mirror of
https://github.com/hwchase17/langchain.git
synced 2025-04-27 11:41:51 +00:00
Deprecating sql_database access for creating UC functions for agent tools (#29745)
Thank you for contributing to LangChain!
- [ ] **PR title**: "package: description"
- Where "package" is whichever of langchain, community, core, etc. is
being modified. Use "docs: ..." for purely docs changes, "infra: ..."
for CI changes.
- Example: "community: add foobar LLM"
- [ ] **PR message**: ***Delete this entire checklist*** and replace
with
- **Description:** a description of the change
- **Issue:** the issue # it fixes, if applicable
- **Dependencies:** any dependencies required for this change
- **Twitter handle:** if your PR gets announced, and you'd like a
mention, we'll gladly shout you out!
- [ ] **Add tests and docs**: If you're adding a new integration, please
include
1. a test for the integration, preferably unit tests that do not rely on
network access,
2. an example notebook showing its use. It lives in
`docs/docs/integrations` directory.
- [ ] **Lint and test**: Run `make format`, `make lint` and `make test`
from the root of the package(s) you've modified. See contribution
guidelines for more: https://python.langchain.com/docs/contributing/
Additional guidelines:
- Make sure optional dependencies are imported within a function.
- Please do not add dependencies to pyproject.toml files (even optional
ones) unless they are required for unit tests.
- Most PRs should not touch more than one package.
- Changes should be backwards compatible.
- If you are adding something to community, do not re-import it in
langchain.
If no one reviews your PR within a few days, please @-mention one of
baskaryan, efriis, eyurtsev, ccurme, vbarda, hwchase17.
---------
Co-authored-by: ccurme <chester.curme@gmail.com>
This commit is contained in:
Sunish Sheth
GitHub
parent
a0970d8d7e
commit
f42dafa809
@ -21,7 +21,6 @@ Notebook | Description
|
||||
[code-analysis-deeplake.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/code-analysis-deeplake.ipynb) | Analyze its own code base with the help of gpt and activeloop's deep lake.
|
||||
[custom_agent_with_plugin_retri...](https://github.com/langchain-ai/langchain/tree/master/cookbook/custom_agent_with_plugin_retrieval.ipynb) | Build a custom agent that can interact with ai plugins by retrieving tools and creating natural language wrappers around openapi endpoints.
|
||||
[custom_agent_with_plugin_retri...](https://github.com/langchain-ai/langchain/tree/master/cookbook/custom_agent_with_plugin_retrieval_using_plugnplai.ipynb) | Build a custom agent with plugin retrieval functionality, utilizing ai plugins from the `plugnplai` directory.
|
||||
[databricks_sql_db.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/databricks_sql_db.ipynb) | Connect to databricks runtimes and databricks sql.
|
||||
[deeplake_semantic_search_over_...](https://github.com/langchain-ai/langchain/tree/master/cookbook/deeplake_semantic_search_over_chat.ipynb) | Perform semantic search and question-answering over a group chat using activeloop's deep lake with gpt4.
|
||||
[elasticsearch_db_qa.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/elasticsearch_db_qa.ipynb) | Interact with elasticsearch analytics databases in natural language and build search queries via the elasticsearch dsl API.
