Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

fix templating of language in prompts #343

Merged
merged 1 commit into from
Nov 28, 2024
Merged

fix templating of language in prompts #343

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
9 changes: 8 additions & 1 deletion lightrag/operate.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -59,6 +59,9 @@ async def _handle_entity_relation_summary(
llm_max_tokens = global_config["llm_model_max_token_size"]
tiktoken_model_name = global_config["tiktoken_model_name"]
summary_max_tokens = global_config["entity_summary_to_max_tokens"]
language = global_config["addon_params"].get(
"language", PROMPTS["DEFAULT_LANGUAGE"]
)

tokens = encode_string_by_tiktoken(description, model_name=tiktoken_model_name)
if len(tokens) < summary_max_tokens: # No need for summary
Expand All @@ -70,6 +73,7 @@ async def _handle_entity_relation_summary(
context_base = dict(
entity_name=entity_or_relation_name,
description_list=use_description.split(GRAPH_FIELD_SEP),
language=language,
)
use_prompt = prompt_template.format(**context_base)
logger.debug(f"Trigger summary: {entity_or_relation_name}")
Expand Down Expand Up @@ -444,6 +448,9 @@ async def kg_query(
)
else:
examples = "\n".join(PROMPTS["keywords_extraction_examples"])
language = global_config["addon_params"].get(
"language", PROMPTS["DEFAULT_LANGUAGE"]
)

# Set mode
if query_param.mode not in ["local", "global", "hybrid"]:
Expand All @@ -453,7 +460,7 @@ async def kg_query(
# LLM generate keywords
use_model_func = global_config["llm_model_func"]
kw_prompt_temp = PROMPTS["keywords_extraction"]
kw_prompt = kw_prompt_temp.format(query=query, examples=examples)
kw_prompt = kw_prompt_temp.format(query=query, examples=examples, language=language)
result = await use_model_func(kw_prompt)
logger.info("kw_prompt result:")
print(result)
Expand Down
6 changes: 3 additions & 3 deletions lightrag/prompt.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@
3. Identify high-level key words that summarize the main concepts, themes, or topics of the entire text. These should capture the overarching ideas present in the document.
Format the content-level key words as ("content_keywords"{tuple_delimiter}<high_level_keywords>)

4. Return output in English as a single list of all the entities and relationships identified in steps 1 and 2. Use **{record_delimiter}** as the list delimiter.
4. Return output in {language} as a single list of all the entities and relationships identified in steps 1 and 2. Use **{record_delimiter}** as the list delimiter.

5. When finished, output {completion_delimiter}

Expand Down Expand Up @@ -131,7 +131,7 @@
Please concatenate all of these into a single, comprehensive description. Make sure to include information collected from all the descriptions.
If the provided descriptions are contradictory, please resolve the contradictions and provide a single, coherent summary.
Make sure it is written in third person, and include the entity names so we the have full context.
Use Chinese as output language.
Use {language} as output language.

#######
-Data-
Expand Down Expand Up @@ -178,7 +178,7 @@
PROMPTS["keywords_extraction"] = """---Role---

You are a helpful assistant tasked with identifying both high-level and low-level keywords in the user's query.
Use Chinese as output language.
Use {language} as output language.

---Goal---

Expand Down