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Based on the provided extracts, there is no direct interaction mentioned between
ChatMemoryBuffer and
SQLTableRetrieverQueryEngine. However, I can provide information on how to use each of them separately.
ChatMemoryBuffer is a simple buffer for storing chat history. It has several methods such as
from_dict,
from_orm,
from_string,
get,
get_all, and
json for various operations like creating a chat memory buffer from a dictionary or a string, getting the chat history, and generating a JSON representation of the model. Here is an example of how to use
from_string method:
ChatMemoryBuffer.from_string(json_str)
On the other hand,
SQLTableRetrieverQueryEngine is used to construct a query engine that can retrieve relevant table schema and synthesize a SQL query and a response from the results of that query. Here is an example of how to use it:
query_engine = SQLTableRetrieverQueryEngine(
sql_database, obj_index.as_retriever(similarity_top_k=1)
)
response = query_engine.query("Which city has the highest population?")
print(response)In this example,
sql_database is your SQLDatabase and
obj_index is your table schema index.
For more detailed information, you can refer to the
LlamaIndex documentation ().
Please note that the interaction between
ChatMemoryBuffer and
SQLTableRetrieverQueryEngine might require additional steps or methods that are not mentioned in the provided extracts.
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Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:
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