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Add some logging into the powerbi tool so that you can see the queries
being sent to PBI and attempts to correct them.
<!-- Remove if not applicable -->
Fixes # (issue)
#### Before submitting
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# Reduce DB query error rate
If you use sql agent of `SQLDatabaseToolkit` to query data, it is prone
to errors in query fields and often uses fields that do not exist in
database tables for queries. However, the existing prompt does not
effectively make the agent aware that there are problems with the fields
they query. At this time, we urgently need to improve the prompt so that
the agent realizes that they have queried non-existent fields and allows
them to use the `schema_sql_db`, that is,` ListSQLDatabaseTool` first
queries the corresponding fields in the table in the database, and then
uses `QuerySQLDatabaseTool` for querying.
There is a demo of my project to show this problem.
**Original Agent**
```python
def create_mysql_kit():
db = SQLDatabase.from_uri("mysql+pymysql://xxxxxxx")
llm = OpenAI(temperature=0)
toolkit = SQLDatabaseToolkit(db=db, llm=llm)
agent_executor = create_sql_agent(
llm=OpenAI(temperature=0),
toolkit=toolkit,
verbose=True
)
agent_executor.run("Who are the users of sysuser in this system? Tell me the username of all users")
if __name__ == '__main__':
create_mysql_kit()
```
**original output**
```
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""
Observation: app_sysrole_menus, app_bimfacemodel, app_project_users, app_measuringpointdata, auth_user, auth_user_groups, django_apscheduler_djangojobexecution, app_project, app_elementpoint, django_apscheduler_djangojob, django_content_type, app_sysrole, django_admin_log, app_bimfaceaccount, app_measuringpoint_warning_thresholds, app_measuringpoint, app_company, auth_group_permissions, app_sysuser, app_sysuser_companies, app_sysmenu, app_datawarningthreshold, auth_group, auth_permission, app_datawarningrecord, auth_user_user_permissions, app_bimfaceaccount_bimface_models, django_migrations, app_measuringitem, django_session
Thought: I should query the app_sysuser table to get the users in the system.
Action: query_sql_db
Action Input: SELECT username FROM app_sysuser LIMIT 10;
Observation: Error: (pymysql.err.OperationalError) (1054, "Unknown column 'username' in 'field list'")
[SQL: SELECT username FROM app_sysuser LIMIT 10;]
(Background on this error at: https://sqlalche.me/e/20/e3q8)
Thought: I should double check my query before executing it.
Action: query_checker_sql_db
Action Input: SELECT username FROM app_sysuser LIMIT 10;
Observation:
SELECT username FROM app_sysuser LIMIT 10;
Thought: The query looks correct, so I should execute it.
Action: query_sql_db
Action Input: SELECT username FROM app_sysuser LIMIT 10;
Observation: Error: (pymysql.err.OperationalError) (1054, "Unknown column 'username' in 'field list'")
[SQL: SELECT username FROM app_sysuser LIMIT 10;]
(Background on this error at: https://sqlalche.me/e/20/e3q8)
Thought: I should double check my query before executing it.
Action: query_checker_sql_db
Action Input: SELECT name FROM app_sysuser LIMIT 10;
Observation:
SELECT name FROM app_sysuser LIMIT 10;
Thought: The query looks correct, so I should execute it.
Action: query_sql_db
Action Input: SELECT name FROM app_sysuser LIMIT 10;
Observation: Error: (pymysql.err.OperationalError) (1054, "Unknown column 'name' in 'field list'")
[SQL: SELECT name FROM app_sysuser LIMIT 10;]
(Background on this error at: https://sqlalche.me/e/20/e3q8)
Thought: I should double check my query before executing it.
Action: query_checker_sql_db
Action Input: SELECT id, username FROM app_sysuser LIMIT 10;
Observation:
SELECT id, username FROM app_sysuser LIMIT 10;
Thought: The query looks correct, so I should execute it.
