+                ("system", "You are a creative writer specialising in writing about technology"),
-                ("human", "make the blog post in a format to be edited easily" )
+                ("human", self.prompt_inject )
             ]
             response = agent_llm.invoke(messages)
             # self.response = self.ollama_client.chat(model=model,
@@ -119,26 +119,30 @@ class OllamaGenerator:
 
     def generate_markdown(self) -> str:
 
-        prompt_system = f"""
+        prompt_human = f"""
             You are an editor taking information from {len(self.agent_models)} Software
             Developers and Data experts
-            writing a 3000 word blog article. You like when they use almost no code examples.
+            writing a 5000 word blog article. 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.
+            As this person produce the final version of this blog as a markdown document
+            keeping in mind the context provided by the previous drafts.
+            You are to produce the content not placeholders for further editors
             The title for the blog is {self.inner_title}.
             Do not output the title in the markdown. Avoid repeated sentences
             The basis for the content of the blog is:
-                {self.content}
+                <blog>{self.content}</blog>
             """
         try:
-            query_embed = self.ollama_client.embed(model=self.embed_model, input=prompt_system)['embeddings']
+            query_embed = self.ollama_client.embed(model=self.embed_model, input=prompt_human)['embeddings']
             collection = self.load_to_vector_db()
             collection_query = collection.query(query_embeddings=query_embed, n_results=100)
             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 in markdown, do not wrap in markdown tags"
+            prompt_system = f"""Generate the final, 5000 word, draft of the blog using this information from the drafts: <context>{pertinent_draft_info}</context>
+                            - Only output in markdown, do not wrap in markdown tags, Only provide the draft not a commentary on the drafts in the context
+                            """
             print("Generating final document")
             messages = [("system", prompt_system), ("human", prompt_human),]
             self.response = self.llm.invoke(messages).text()