This PR makes minor improvements to our python notebook, and adds
support for `Rockset` workspaces in our vectorstore client.
@rlancemartin, @eyurtsev
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
This PR handles modifying the Chroma DB integration's documentation.
It modifies the **Docker container** example to fix the instructions
mentioned in the documentation.
In the current documentation, the below `client.reset()` line causes a
runtime error:
```py
...
client = chromadb.HttpClient(settings=Settings(allow_reset=True))
client.reset() # resets the database
collection = client.create_collection("my_collection")
...
```
`Exception: {"error":"ValueError('Resetting is not allowed by this
configuration')"}`
This is due to the Chroma DB server needing to have the `allow_reset`
flag set to `true` there as well.
This is fixed by adding the `ALLOW_RESET=TRUE` to the `docker-compose`
file environment variable to the docker container before spinning it
## Issue
This fixes the runtime error that occurs when running the docker
container example code
## Tag Maintainer
@rlancemartin, @eyurtsev
## Description
The imports for `NeptuneOpenCypherQAChain` are failing. This PR adds the
chain class to the `__init__.py` file to fix this issue.
## Maintainers
@dev2049
@krlawrence
Docs for from_documents() were outdated as seen in
https://github.com/langchain-ai/langchain/issues/8457 .
fixes#8457
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
### Description
In the LangChain Documentation and Comments, I've Noticed that `pip
install faiss` was mentioned, instead of `pip install faiss-gpu`, since
installing `pip install faiss` results in an error. I've gone ahead and
updated the Documentation, and `faiss.ipynb`. This Change will ensure
ease of use for the end user, trying to install `faiss-gpu`.
### Issue:
Documentation / Comments Related.
### Dependencies:
No Dependencies we're changed only updated the files with the wrong
reference.
### Tag maintainer:
@rlancemartin, @eyurtsev (Thank You for your contributions 😄 )
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
---------
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Co-authored-by: Eugene Yurtsev <eyurtsev@gmail.com>
- Install langchain
- Set Pinecone API key and environment as env vars
- Create Pinecone index if it doesn't already exist
---
- Description: Fix a couple minor issues I came across when running this
notebook,
- Issue: the issue # it fixes (if applicable),
- Dependencies: none,
- Tag maintainer: @rlancemartin @eyurtsev,
- Twitter handle: @zackproser (certainly not necessary!)
**Description:**
Add support for Meilisearch vector store.
Resolve#7603
- No external dependencies added
- A notebook has been added
@rlancemartin
https://twitter.com/meilisearch
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Minimax is a great AI startup from China, recently they
released their latest model and chat API, and the API is widely-spread
in China. As a result, I'd like to add the Minimax llm model to
Langchain.
- Tag maintainer: @hwchase17, @baskaryan
---------
Co-authored-by: the <tao.he@hulu.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
- [Xorbits
Inference(Xinference)](https://github.com/xorbitsai/inference) is a
powerful and versatile library designed to serve language, speech
recognition, and multimodal models. Xinference supports a variety of
GGML-compatible models including chatglm, whisper, and vicuna, and
utilizes heterogeneous hardware and a distributed architecture for
seamless cross-device and cross-server model deployment.
- This PR integrates Xinference models and Xinference embeddings into
LangChain.
- Dependencies: To install the depenedencies for this integration, run
`pip install "xinference[all]"`
- Example Usage:
To start a local instance of Xinference, run `xinference`.
To deploy Xinference in a distributed cluster, first start an Xinference
supervisor using `xinference-supervisor`:
`xinference-supervisor -H "${supervisor_host}"`
Then, start the Xinference workers using `xinference-worker` on each
server you want to run them on.
`xinference-worker -e "http://${supervisor_host}:9997"`
To use Xinference with LangChain, you also need to launch a model. You
can use command line interface (CLI) to do so. Fo example: `xinference
launch -n vicuna-v1.3 -f ggmlv3 -q q4_0`. This launches a model named
vicuna-v1.3 with `model_format="ggmlv3"` and `quantization="q4_0"`. A
model UID is returned for you to use.
Now you can use Xinference with LangChain:
```python
from langchain.llms import Xinference
llm = Xinference(
server_url="http://0.0.0.0:9997", # suppose the supervisor_host is "0.0.0.0"
model_uid = {model_uid} # model UID returned from launching a model
)
llm(
prompt="Q: where can we visit in the capital of France? A:",
generate_config={"max_tokens": 1024},
)
```
You can also use RESTful client to launch a model:
```python
from xinference.client import RESTfulClient
client = RESTfulClient("http://0.0.0.0:9997")
model_uid = client.launch_model(model_name="vicuna-v1.3", model_size_in_billions=7, quantization="q4_0")
```
The following code block demonstrates how to use Xinference embeddings
with LangChain:
```python
from langchain.embeddings import XinferenceEmbeddings
xinference = XinferenceEmbeddings(
server_url="http://0.0.0.0:9997",
model_uid = model_uid
)
```
```python
query_result = xinference.embed_query("This is a test query")
```
```python
doc_result = xinference.embed_documents(["text A", "text B"])
```
Xinference is still under rapid development. Feel free to [join our
Slack
community](https://xorbitsio.slack.com/join/shared_invite/zt-1z3zsm9ep-87yI9YZ_B79HLB2ccTq4WA)
to get the latest updates!