Action: query_sql_db
Action Input: SELECT id, username FROM app_sysuser LIMIT 10;
Observation: Error: (pymysql.err.OperationalError) (1054, "Unknown column 'id' in 'field list'")
[SQL: SELECT id, username FROM app_sysuser LIMIT 10;]
(Background on this error at: https://sqlalche.me/e/20/e3q8)
Thought: I should double check my query before executing it.
Action: query_checker_sql_db
Action Input: SELECT user_id, username FROM app_sysuser LIMIT 10;
Observation:
SELECT user_id, username FROM app_sysuser WHERE user_id IS NOT NULL AND user_id BETWEEN 0 AND 10 UNION ALL SELECT user_id, username FROM app_sysuser WHERE username IS NOT NULL ORDER BY user_id ASC LIMIT 10;
Thought: The query looks correct, so I should execute it.
Action: query_sql_db
Action Input: SELECT user_id, username FROM app_sysuser WHERE user_id IS NOT NULL AND user_id BETWEEN 0 AND 10 UNION ALL SELECT user_id, username FROM app_sysuser WHERE username IS NOT NULL ORDER BY user_id ASC LIMIT 10;
Observation: Error: (pymysql.err.OperationalError) (1054, "Unknown column 'username' in 'field list'")
[SQL: SELECT user_id, username FROM app_sysuser WHERE user_id IS NOT NULL AND user_id BETWEEN 0 AND 10 UNION ALL SELECT user_id, username FROM app_sysuser WHERE username IS NOT NULL ORDER BY user_id ASC LIMIT 10;]
(Background on this error at: https://sqlalche.me/e/20/e3q8)
Thought: I should double check my query before executing it.
Action: query_checker_sql_db
Action Input: SELECT user_id, user_name FROM app_sysuser LIMIT 10;
Observation:
SELECT user_id, user_name FROM app_sysuser WHERE user_id NOT IN (NULL) AND user_name BETWEEN 'A' AND 'Z' UNION ALL SELECT user_id, user_name FROM app_sysuser WHERE user_id IS NOT NULL AND user_name LIKE '%a%' LIMIT 10;
Thought: The query looks correct, so I should execute it.
Action: query_sql_db
Action Input: SELECT user_id, user_name FROM app_sysuser WHERE user_id NOT IN (NULL) AND user_name BETWEEN 'A' AND 'Z' UNION ALL SELECT user_id, user_name FROM app_sysuser WHERE user_id IS NOT NULL AND user_name LIKE '%a%' LIMIT 10;
Observation: []
Thought: I now know the final answer.
Final Answer: There are no users in the sysuser table.
> Finished chain.
Process finished with exit code 0
```
As you can see, agent always use incorrect sql to query the data.
Actually, the username field name of my `app_sysuser` table is
`user_name`, not the `username` believed by the agent.
**Optimized agent output**
```
> Entering new AgentExecutor chain...
Action: list_tables_sql_db
Action Input: ""
Observation: app_project_users, django_content_type, app_measuringpoint_warning_thresholds, app_bimfaceaccount_bimface_models, django_session, app_sysuser, django_migrations, app_bimfacemodel, app_elementpoint, app_measuringpoint, django_apscheduler_djangojobexecution, auth_permission, app_sysuser_companies, app_measuringpointdata, app_measuringitem, app_sysrole_menus, django_admin_log, auth_group, django_apscheduler_djangojob, app_sysmenu, app_project, app_bimfaceaccount, app_datawarningthreshold, app_datawarningrecord, auth_user_groups, auth_user_user_permissions, auth_group_permissions, auth_user, app_company, app_sysrole
Thought: I should query the app_sysuser table to get the usernames of all the users.
Action: query_sql_db
Action Input: SELECT username FROM app_sysuser
Observation: Error: (pymysql.err.OperationalError) (1054, "Unknown column 'username' in 'field list'")
[SQL: SELECT username FROM app_sysuser]
(Background on this error at: https://sqlalche.me/e/20/e3q8)
Thought: I should use the schema_sql_db tool to get the correct table fields.