|
||||
[extraction_openai_tools.ipynb](https://github.com/langchain-ai/langchain/tree/master/cookbook/extraction_openai_tools.ipynb) | Structured Data Extraction with OpenAI Tools
|
||||
|
||||
@ -1,273 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "707d13a7",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Databricks\n",
|
||||
"\n",
|
||||
"This notebook covers how to connect to the [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the SQLDatabase wrapper of LangChain.\n",
|
||||
"It is broken into 3 parts: installation and setup, connecting to Databricks, and examples."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "0076d072",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Installation and Setup"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "739b489b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install databricks-sql-connector"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "73113163",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Connecting to Databricks\n",
|
||||
"\n",
|
||||
"You can connect to [Databricks runtimes](https://docs.databricks.com/runtime/index.html) and [Databricks SQL](https://www.databricks.com/product/databricks-sql) using the `SQLDatabase.from_databricks()` method.\n",
|
||||
"\n",
|
||||
"### Syntax\n",
|
||||
"```python\n",
|
||||
"SQLDatabase.from_databricks(\n",
|
||||
" catalog: str,\n",
|
||||
" schema: str,\n",
|
||||
" host: Optional[str] = None,\n",
|
||||
" api_token: Optional[str] = None,\n",
|
||||
" warehouse_id: Optional[str] = None,\n",
|
||||
" cluster_id: Optional[str] = None,\n",
|
||||
" engine_args: Optional[dict] = None,\n",
|
||||
" **kwargs: Any)\n",
|
||||
"```\n",
|
||||
"### Required Parameters\n",
|
||||
"* `catalog`: The catalog name in the Databricks database.\n",
|
||||
"* `schema`: The schema name in the catalog.\n",
|
||||
"\n",
|
||||
"### Optional Parameters\n",
|
||||
"There following parameters are optional. When executing the method in a Databricks notebook, you don't need to provide them in most of the cases.\n",
|
||||
"* `host`: The Databricks workspace hostname, excluding 'https://' part. Defaults to 'DATABRICKS_HOST' environment variable or current workspace if in a Databricks notebook.\n",
|
||||
"* `api_token`: The Databricks personal access token for accessing the Databricks SQL warehouse or the cluster. Defaults to 'DATABRICKS_TOKEN' environment variable or a temporary one is generated if in a Databricks notebook.\n",
|
||||
"* `warehouse_id`: The warehouse ID in the Databricks SQL.\n",
|
||||
"* `cluster_id`: The cluster ID in the Databricks Runtime. If running in a Databricks notebook and both 'warehouse_id' and 'cluster_id' are None, it uses the ID of the cluster the notebook is attached to.\n",
|
||||
"* `engine_args`: The arguments to be used when connecting Databricks.\n",
|
||||
"* `**kwargs`: Additional keyword arguments for the `SQLDatabase.from_uri` method."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b11c7e48",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Examples"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"id": "8102bca0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Connecting to Databricks with SQLDatabase wrapper\n",
|
||||
"from langchain_community.utilities import SQLDatabase\n",
|
||||
"\n",
|
||||
"db = SQLDatabase.from_databricks(catalog=\"samples\", schema=\"nyctaxi\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "9dd36f58",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Creating a OpenAI Chat LLM wrapper\n",
|
||||
"from langchain_openai import ChatOpenAI\n",
|
||||
"\n",
|
||||
"llm = ChatOpenAI(temperature=0, model_name=\"gpt-4\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5b5c5f1a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### SQL Chain example\n",
|
||||
"\n",
|
||||
"This example demonstrates the use of the [SQL Chain](https://python.langchain.com/en/latest/modules/chains/examples/sqlite.html) for answering a question over a Databricks database."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "36f2270b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain_community.utilities import SQLDatabaseChain\n",
|
||||
"\n",
|
||||
"db_chain = SQLDatabaseChain.from_llm(llm, db, verbose=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "4e2b5f25",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new SQLDatabaseChain chain...\u001b[0m\n",
|
||||
"What is the average duration of taxi rides that start between midnight and 6am?\n",
|
||||
"SQLQuery:\u001b[32;1m\u001b[1;3mSELECT AVG(UNIX_TIMESTAMP(tpep_dropoff_datetime) - UNIX_TIMESTAMP(tpep_pickup_datetime)) as avg_duration\n",
|
||||
"FROM trips\n",
|
||||
"WHERE HOUR(tpep_pickup_datetime) >= 0 AND HOUR(tpep_pickup_datetime) < 6\u001b[0m\n",
|
||||
"SQLResult: \u001b[33;1m\u001b[1;3m[(987.8122786304605,)]\u001b[0m\n",
|
||||
"Answer:\u001b[32;1m\u001b[1;3mThe average duration of taxi rides that start between midnight and 6am is 987.81 seconds.\u001b[0m\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'The average duration of taxi rides that start between midnight and 6am is 987.81 seconds.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"db_chain.run(\n",
