Refactors the docs build in order to:
- run the same `make build` command in both vercel and local build
- incrementally build artifacts in 2 distinct steps, instead of building
all docs in-place (in vercel) or in a _dist dir (locally)
Highlights:
- introduces `make build` in order to build the docs
- collects and generates all files for the build in
`docs/build/intermediate`
- renders those jupyter notebook + markdown files into
`docs/build/outputs`
And now the outputs to host are in `docs/build/outputs`, which will need
a vercel settings change.
Todo:
- [ ] figure out how to point the right directory (right now deleting
and moving docs dir in vercel_build.sh isn't great)
**Description:**
This pull request introduces a new feature for LangChain: the
integration with the Rememberizer API through a custom retriever.
This enables LangChain applications to allow users to load and sync
their data from Dropbox, Google Drive, Slack, their hard drive into a
vector database that LangChain can query. Queries involve sending text
chunks generated within LangChain and retrieving a collection of
semantically relevant user data for inclusion in LLM prompts.
User knowledge dramatically improved AI applications.
The Rememberizer integration will also allow users to access general
purpose vectorized data such as Reddit channel discussions and US
patents.
**Issue:**
N/A
**Dependencies:**
N/A
**Twitter handle:**
https://twitter.com/Rememberizer
## Summary
`ruff /path/to/file.py` works but is deprecated, and we now recommend
`ruff check /path/to/file.py` (to match `ruff format /path/to/file.py`).
Vertex DIY RAG APIs helps to build complex RAG systems and provide more
granular control, and are suited for custom use cases.
The Ranking API takes in a list of documents and reranks those documents
based on how relevant the documents are to a given query. Compared to
embeddings that look purely at the semantic similarity of a document and
a query, the ranking API can give you a more precise score for how well
a document answers a given query.
[Reference](https://cloud.google.com/generative-ai-app-builder/docs/ranking)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Sync the config in `devcontainer.json` and `docker-compose.yml`**
Issue: when opening the current `master` branch in a dev container in VS
Code, I get the following message as VS Code cannot find the mounted
source folder:
Opening in a GitHub Codespace works (it seems to ignore the mounts in
the `docker-compose.yml`.
This PR updates the mount in `docker-compose.yml` and the config in
`devcontainer.json` so that the two align.
I have tested these changes in GitHub Codespaces and a VS Code dev
container and both loaded successfully.
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
**Description:** Add tests to check API keys and Active Directory tokens
are masked
**Issue:** Resolves#12165 for OpenAI and Azure OpenAI models
**Dependencies:** None
Also resolves#12473 which may be closed.
Additional contributors @alex4321 (#12473) and @onesolpark (#12542)
- [ ] **PR message**:
- **Description:** Refactored the lazy_load method to use asynchronous
execution for improved performance. The method now initiates scraping of
all URLs simultaneously using asyncio.gather, enhancing data fetching
efficiency. Each Document object is yielded immediately once its content
becomes available, streamlining the entire process.
- **Issue:** N/A
- **Dependencies:** Requires the asyncio library for handling
asynchronous tasks, which should already be part of standard Python
libraries in Python 3.7 and above.
- **Email:** [r73327118@gmail.com](mailto:r73327118@gmail.com)
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
Update python.py（experimental：Added code for PythonREPL）
Added code for PythonREPL, defining a static method 'sanitize_input'
that takes the string 'query' as input and returns a sanitizing string.
The purpose of this method is to remove unwanted characters from the
input string, Specifically:
1. Delete the whitespace at the beginning and end of the string (' \s').
2. Remove the quotation marks (`` ` ``) at the beginning and end of the
string.
3. Remove the keyword "python" at the beginning of the string (case
insensitive) because the user may have typed it.
This method uses regular expressions (regex) to implement sanitizing.
It all started with this code：
from langchain.agents import Tool
from langchain_experimental.utilities import PythonREPL
python_repl = PythonREPL()
repl_tool = Tool(
name="python_repl",
description="Remove redundant formatting marks at the beginning and end
of source code from input.Use a Python shell to execute python commands.
If you want to see the output of a value, you should print it out with
`print(...)`.",
func=python_repl.run,
)
When I call the agent to write a piece of code for me and execute it
with the defined code, I must get an error: SyntaxError('invalid
syntax', ('<string>', 1, 1,'In', 1, 2))
After checking, I found that pythonREPL has less formatting of input
code than the soon-to-be deprecated pythonREPL tool, so I added this
step to it, so that no matter what code I ask the agent to write for me,
it can be executed smoothly and get the output result.
I have tried modifying the prompt words to solve this problem before,
but it did not work, and by adding a simple format check, the problem is
well resolved.
<img width="1271" alt="image"
src="https://github.com/langchain-ai/langchain/assets/164149097/c49a685f-d246-4b11-b655-fd952fc2f04c">
---------
Co-authored-by: Bagatur <22008038+baskaryan@users.noreply.github.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
**Description**
This pull request updates the Bagel Network package name from
"betabageldb" to "bagelML" to align with the latest changes made by the
Bagel Network team.
The following modifications have been made:
- Updated all references to the old package name ("betabageldb") with
the new package name ("bagelML") throughout the codebase.
- Modified the documentation, and any relevant scripts to reflect the
package name change.
- Tested the changes to ensure that the functionality remains intact and
no breaking changes were introduced.
By merging this pull request, our project will stay up to date with the
latest Bagel Network package naming convention, ensuring compatibility
and smooth integration with their updated library.
Please review the changes and provide any feedback or suggestions. Thank
you!
**Description:** Update UpstageLayoutAnalysisParser and Loader and add
upstage loader example in pdf section
**Dependencies:** langchain_community
**Twitter handle:** [@upstageai](https://twitter.com/upstageai)
- [x] **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.
- [x] **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, hwchase17.
**Issue:**
Currently `AzureSearch` vector store does not implement `delete` method.
This PR implements it. This also makes it compatible with LangChain
indexer.
**Dependencies:**
None
**Twitter handle:**
@martintriska1
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
