Missing function

Nobody has claimed this yet.

The tutoriels for LLM topic generation use textgeneration.py or openai, thouse classes have this function to insert topics and documents into a custom prompt.

def _create_prompt(self, docs, topic, topics):
keywords = ", ".join(list(zip(*topics[topic]))[0])

# Use the default prompt and replace keywords
if self.prompt == DEFAULT_PROMPT:
prompt = self.prompt.replace("[KEYWORDS]", keywords)

# Use a prompt that leverages either keywords or documents in
# a custom location
else:
prompt = self.prompt
if "[KEYWORDS]" in prompt:
prompt = prompt.replace("[KEYWORDS]", keywords)
if "[DOCUMENTS]" in prompt:
to_replace = ""
for doc in docs:
to_replace += f"- {doc}\n"
prompt = prompt.replace("[DOCUMENTS]", to_replace)

return prompt


It seems like this function is missing from the Langchain wrapper and therefore using a langchain pipeline will not replace the prompt keywords with DOCUMENTS/TOPICS

I will write my own wrapper for now, just wanted confirmation if this is the reason topics were not inserted into my prompt or if I am missing something crucial here in comparison to the other wrapers.

Start by locating the LangChain wrapper and compare its prompt construction with the textgeneration.py and OpenAI wrappers described in the issue. Verify how custom prompts handle [KEYWORDS] and [DOCUMENTS]. Done means the LangChain pipeline inserts the expected topics and documents into those placeholders, with matching coverage in the relevant tests if present.

Written by the indexing model from the issue text.