Mentioned the idea here initially:
https://github.com/hwchase17/langchain/pull/2106#issuecomment-1487509106
Since there have been dialect-specific issues, we should use
dialect-specific prompts. This way, each prompt can be separately
modified to best suit each dialect as needed. This adds a prompt for
each dialect supported in sqlalchemy (mssql, mysql, mariadb, postgres,
oracle, sqlite). For this initial implementation, the only differencse
between the prompts is the instruction for the clause to use to limit
the number of rows queried for, and the instruction for wrapping column
names using each dialect's identifier quote character.
Optimization :Limit search results when k < 10
Fix issue when k > 10: Elasticsearch will return only 10 docs
[default-search-result](https://www.elastic.co/guide/en/elasticsearch/reference/current/paginate-search-results.html)
By default, searches return the top 10 matching hits
Add size parameter to the search request to limit the number of returned
results from Elasticsearch. Remove slicing of the hits list, since the
response will already contain the desired number of results.
Mendable Seach Integration is Finally here!
Hey yall,
After various requests for Mendable in Python docs, we decided to get
our hands dirty and try to implement it.
Here is a version where we implement our **floating button** that sits
on the bottom right of the screen that once triggered (via press or CMD
K) will work the same as the js langchain docs.
Super excited about this and hopefully the community will be too.
@hwchase17 will send you the admin details via dm etc. The anon_key is
fine to be public.
Let me know if you need any further customization. I added the langchain
logo to it.
Fixes linting issue from #2835
Adds a loader for Slack Exports which can be a very valuable source of
knowledge to use for internal QA bots and other use cases.
```py
# Export data from your Slack Workspace first.
from langchain.document_loaders import SLackDirectoryLoader
SLACK_WORKSPACE_URL = "https://awesome.slack.com"
loader = ("Slack_Exports", SLACK_WORKSPACE_URL)
docs = loader.load()
```
My recent pull request (#2729) neglected to update the
`reduce_openapi_spec` in spec.py to also accommodate PATCH and DELETE
added to planner.py and prompt_planner.py.
Have seen questions about whether or not the `SQLDatabaseChain` supports
more than just sqlite, which was unclear in the docs, so tried to
clarify that and how to connect to other dialects.
The doc loaders index was picking up a bunch of subheadings because I
mistakenly made the MD titles H1s. Fixed that.
also the easy minor warnings from docs_build
I was testing out the WhatsApp Document loader, and noticed that
sometimes the date is of the following format (notice the additional
underscore):
```
3/24/23, 1:54_PM - +91 99999 99999 joined using this group's invite link
3/24/23, 6:29_PM - +91 99999 99999: When are we starting then?
```
Wierdly, the underscore is visible in Vim, but not on editors like
VSCode. I presume it is some unusual character/line terminator.
Nevertheless, I think handling this edge case will make the document
loader more robust.
Adds a loader for Slack Exports which can be a very valuable source of
knowledge to use for internal QA bots and other use cases.
```py
# Export data from your Slack Workspace first.
from langchain.document_loaders import SLackDirectoryLoader
SLACK_WORKSPACE_URL = "https://awesome.slack.com"
loader = ("Slack_Exports", SLACK_WORKSPACE_URL)
docs = loader.load()
```
---------
Co-authored-by: Mikhail Dubov <mikhail@chattermill.io>
When the code ran by the PythonAstREPLTool contains multiple statements
it will fallback to exec() instead of using eval(). With this change, it
will also return the output of the code in the same way the
PythonREPLTool will.
In #2399 we added the ability to set `max_execution_time` when creating
an AgentExecutor. This PR adds the `max_execution_time` argument to the
built-in pandas, sql, and openapi agents.
Co-authored-by: Zachary Jones <zjones@zetaglobal.com>
### Summary
Adds support for processing non HTML document types in the URL loader.
For example, the URL loader can now process a PDF or markdown files
hosted at a URL.
### Testing
```python
from langchain.document_loaders import UnstructuredURLLoader
urls = ["https://www.understandingwar.org/sites/default/files/Russian%20Offensive%20Campaign%20Assessment%2C%20April%2011%2C%202023.pdf"]
loader = UnstructuredURLLoader(urls=urls, strategy="fast")
docs = loader.load()
print(docs[0].page_content[:1000])
```
Updated the "load_memory_variables" function of the
ConversationBufferWindowMemory to support a window size of 0 (k=0).
Previous behavior would return the full memory instead of an empty
array.
Eval chain is currently very sensitive to differences in phrasing,
punctuation, and tangential information. This prompt has worked better
for me on my examples.
More general q: Do we have any framework for evaluating default prompt
changes? Could maybe start doing some regression testing?