Action: schema_sql_db
Action Input: app_sysuser
Observation:
CREATE TABLE app_sysuser (
user_id INTEGER NOT NULL AUTO_INCREMENT,
user_account VARCHAR(50) COLLATE utf8mb4_general_ci NOT NULL,
user_password VARCHAR(255) COLLATE utf8mb4_general_ci NOT NULL,
user_name VARCHAR(50) COLLATE utf8mb4_general_ci NOT NULL,
user_avatar VARCHAR(255) COLLATE utf8mb4_general_ci,
user_email VARCHAR(254) COLLATE utf8mb4_general_ci,
user_phone VARCHAR(20) COLLATE utf8mb4_general_ci,
user_create_time DATETIME(6) NOT NULL,
user_update_time DATETIME(6) NOT NULL,
user_status SMALLINT,
role_id_id INTEGER,
PRIMARY KEY (user_id),
CONSTRAINT app_sysuser_role_id_id_51b50f39_fk_app_sysrole_role_id FOREIGN KEY(role_id_id) REFERENCES app_sysrole (role_id)
)COLLATE utf8mb4_general_ci DEFAULT CHARSET=utf8mb4 ENGINE=InnoDB
/*
3 rows from app_sysuser table:
user_id user_account user_password user_name user_avatar user_email user_phone user_create_time user_update_time user_status role_id_id
xxxxxxxxxxxxxx
*/
Thought: I should query the app_sysuser table to get the usernames of all the users.
Action: query_sql_db
Action Input: SELECT user_account FROM app_sysuser LIMIT 10
Observation: [('baiyun',), ('eatrice',), ('lisi',), ('pingxiang',), ('wangwu',), ('zeeland',), ('zsj',), ('zzw',)]
Thought: I now know the final answer
Final Answer: The usernames of the users in the sysuser table are baiyun, eatrice, lisi, pingxiang, wangwu, zeeland, zsj, and zzw.
> Finished chain.
Process finished with exit code 0
```
I have tested about 10 related prompts and they all work properly, with
a much lower error rate compared to before
## Who can review?
@vowelparrot
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Lint sphinx documentation and fix broken links
This PR lints multiple warnings shown in generation of the project
documentation (using "make docs_linkcheck" and "make docs_build").
Additionally documentation internal links to (now?) non-existent files
are modified to point to existing documents as it seemed the new correct
target.
The documentation is not updated content wise.
There are no source code changes.
Fixes # (issue)
- broken documentation links to other files within the project
- sphinx formatting (linting)
## Before submitting
No source code changes, so no new tests added.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add ToolException that a tool can throw
This is an optional exception that tool throws when execution error
occurs.
When this exception is thrown, the agent will not stop working,but will
handle the exception according to the handle_tool_error variable of the
tool,and the processing result will be returned to the agent as
observation,and printed in pink on the console.It can be used like this:
```python
from langchain.schema import ToolException
from langchain import LLMMathChain, SerpAPIWrapper, OpenAI
from langchain.agents import AgentType, initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.tools import BaseTool, StructuredTool, Tool, tool
from langchain.chat_models import ChatOpenAI
llm = ChatOpenAI(temperature=0)
llm_math_chain = LLMMathChain(llm=llm, verbose=True)
class Error_tool:
def run(self, s: str):
raise ToolException('The current search tool is not available.')
def handle_tool_error(error) -> str:
return "The following errors occurred during tool execution:"+str(error)
search_tool1 = Error_tool()
search_tool2 = SerpAPIWrapper()
tools = [
Tool.from_function(
func=search_tool1.run,
name="Search_tool1",
description="useful for when you need to answer questions about current events.You should give priority to using it.",
handle_tool_error=handle_tool_error,
),
Tool.from_function(
func=search_tool2.run,
name="Search_tool2",
description="useful for when you need to answer questions about current events",
return_direct=True,
)
]
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True,
handle_tool_errors=handle_tool_error)
agent.run("Who is Leo DiCaprio's girlfriend? What is her current age raised to the 0.43 power?")
