gardel/lightrag
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Added system prompt support in all modes

Browse Source
This commit is contained in:
MdNazishArmanShorthillsAI
2025-02-17 16:45:00 +05:30
parent da9560d706
commit c2ff17d343
3 changed files with 38 additions and 18 deletions
Download Patch File Download Diff File Expand all files Collapse all files

30
lightrag/operate.py
View File

@@ -613,7 +613,7 @@ async def kg_query(
query_param: QueryParam,
global_config: dict[str, str],
hashing_kv: BaseKVStorage | None = None,
prompt: str | None = None,
system_prompt: str | None = None,
) -> str:
# Handle cache
use_model_func = global_config["llm_model_func"]
@@ -677,7 +677,7 @@ async def kg_query(
query_param.conversation_history, query_param.history_turns
)
sys_prompt_temp = prompt if prompt else PROMPTS["rag_response"]
sys_prompt_temp = system_prompt if system_prompt else PROMPTS["rag_response"]
sys_prompt = sys_prompt_temp.format(
context_data=context,
response_type=query_param.response_type,
@@ -828,6 +828,7 @@ async def mix_kg_vector_query(
query_param: QueryParam,
global_config: dict[str, str],
hashing_kv: BaseKVStorage | None = None,
system_prompt: str | None = None,
) -> str | AsyncIterator[str]:
"""
Hybrid retrieval implementation combining knowledge graph and vector search.
@@ -962,15 +963,19 @@ async def mix_kg_vector_query(
return {"kg_context": kg_context, "vector_context": vector_context}
# 5. Construct hybrid prompt
sys_prompt = PROMPTS["mix_rag_response"].format(
kg_context=kg_context
if kg_context
else "No relevant knowledge graph information found",
vector_context=vector_context
if vector_context
else "No relevant text information found",
response_type=query_param.response_type,
history=history_context,
sys_prompt = (
system_prompt
if system_prompt
else PROMPTS["mix_rag_response"].format(
kg_context=kg_context
if kg_context
else "No relevant knowledge graph information found",
vector_context=vector_context
if vector_context
else "No relevant text information found",
response_type=query_param.response_type,
history=history_context,
)
)
if query_param.only_need_prompt:
@@ -1599,6 +1604,7 @@ async def naive_query(
query_param: QueryParam,
global_config: dict[str, str],
hashing_kv: BaseKVStorage | None = None,
system_prompt: str | None = None,
) -> str | AsyncIterator[str]:
# Handle cache
use_model_func = global_config["llm_model_func"]
@@ -1651,7 +1657,7 @@ async def naive_query(
query_param.conversation_history, query_param.history_turns
)
sys_prompt_temp = PROMPTS["naive_rag_response"]
sys_prompt_temp = system_prompt if system_prompt else PROMPTS["naive_rag_response"]
sys_prompt = sys_prompt_temp.format(
content_data=section,
response_type=query_param.response_type,