Commit Graph

29 Commits

Author SHA1 Message Date
Ikko Eltociear Ashimine
33884b2184
Fix typo in gradient.ipynb (#11206)
Enviroment -> Environment

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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/extras`
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2023-09-29 11:45:40 -04:00
Anar
ff732e10f8
LLMRails Embedding (#10959)
LLMRails  Embedding Integration
This PR provides integration with LLMRails. Implemented here are:

langchain/embeddings/llm_rails.py
docs/extras/integrations/text_embedding/llm_rails.ipynb


Hi @hwchase17 after adding our vectorstore integration to langchain with
confirmation of you and @baskaryan, now we want to add our embedding
integration

---------

Co-authored-by: Anar Aliyev <aaliyev@mgmt.cloudnet.services>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-23 16:11:02 -07:00
Michael Feil
94e31647bd
Support for Gradient.ai embedding (#10968)
Adds support for gradient.ai's embedding model.

This will remain a Draft, as the code will likely be refactored with the
`pip install gradientai` python sdk.
2023-09-23 16:10:23 -07:00
DanielZzz
ebe08412ad
fix: chat_models Qianfan not compatiable with SystemMessage (#10642)
- **Description:** QianfanEndpoint bugs for SystemMessages. When the
`SystemMessage` is input as the messages to
`chat_models.QianfanEndpoint`. A `TypeError` will be raised.
  - **Issue:** #10643
  - **Dependencies:** 
  - **Tag maintainer:** @baskaryan
  - **Twitter handle:** no
2023-09-19 22:35:51 -07:00
Harrison Chase
5442d2b1fa
Harrison/stop importing from init (#10690) 2023-09-16 17:22:48 -07:00
Bagatur
2ae568dcf5
Separate platforms integrations docs (#10609) 2023-09-15 12:18:57 -07:00
Ackermann Yuriy
5e50b89164
Added embeddings support for ollama (#10124)
- Description: Added support for Ollama embeddings
  - Issue: the issue # it fixes (if applicable),
  - Dependencies: N/A
- Tag maintainer: for a quicker response, tag the relevant maintainer
(see below),
  - Twitter handle: @herrjemand

cc  https://github.com/jmorganca/ollama/issues/436
2023-09-14 17:42:39 -07:00
ItzPAX
cbb4860fcd
fix typo in aleph_alpha.ipynb (#10478)
fixes the aleph_alpha.ipynb typo from contnt to content
2023-09-13 17:09:11 -07:00
stonekim
adabdfdfc7
Add Baidu Qianfan endpoint for LLM (#10496)
- Description:
* Baidu AI Cloud's [Qianfan
Platform](https://cloud.baidu.com/doc/WENXINWORKSHOP/index.html) is an
all-in-one platform for large model development and service deployment,
catering to enterprise developers in China. Qianfan Platform offers a
wide range of resources, including the Wenxin Yiyan model (ERNIE-Bot)
and various third-party open-source models.
- Issue: none
- Dependencies: 
    * qianfan
- Tag maintainer: @baskaryan
- Twitter handle:

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-13 16:23:49 -07:00
Leonid Ganeline
fdba711d28
docs integrations/embeddings consistency (#10302)
Updated `integrations/embeddings`: fixed titles; added links,
descriptions
Updated `integrations/providers`.
2023-09-07 19:53:33 -07:00
JaéGeR
b8669b249e
Added Hugging face inference api (#10280)
Embed documents without locally downloading the HF model


---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-09-06 14:55:48 -07:00
KyrianC
491089754d
EdenAI LLM update. Add models name option (#8963)
This PR follows the **Eden AI (LLM + embeddings) integration**. #8633 

We added an optional parameter to choose different AI models for
providers (like 'text-bison' for provider 'google', 'text-davinci-003'
for provider 'openai', etc.).