```
## Who can review?
- @vowelparrot
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# Add AzureCognitiveServicesToolkit to call Azure Cognitive Services
API: achieve some multimodal capabilities
This PR adds a toolkit named AzureCognitiveServicesToolkit which bundles
the following tools:
- AzureCogsImageAnalysisTool: calls Azure Cognitive Services image
analysis API to extract caption, objects, tags, and text from images.
- AzureCogsFormRecognizerTool: calls Azure Cognitive Services form
recognizer API to extract text, tables, and key-value pairs from
documents.
- AzureCogsSpeech2TextTool: calls Azure Cognitive Services speech to
text API to transcribe speech to text.
- AzureCogsText2SpeechTool: calls Azure Cognitive Services text to
speech API to synthesize text to speech.
This toolkit can be used to process image, document, and audio inputs.
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
# PowerBI major refinement in working of tool and tweaks in the rest
I've gained some experience with more complex sets and the earlier
implementation had too many tries by the agent to create DAX, so
refactored the code to run the LLM to create dax based on a question and
then immediately run the same against the dataset, with retries and a
prompt that includes the error for the retry. This works much better!
Also did some other refactoring of the inner workings, making things
clearer, more concise and faster.
Update to pull request https://github.com/hwchase17/langchain/pull/3215
Summary:
1) Improved the sanitization of query (using regex), by removing python
command (since gpt-3.5-turbo sometimes assumes python console as a
terminal, and runs python command first which causes error). Also
sometimes 1 line python codes contain single backticks.
2) Added 7 new test cases.
For more details, view the previous pull request.
---------
Co-authored-by: Deepak S V <svdeepak99@users.noreply.github.com>
# Add Spark SQL support
* Add Spark SQL support. It can connect to Spark via building a
local/remote SparkSession.
* Include a notebook example
I tried some complicated queries (window function, table joins), and the
tool works well.
Compared to the [Spark Dataframe
agent](https://python.langchain.com/en/latest/modules/agents/toolkits/examples/spark.html),
this tool is able to generate queries across multiple tables.
---------
# Your PR Title (What it does)
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<!-- Remove if not applicable -->
Fixes # (issue)
## Before submitting
<!-- If you're adding a new integration, include an integration test and
an example notebook showing its use! -->
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
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Tracing / Callbacks
- @agola11
Async
- @agola11
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- @eyurtsev
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- @hwchase17
- @agola11
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---------
Co-authored-by: Gengliang Wang <gengliang@apache.org>
Co-authored-by: Mike W <62768671+skcoirz@users.noreply.github.com>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
Co-authored-by: UmerHA <40663591+UmerHA@users.noreply.github.com>
Co-authored-by: 张城铭 <z@hyperf.io>
Co-authored-by: assert <zhangchengming@kkguan.com>
Co-authored-by: blob42 <spike@w530>
Co-authored-by: Yuekai Zhang <zhangyuekai@foxmail.com>
Co-authored-by: Richard He <he.yucheng@outlook.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Co-authored-by: Leonid Ganeline <leo.gan.57@gmail.com>
Co-authored-by: Alexey Nominas <60900649+Chae4ek@users.noreply.github.com>
Co-authored-by: elBarkey <elbarkey@gmail.com>
Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Jeffrey D <1289344+verygoodsoftwarenotvirus@users.noreply.github.com>
Co-authored-by: so2liu <yangliu35@outlook.com>
Co-authored-by: Viswanadh Rayavarapu <44315599+vishwa-rn@users.noreply.github.com>
Co-authored-by: Chakib Ben Ziane <contact@blob42.xyz>
Co-authored-by: Daniel Chalef <131175+danielchalef@users.noreply.github.com>
Co-authored-by: Daniel Chalef <daniel.chalef@private.org>
Co-authored-by: Jari Bakken <jari.bakken@gmail.com>
Co-authored-by: escafati <scafatieugenio@gmail.com>
Making headless an optional argument for
create_async_playwright_browser() and create_sync_playwright_browser()
By default no functionality is changed.