- Request for review: @hwchase17, @baskaryan
- Twitter handle: https://twitter.com/Xorbitsio
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
Added a new tool to the Github toolkit called **Create Pull Request.**
Now we can make our own langchain contributor in langchain 😁
In order to have somewhere to pull from, I also added a new env var,
"GITHUB_BASE_BRANCH." This will allow the existing env var,
"GITHUB_BRANCH," to be a working branch for the bot (so that it doesn't
have to always commit on the main/master). For example, if you want the
bot to work in a branch called `bot_dev` and your repo base is `main`,
you would set up the vars like:
```
GITHUB_BASE_BRANCH = "main"
GITHUB_BRANCH = "bot_dev"
```
Maintainer responsibilities:
- Agents / Tools / Toolkits: @hinthornw
In this PR:
- Removed restricted model loading logic for Petals-Bloom
- Removed petals imports (DistributedBloomForCausalLM,
BloomTokenizerFast)
- Instead imported more generalized versions of loader
(AutoDistributedModelForCausalLM, AutoTokenizer)
- Updated the Petals example notebook to allow for a successful
installation of Petals in Apple Silicon Macs
- Tag maintainer: @hwchase17, @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
- Description: Adds AwaEmbeddings class for embeddings, which provides
users with a convenient way to do fine-tuning, as well as the potential
need for multimodality
- Tag maintainer: @baskaryan
Create `Awa.ipynb`: an example notebook for AwaEmbeddings class
Modify `embeddings/__init__.py`: Import the class
Create `embeddings/awa.py`: The embedding class
Create `embeddings/test_awa.py`: The test file.
---------
Co-authored-by: taozhiwang <taozhiwa@gmail.com>
Spelling error fix
<!-- Thank you for contributing to LangChain!
Replace this comment with:
- Description: a description of the change,
- Issue: the issue # it fixes (if applicable),
- Dependencies: any dependencies required for this change,
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
- Twitter handle: we announce bigger features on Twitter. If your PR
gets announced and you'd like a mention, we'll gladly shout you out!
Please make sure you're PR is passing linting and testing before
submitting. Run `make format`, `make lint` and `make test` to check this
locally.
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.
Maintainer responsibilities:
- General / Misc / if you don't know who to tag: @baskaryan
- DataLoaders / VectorStores / Retrievers: @rlancemartin, @eyurtsev
- Models / Prompts: @hwchase17, @baskaryan
- Memory: @hwchase17
- Agents / Tools / Toolkits: @hinthornw
- Tracing / Callbacks: @agola11
- Async: @agola11
If no one reviews your PR within a few days, feel free to @-mention the
same people again.
See contribution guidelines for more information on how to write/run
tests, lint, etc:
https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md
-->
## Description
This commit introduces the `DropboxLoader` class, a new document loader
that allows loading files from Dropbox into the application. The loader
relies on a Dropbox app, which requires creating an app on Dropbox,
obtaining the necessary scope permissions, and generating an access
token. Additionally, the dropbox Python package is required.
The `DropboxLoader` class is designed to be used as a document loader
for processing various file types, including text files, PDFs, and
Dropbox Paper files.
## Dependencies
`pip install dropbox` and `pip install unstructured` for PDF reading.
## Tag maintainer
@rlancemartin, @eyurtsev (from Data Loaders). I'd appreciate some
feedback here 🙏 .
## Social Networks
https://github.com/rubenbarraganhttps://www.linkedin.com/in/rgbarragan/https://twitter.com/RubenBarraganP
---------
Co-authored-by: Ruben Barragan <rbarragan@Rubens-MacBook-Air.local>
# [WIP] Tree of Thought introducing a new ToTChain.
This PR adds a new chain called ToTChain that implements the ["Large
Language Model Guided
Tree-of-Though"](https://arxiv.org/pdf/2305.08291.pdf) paper.
There's a notebook example `docs/modules/chains/examples/tot.ipynb` that
shows how to use it.
Implements #4975
## Who can review?
Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:
- @hwchase17
- @vowelparrot
---------
Co-authored-by: Vadim Gubergrits <vgubergrits@outbox.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
Description: Adding support for custom index and scoring profile support
in Azure Cognitive Search
@hwchase17
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
This change compacts the left-side Navbar (ToC) of the [API
Reference](https://api.python.langchain.com/en/latest/api_reference.html).
Now almost each namespace item is split into two lines. For example
`langchain.chat_models: Chat Models`
We remove the `Chat Models` and leave one the `langchain.chat_models`.
This effectively compacts the navbar and increases the main page's
usability. On my screen, it reduces # of lines in Toc from 28 t to 18,
which is huge.
Removing the namespace "title" (like `Chat Models`) does not remove any
information because the title is composed directly from the namespace.
API Reference users are developers. Usability for them is very
important. We see less text => we find faster.
This PR introduces async API support for Cohere, both LLM and
embeddings. It requires updating `cohere` package to `^4`.
Tagging @hwchase17, @baskaryan, @agola11
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
