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from langchain_core.prompts.chat import ChatPromptTemplate
from langchain_ollama import ChatOllama
from langchain_core.output_parsers import StrOutputParser
def create_query_rewriter(llm):
"""
Create a query rewriter to optimize retrieval.
Returns:
Callable: Query rewriter function
"""
# Prompt for query rewriting
system = """You are a question re-writer that converts an input question to a better version that is optimized
for vectorstore retrieval. Look at the input and try to reason about the underlying semantic intent / meaning."""
re_write_prompt = ChatPromptTemplate.from_messages([
("system", system),
("human", "Here is the initial question: \n\n {question} \n Formulate an improved question."),
])
# Create query rewriter chain
return re_write_prompt | llm | StrOutputParser()
def rewrite_query(question: str, llm):
"""
Rewrite a given query to optimize retrieval.
Args:
question (str): Original user question
Returns:
str: Rewritten query
"""
query_rewriter = create_query_rewriter(llm)
try:
rewritten_query = query_rewriter.invoke({"question": question})
return rewritten_query
except Exception as e:
print(f"Query rewriting error: {e}")
return question
if __name__ == "__main__":
# Example usage
test_queries = [
"Tell me about AI agents",
"What do we know about memory in AI systems?",
"Bears draft strategy"
]
llm = ChatOllama(model = "llama3.2", temperature = 0.1, num_predict = 256, top_p=0.5)
for query in test_queries:
rewritten = rewrite_query(query, llm)
print(f"Original: {query}")
print(f"Rewritten: {rewritten}\n") |