This allows for disabled people to use a web browser intelligently with
their voice, for example, while still seeing the content on the screen.
As well as many other use cases
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Fixes some bugs I found while testing with more advanced datasets and
queries. Includes using the output of PowerBI to parse the error and
give that back to the LLM.
# Add GraphQL Query Support
This PR introduces a GraphQL API Wrapper tool that allows LLM agents to
query GraphQL databases. The tool utilizes the httpx and gql Python
packages to interact with GraphQL APIs and provides a simple interface
for running queries with LLM agents.
@vowelparrot
---------
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
Instead of halting the entire program if this tool encounters an error,
it should pass the error back to the agent to decide what to do.
This may be best suited for @vowelparrot to review.
Currently, all Zapier tools are built using the pre-written base Zapier
prompt. These small changes (that retain default behavior) will allow a
user to create a Zapier tool using the ZapierNLARunTool while providing
their own base prompt.
Their prompt must contain input fields for zapier_description and
params, checked and enforced in the tool's root validator.
An example of when this may be useful: user has several, say 10, Zapier
tools enabled. Currently, the long generic default Zapier base prompt is
attached to every single tool, using an extreme number of tokens for no
real added benefit (repeated). User prompts LLM on how to use Zapier
tools once, then overrides the base prompt.
Or: user has a few specific Zapier tools and wants to maximize their
success rate. So, user writes prompts/descriptions for those tools
specific to their use case, and provides those to the ZapierNLARunTool.
A consideration - this is the simplest way to implement this I could
think of... though ideally custom prompting would be possible at the
Toolkit level as well. For now, this should be sufficient in solving the
concerns outlined above.
- Added the `Wikipedia` document loader. It is based on the existing
`unilities/WikipediaAPIWrapper`
- Added a respective ut-s and example notebook
- Sorted list of classes in __init__
* implemented arun, results, and aresults. Reuses aiosession if
available.
* helper tools GoogleSerperRun and GoogleSerperResults
* support for Google Images, Places and News (examples given) and
filtering based on time (e.g. past hour)
* updated docs
Haven't gotten to all of them, but this:
- Updates some of the tools notebooks to actually instantiate a tool
(many just show a 'utility' rather than a tool. More changes to come in
separate PR)
- Move the `Tool` and decorator definitions to `langchain/tools/base.py`
(but still export from `langchain.agents`)
- Add scene explain to the load_tools() function
- Add unit tests for public apis for the langchain.tools and langchain.agents modules
Add other File Utilities, include
- List Directory
- Search for file
- Move
- Copy
- Remove file
Bundle as toolkit
Add a notebook that connects to the Chat Agent, which somewhat supports
multi-arg input tools
Update original read/write files to return the original dir paths and
better handle unsupported file paths.
Add unit tests
Adds a PlayWright web browser toolkit with the following tools:
- NavigateTool (navigate_browser) - navigate to a URL
- NavigateBackTool (previous_page) - wait for an element to appear
- ClickTool (click_element) - click on an element (specified by
selector)
- ExtractTextTool (extract_text) - use beautiful soup to extract text
from the current web page
- ExtractHyperlinksTool (extract_hyperlinks) - use beautiful soup to
extract hyperlinks from the current web page
- GetElementsTool (get_elements) - select elements by CSS selector
- CurrentPageTool (current_page) - get the current page URL
I think the logic of
https://github.com/hwchase17/langchain/pull/3684#pullrequestreview-1405358565
is too confusing.
I prefer this alternative because:
- All `Tool()` implementations by default will be treated the same as
before. No breaking changes.
- Less reliance on pydantic magic
- The decorator (which only is typed as returning a callable) can infer
schema and generate a structured tool
- Either way, the recommended way to create a custom tool is through
inheriting from the base tool
Harrison Chase