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syntax fixes for retry logic

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This commit is contained in:
Andrew Ridgway 2026-02-03 12:21:17 +10:00
parent a4fb413151
commit 3cd0654306
Signed by: armistace
GPG Key ID: C8D9EAC514B47EF1
1 changed files with 113 additions and 28 deletions
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117
src/ai_generators/ollama_md_generator.py
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@ -91,7 +91,9 @@ class OllamaGenerator:
("human", self.prompt_inject),
]
response = agent_llm.invoke(messages)
return response.text() # ['message']['content']
return (
response.text if hasattr(response, "text") else str(response)
) # ['message']['content']
# Retry mechanism with 30-minute timeout
timeout_seconds = 30 * 60 # 30 minutes
@ -127,8 +129,26 @@ class OllamaGenerator:
def get_draft_embeddings(self, draft_chunks):
"""Get embeddings for the draft chunks"""
embeds = self.ollama_client.embed(model=self.embed_model, input=draft_chunks)
return embeds.get("embeddings", [])
try:
# Handle empty draft chunks
if not draft_chunks:
print("Warning: No draft chunks to embed")
return []
embeds = self.ollama_client.embed(
model=self.embed_model, input=draft_chunks
)
embeddings = embeds.get("embeddings", [])
# Check if embeddings were generated successfully
if not embeddings:
print("Warning: No embeddings generated")
return []
return embeddings
except Exception as e:
print(f"Error generating embeddings: {e}")
return []
def id_generator(self, size=6, chars=string.ascii_uppercase + string.digits):
return "".join(random.choice(chars) for _ in range(size))
@ -145,16 +165,52 @@ class OllamaGenerator:
# self.chroma.delete_collection("blog_creator")
for model in self.agent_models:
print(f"Generating draft from {model} for load into vector database")
draft_chunks = self.split_into_chunks(self.generate_draft(model))
print(f"generating embeds")
try:
draft_content = self.generate_draft(model)
draft_chunks = self.split_into_chunks(draft_content)
# Skip if no content was generated
if not draft_chunks or all(
chunk.strip() == "" for chunk in draft_chunks
):
print(f"Skipping {model} - no content generated")
continue
print(f"generating embeds for {model}")
embeds = self.get_draft_embeddings(draft_chunks)
# Skip if no embeddings were generated
if not embeds:
print(f"Skipping {model} - no embeddings generated")
continue
# Ensure we have the same number of embeddings as chunks
if len(embeds) != len(draft_chunks):
print(
f"Warning: Mismatch between chunks ({len(draft_chunks)}) and embeddings ({len(embeds)}) for {model}"
)
# Truncate or pad to match
min_length = min(len(embeds), len(draft_chunks))
draft_chunks = draft_chunks[:min_length]
embeds = embeds[:min_length]
if min_length == 0:
print(f"Skipping {model} - no valid content/embeddings pairs")
continue
ids = [model + str(i) for i in range(len(draft_chunks))]
chunknumber = list(range(len(draft_chunks)))
metadata = [{"model_agent": model} for index in chunknumber]
print(f"loading into collection")
print(f"loading into collection for {model}")
collection.add(
documents=draft_chunks, embeddings=embeds, ids=ids, metadatas=metadata
documents=draft_chunks,
embeddings=embeds,
ids=ids,
metadatas=metadata,
)
except Exception as e:
print(f"Error processing model {model}: {e}")
# Continue with other models rather than failing completely
continue
return collection
@ -175,18 +231,46 @@ class OllamaGenerator:
"""
def _generate_final_document():
query_embed = self.ollama_client.embed(
try:
embed_result = self.ollama_client.embed(
model=self.embed_model, input=prompt_human
)["embeddings"]
)
query_embed = embed_result.get("embeddings", [])
if not query_embed:
print(
"Warning: Failed to generate query embeddings, using empty list"
)
query_embed = [[]] # Use a single empty embedding as fallback
except Exception as e:
print(f"Error generating query embeddings: {e}")
# Generate empty embeddings as fallback
query_embed = [[]] # Use a single empty embedding as fallback
collection = self.load_to_vector_db()
# Try to query the collection, with fallback for empty collections
try:
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]
# Get documents with error handling
query_result = collection.query(
query_embeddings=query_embed, n_results=100
)
documents = query_result.get("documents", [])
if documents and len(documents) > 0 and len(documents[0]) > 0:
pertinent_draft_info = "\n\n".join(documents[0])
else:
print("Warning: No relevant documents found in collection")
pertinent_draft_info = "No relevant information found in drafts."
except Exception as query_error:
print(f"Error querying collection: {query_error}")
pertinent_draft_info = (
"No relevant information found in drafts due to query error."
)
# print(pertinent_draft_info)
prompt_system = f"""Generate the final, 5000 word, draft of the blog using this information from the drafts: <context>{pertinent_draft_info}</context>
@ -197,8 +281,8 @@ class OllamaGenerator:
("system", prompt_system),
("human", prompt_human),
]
response = self.llm.invoke(messages).text()
return response
response = self.llm.invoke(messages)
return response.text if hasattr(response, "text") else str(response)
try:
# Retry mechanism with 30-minute timeout
@ -257,7 +341,8 @@ class OllamaGenerator:
("system", prompt_system),
("human", prompt_human),
]
ai_message = self.llm.invoke(messages).text()
response = self.llm.invoke(messages)
ai_message = response.text if hasattr(response, "text") else str(response)
return ai_message
# Retry mechanism with 30-minute timeout