- Improved docs
- Improved performance in multiple ways through batching, threading,
etc.
- fixed error message
- Added support for metadata filtering during similarity search.
@baskaryan PTAL
- Description: support [ERNIE
Embedding-V1](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/alj562vvu),
which is part of ERNIE ecology
- Issue: None
- Dependencies: None
- Tag maintainer: @baskaryan
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
In second section it looks like a copy/paste from the first section and
doesn't include the specific embedding model mentioned in the example so
I added it for clarity.
---------
Co-authored-by: Bagatur <baskaryan@gmail.com>
## Description
This PR adds the `aembed_query` and `aembed_documents` async methods for
improving the embeddings generation for large documents. The
implementation uses asyncio tasks and gather to achieve concurrency as
there is no bedrock async API in boto3.
### Maintainers
@agola11
@aarora79
### Open questions
To avoid throttling from the Bedrock API, should there be an option to
limit the concurrency of the calls?
- [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>
- 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>
- **Description:** Simple change of the Class that ContentHandler
inherits from. To create an object of type SagemakerEndpointEmbeddings,
the property content_handler must be of type EmbeddingsContentHandler
not ContentHandlerBase anymore,
- **Twitter handle:** @Juanjo_Torres11
Co-authored-by: Bagatur <baskaryan@gmail.com>
Description:
This PR adds embeddings for LocalAI (
https://github.com/go-skynet/LocalAI ), a self-hosted OpenAI drop-in
replacement. As LocalAI can re-use OpenAI clients it is mostly following
the lines of the OpenAI embeddings, however when embedding documents, it
just uses string instead of sending tokens as sending tokens is
best-effort depending on the model being used in LocalAI. Sending tokens
is also tricky as token id's can mismatch with the model - so it's safer
to just send strings in this case.
Partly related to: https://github.com/hwchase17/langchain/issues/5256
Dependencies: No new dependencies
Twitter: @mudler_it
---------
Signed-off-by: mudler <mudler@localai.io>
Co-authored-by: Bagatur <baskaryan@gmail.com>
