LangChain
LangChain is a machine learning framework for developing applications powered by large language models (LLMs).
How to Bring LangChain Models to MindsDB
We use the CREATE MODEL statement to bring LangChain models to MindsDB.
CREATE MODEL tool_based_agent
PREDICT completion
USING
engine = 'langchain',
prompt_template = 'Answer the users input in a helpful way: {{input}}';
Before using the LangChain engine, you need to create it based on the handler available in MindsDB:
CREATE ML_ENGINE langchain
FROM langchain;
Here, we create the tool_based_agent model using the LangChain engine, as defined in the engine parameter. This model answers users’ questions in a helpful way, as defined in the prompt_template parameter, which specifies input as the input column when calling the model. Agents and Tools are some of the main abstractions that LangChain offers. You can read more about them in the LangChain documentation ([1], [2]).
There are three different tools utilized by this agent:
- MindsDB is the internal MindsDB executor.
- Metadata fetches the metadata information for the available tables.
- Write is able to write agent responses into a MindsDB data source.
Each tool exposes the internal MindsDB executor in a different way to perform its tasks, effectively enabling the agent model to read from (and potentially write to) data sources or models available in the active MindsDB project.
Example 1: Describing Connected Data Sources
We can ask questions about data sources connected to MindsDB.
SELECT input, completion
FROM tool_based_agent
WHERE input = 'Could you describe the `mysql_demo_db.house_sales` table please?'
USING
verbose = True,
tools = [],
max_iterations = 10;
On execution, we get:
+------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------+
| input | completion |
+------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------+
| Could you describe the `mysql_demo_db.house_sales` table please? | The `mysql_demo_db.house_sales` table has four columns: `saledate` (text), `house_price_moving_average` (integer), `type` (text), and `bedrooms` (integer). |
+------------------------------------------------------------------+-------------------------------------------------------------------------------------------------------------------------------------------------------------+
To get information about the mysql_demo_db.house_sales table, the agent uses the Metadata tool. Then the agent prepares the response.
The Write tool only comes into play when writing to other data sources. By default, the agent is able to write an answer back to the user (through standard output), like in this case.
Example 2: Analyzing Data
We can ask questions to analyze the available data.
SELECT input, completion
FROM tool_based_agent
WHERE input = 'I want to know the average number of beds in the downtown neighbourhood as per the `mysql_demo_db.home_rentals` table'
USING
verbose = True,
tools = [],
max_iterations = 10;
On execution, we get:
+-----------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------+
| input | completion |
+-----------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------+
| I want to know the average number of beds in the downtown neighbourhood as per the `mysql_demo_db.home_rentals` table | The average number of beds in the downtown neighbourhood as per the `mysql_demo_db.home_rentals` table is 1.6. |
+-----------------------------------------------------------------------------------------------------------------------+----------------------------------------------------------------------------------------------------------------+
Here, the model uses the Metadata tool again to fetch the column information. As there is no beds column in the mysql_demo_db.home_rentals table, it uses the number_of_rooms column and writes the following query:
SELECT AVG(number_of_rooms)
FROM mysql_demo_db.home_rentals
WHERE neighborhood = 'downtown';
This query returns the value of 1.6, which is then used to write an answer.
Example 3: Retrieving Data
We can ask the model to retrieve specific data.
SELECT input, completion
FROM tool_based_agent
WHERE input = 'There is a property in the south_side neighborhood with an initial price of 2543 the `mysql_demo_db.home_rentals` table. What are some other deatils of this listing?'
USING
verbose = True,
tools = [],
max_iterations = 10;
On execution, we get:
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| input | completion |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
| There is a property in the south_side neighborhood with an initial price of 2543 the `mysql_demo_db.home_rentals` table. What are some other deatils of this listing? | The property in the south_side neighborhood with an initial price of 2543 has 1 bedroom, 1 bathroom, is 630 sqft, has been on the market for 11 days, and has a rental price of 2543.0. |
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Here, the model uses the Metadata tool again to fetch information about the table. Then, it creates and executes the following query:
SELECT *
FROM mysql_demo_db.home_rentals
WHERE neightborhood = 'south_side'
AND initial_price = 2543;
On execution, the model gets this output:
+---------------+-------------------+----+--------+--------------+-------------+------------+------------+
|number_of_rooms|number_of_bathrooms|sqft|location|days_on_market|initial_price|neighborhood|rental_price|
+---------------+-------------------+----+--------+--------------+-------------+------------+------------+
|1 |1 |630 |great |11 |2543 |south_side |2543 |
+---------------+-------------------+----+--------+--------------+-------------+------------+------------+
Consequently, it takes the query output and writes an answer.
Example 4: Inserting Data
We can ask the model to insert data into a table, assuming we have sufficient privileges on that database.
SELECT input, completion
FROM tool_based_agent
WHERE input = 'I want to insert a new record into the `local_database.home_rentals` table. Its details are: number_of_rooms=4, number_of_bathrooms=2, sqft=950'
USING
verbose = True,
tools = [],
max_iterations = 10;
On execution, we get:
+----------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
| input | completion |
+----------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
| I want to insert a new record into the `local_database.home_rentals_inserted` table. Its details are: number_of_rooms=4, number_of_bathrooms=2, sqft=950 | The record has been successfully inserted into the `local_database.home_rentals` table with the details: number_of_rooms=4, number_of_bathrooms=2, sqft=950. |
+----------------------------------------------------------------------------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------+
The agent uses the Write tool to INSERT INTO the local_database database.