Usage:

```python
llm = EdenAI(
    feature="text",
    provider="google",
    params={
        "model": "text-bison",  # new
        "temperature": 0.2,
        "max_tokens": 250,
    },
)

```

You can also change the provider + model after initialization
```python
llm = EdenAI(
    feature="text",
    provider="google",
    params={
        "temperature": 0.2,
        "max_tokens": 250,
    },
)

prompt = """
hi 
"""

llm(prompt, providers='openai', model='text-davinci-003')  # change provider & model
```

The jupyter notebook as been updated with an example well.


Ping: @hwchase17, @baskaryan

---------

Co-authored-by: RedhaWassim <rwasssim@gmail.com>
Co-authored-by: sam <melaine.samy@gmail.com>
2023-09-01 12:11:33 -07:00
Corvus Lee
0fb95ebe66
Docs: enrich SageMaker endpoint embeddings with docstrings and examples (#9924)
Description: added comments to address the relationship between
input/output transformations and the customised inference.py script.
2023-08-29 11:38:52 -07:00
Lakshay Kansal
a8c916955f
Updates to Nomic Atlas and GPT4All documentation (#9414)
Description: Updates for Nomic AI Atlas and GPT4All integrations
documentation.

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-23 17:49:44 -07:00
Julien Salinas
4d0b7bb8e1 Remove Dolphin and GPT-J from the embeddings docs.
These models are not proposed anymore.
2023-08-22 09:28:22 +02:00
Matthew Zeiler
949b2cf177
Improvements to the Clarifai integration (#9290)
- Improved docs
- Improved performance in multiple ways through batching, threading,
etc.
 - fixed error message 
 - Added support for metadata filtering during similarity search.

@baskaryan PTAL
2023-08-21 12:53:36 -07:00
axiangcoding
05aa02005b
feat(llms): support ERNIE Embedding-V1 (#9370)
- 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>
2023-08-21 07:52:25 -07:00
Charles Lanahan
a2588d6c57
Update openai embeddings notebook with correct embedding model in section 2 (#5831)
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>
2023-08-10 19:02:10 -07:00
Piyush Jain
8eea46ed0e
Bedrock embeddings async methods (#9024)
## 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?
2023-08-10 14:21:03 -07:00
manmax31
40096c73cd
Add BGE embeddings support (#8848)
- Description: [BGE-large](https://huggingface.co/BAAI/bge-large-en)
embeddings from BAAI are at the top of [MTEB
leaderboard](https://huggingface.co/spaces/mteb/leaderboard). Hence
adding support for it.
- Tag maintainer: @baskaryan
- Twitter handle: @ManabChetia3

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-08-07 11:15:30 -07:00
Harrison Chase
6c3573e7f6
Harrison/aleph alpha (#8735)
Co-authored-by: PiotrMazurek <piotr.mazurek@aleph-alpha.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-08-03 21:21:15 -07:00
Bagatur
b2b71b0d35
Bagatur/eden llm (#8670)
Co-authored-by: RedhaWassim <rwasssim@gmail.com>
Co-authored-by: KyrianC <ckyrian@protonmail.com>
Co-authored-by: sam <melaine.samy@gmail.com>
2023-08-03 10:24:51 -07:00
Leonid Kuligin
b4a126ae71
Updated docs on Vertex AI going GA (#8531)
#8074

Co-authored-by: Leonid Kuligin <kuligin@google.com>
2023-07-31 17:15:04 -07:00
William FH
b7c0eb9ecb
Wfh/ref links (#8454) 2023-07-29 08:44:32 -07:00
Jiayi Ni
1efb9bae5f
FEAT: Integrate Xinference LLMs and Embeddings (#8171)
- [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>
2023-07-27 21:23:19 -07:00
Taozhi Wang
594f195e54
Add embeddings for AwaEmbedding (#8353)
- 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>
2023-07-27 17:08:00 -07:00
Juan José Torres
1cc7d4c9eb
Update SageMaker Endpoint Embeddings docs to be up to date with current requirements (#8103)
- **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>
2023-07-24 13:35:06 -07:00
Ettore Di Giacinto
ae28568e2a
Add embeddings for LocalAI (#8134)
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>
2023-07-24 12:16:49 -07:00
Bagatur
c8c8635dc9
mv module integrations docs (#8101) 2023-07-23 23:23:16 -07:00