|
||||
" \"What is the average duration of taxi rides that start between midnight and 6am?\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e496d5e5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### SQL Database Agent example\n",
|
||||
"\n",
|
||||
"This example demonstrates the use of the [SQL Database Agent](/docs/integrations/tools/sql_database) for answering questions over a Databricks database."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "9918e86a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.agents import create_sql_agent\n",
|
||||
"from langchain_community.agent_toolkits import SQLDatabaseToolkit\n",
|
||||
"\n",
|
||||
"toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n",
|
||||
"agent = create_sql_agent(llm=llm, toolkit=toolkit, verbose=True)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "c484a76e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\n",
|
||||
"\n",
|
||||
"\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
|
||||
"\u001b[32;1m\u001b[1;3mAction: list_tables_sql_db\n",
|
||||
"Action Input: \u001b[0m\n",
|
||||
"Observation: \u001b[38;5;200m\u001b[1;3mtrips\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3mI should check the schema of the trips table to see if it has the necessary columns for trip distance and duration.\n",
|
||||
"Action: schema_sql_db\n",
|
||||
"Action Input: trips\u001b[0m\n",
|
||||
"Observation: \u001b[33;1m\u001b[1;3m\n",
|
||||
"CREATE TABLE trips (\n",
|
||||
"\ttpep_pickup_datetime TIMESTAMP, \n",
|
||||
"\ttpep_dropoff_datetime TIMESTAMP, \n",
|
||||
"\ttrip_distance FLOAT, \n",
|
||||
"\tfare_amount FLOAT, \n",
|
||||
"\tpickup_zip INT, \n",
|
||||
"\tdropoff_zip INT\n",
|
||||
") USING DELTA\n",
|
||||
"\n",
|
||||
"/*\n",
|
||||
"3 rows from trips table:\n",
|
||||
"tpep_pickup_datetime\ttpep_dropoff_datetime\ttrip_distance\tfare_amount\tpickup_zip\tdropoff_zip\n",
|
||||
"2016-02-14 16:52:13+00:00\t2016-02-14 17:16:04+00:00\t4.94\t19.0\t10282\t10171\n",
|
||||
"2016-02-04 18:44:19+00:00\t2016-02-04 18:46:00+00:00\t0.28\t3.5\t10110\t10110\n",
|
||||
"2016-02-17 17:13:57+00:00\t2016-02-17 17:17:55+00:00\t0.7\t5.0\t10103\t10023\n",
|
||||
"*/\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3mThe trips table has the necessary columns for trip distance and duration. I will write a query to find the longest trip distance and its duration.\n",
|
||||
"Action: query_checker_sql_db\n",
|
||||
"Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
|
||||
"Observation: \u001b[31;1m\u001b[1;3mSELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3mThe query is correct. I will now execute it to find the longest trip distance and its duration.\n",
|
||||
"Action: query_sql_db\n",
|
||||
"Action Input: SELECT trip_distance, tpep_dropoff_datetime - tpep_pickup_datetime as duration FROM trips ORDER BY trip_distance DESC LIMIT 1\u001b[0m\n",
|
||||
"Observation: \u001b[36;1m\u001b[1;3m[(30.6, '0 00:43:31.000000000')]\u001b[0m\n",
|
||||
"Thought:\u001b[32;1m\u001b[1;3mI now know the final answer.\n",
|
||||
"Final Answer: The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.\u001b[0m\n",
|
||||
"\n",
|
||||
"\u001b[1m> Finished chain.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'The longest trip distance is 30.6 miles and it took 43 minutes and 31 seconds.'"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"agent.run(\"What is the longest trip distance and how long did it take?\")"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -103,14 +103,7 @@ See [MLflow LangChain Integration](/docs/integrations/providers/mlflow_tracking)
|
||||
|
||||
SQLDatabase
|
||||
-----------
|
||||
You can connect to Databricks SQL using the SQLDatabase wrapper of LangChain.
|
||||
```
|
||||
from langchain.sql_database import SQLDatabase
|
||||
|
||||
db = SQLDatabase.from_databricks(catalog="samples", schema="nyctaxi")
|
||||
```
|
||||
|
||||
See [Databricks SQL Agent](https://docs.databricks.com/en/large-language-models/langchain.html#databricks-sql-agent) for how to connect Databricks SQL with your LangChain Agent as a powerful querying tool.
|
||||
To connect to Databricks SQL or query structured data, see the [Databricks structured retriever tool documentation](https://docs.databricks.com/en/generative-ai/agent-framework/structured-retrieval-tools.html#table-query-tool) and to create an agent using the above created SQL UDF see [Databricks UC Integration](https://docs.unitycatalog.io/ai/integrations/langchain/).
|
||||
|
||||
Open Models
|
||||
-----------
|
||||
|
||||
@ -139,6 +139,14 @@ class SQLDatabase:
|
||||
return cls(create_engine(database_uri, **_engine_args), **kwargs)
|
||||
|
||||
@classmethod
|
||||
@deprecated(
|
||||
"0.3.18",
|
||||
message="For performing structured retrieval using Databricks SQL, "
|
||||
"see the latest best practices and recommended APIs at "
|
||||
"https://docs.unitycatalog.io/ai/integrations/langchain/ " # noqa: E501
|
||||
"instead",
|
||||
removal="1.0",
|
||||
)
|
||||
def from_databricks(
|
||||
cls,
|
||||
catalog: str,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user