Integrating Portkey, which adds production features like caching,
tracing, tagging, retries, etc. to langchain apps.
- Dependencies: None
- Twitter handle: https://twitter.com/portkeyai
- test_portkey.py added for tests
- example notebook added in new utilities folder in modules
Also fixed a bug with OpenAIEmbeddings where headers weren't passing.
cc @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description:** Golden Query is a wrapper on top of the [Golden Query
API](https://docs.golden.com/reference/query-api) which enables
programmatic access to query results on entities across Golden's
Knowledge Base. For more information about Golden API, please see the
[Golden API Getting
Started](https://docs.golden.com/reference/getting-started) page.
**Issue:** None
**Dependencies:** requests(already present in project)
**Tag maintainer:** @hinthornw
Signed-off-by: Constantin Musca <constantin.musca@gmail.com>
This fixes#4833 and the critical vulnerability
https://nvd.nist.gov/vuln/detail/CVE-2023-34540
Previously, the JIRA API Wrapper had a mode that simply pipelined user
input into an `exec()` function.
[The intended use of the 'other' mode is to cover any of Atlassian's API
that don't have an existing
interface](cc33bde74f/langchain/tools/jira/prompt.py (L24))
Fortunately all of the [Atlassian JIRA API methods are subfunctions of
their `Jira`
class](https://atlassian-python-api.readthedocs.io/jira.html), so this
implementation calls these subfunctions directly.
As well as passing a string representation of the function to call, the
implementation flexibly allows for optionally passing args and/or
keyword-args. These are given as part of the dictionary input. Example:
```
{
"function": "update_issue_field", #function to execute
"args": [ #list of ordered args similar to other examples in this JiraAPIWrapper
"key",
{"summary": "New summary"}
],
"kwargs": {} #dict of key value keyword-args pairs
}
```
the above is equivalent to `self.jira.update_issue_field("key",
{"summary": "New summary"})`
Alternate query schema designs are welcome to make querying easier
without passing and evaluating arbitrary python code. I considered
parsing (without evaluating) input python code and extracting the
function, args, and kwargs from there and then pipelining them into the
callable function via `*f(args, **kwargs)` - but this seemed more
direct.
@vowelparrot @dev2049
---------
Co-authored-by: Jamal Rahman <jamal.rahman@builder.ai>
Adding Confluence to Jira tool. Can create a page in Confluence with
this PR. If accepted, will extend functionality to Bitbucket and
additional Confluence features.
---------
Co-authored-by: Ethan Bowen <ethan.bowen@slalom.com>
This PR refactors the ArxivAPIWrapper class making
`doc_content_chars_max` parameter optional. Additionally, tests have
been added to ensure the functionality of the doc_content_chars_max
parameter.
Fixes#6027 (issue)
# Powerbi API wrapper bug fix + integration tests
- Bug fix by removing `TYPE_CHECKING` in in utilities/powerbi.py
- Added integration test for power bi api in
utilities/test_powerbi_api.py
- Added integration test for power bi agent in
agent/test_powerbi_agent.py
- Edited .env.examples to help set up power bi related environment
variables
- Updated demo notebook with working code in
docs../examples/powerbi.ipynb - AzureOpenAI -> ChatOpenAI
Notes:
Chat models (gpt3.5, gpt4) are much more capable than davinci at writing
DAX queries, so that is important to getting the agent to work properly.
Interestingly, gpt3.5-turbo needed the examples=DEFAULT_FEWSHOT_EXAMPLES
to write consistent DAX queries, so gpt4 seems necessary as the smart
llm.
Fixes#4325
## Before submitting
Azure-core and Azure-identity are necessary dependencies
check integration tests with the following:
`pytest tests/integration_tests/utilities/test_powerbi_api.py`
`pytest tests/integration_tests/agent/test_powerbi_agent.py`
You will need a power bi account with a dataset id + table name in order
to test. See .env.examples for details.
## Who can review?
@hwchase17
@vowelparrot
---------
Co-authored-by: aditya-pethe <adityapethe1@gmail.com>
# 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>
- 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
It makes sense to use `arxiv` as another source of the documents for
downloading.
- Added the `arxiv` document_loader, based on the
`utilities/arxiv.py:ArxivAPIWrapper`
- added tests
- added an example notebook
- sorted `__all__` in `__init__.py` (otherwise it is hard to find a
class in the very long list)
Tools for Bing, DDG and Google weren't consistent even though the
underlying implementations were.
All three services now have the same tools and implementations to easily
switch and experiment when building chains.
