src/ai_generators/ollama_md_generator.py

@@ -33,7 +33,7 @@ class OllamaGenerator:
             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:
-                <blog>{self.content}</blog>
+                {self.content}
             """
 
     def split_into_chunks(self, text, chunk_size=100):
@@ -125,12 +125,11 @@ class OllamaGenerator:
             writing a 3000 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 the final version of this blog as a markdown document
-            keeping in mind the context provided by the previous drafts.
+            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
             The basis for the content of the blog is:
-                <blog>{self.content}</blog>
+                {self.content}
             """
         try:
             query_embed = self.ollama_client.embed(model=self.embed_model, input=prompt_system)['embeddings']
@@ -139,9 +138,7 @@ 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, 3000 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
-                            """
+            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()