Commit Graph

94 Commits

Author SHA1 Message Date
Piyush Jain
562fdfc8f9
Bedrock llm and embeddings (#5464)
# Bedrock LLM and Embeddings
This PR adds a new LLM and an Embeddings class for the
[Bedrock](https://aws.amazon.com/bedrock) service. The PR also includes
example notebooks for using the LLM class in a conversation chain and
embeddings usage in creating an embedding for a query and document.

**Note**: AWS is doing a private release of the Bedrock service on
05/31/2023; users need to request access and added to an allowlist in
order to start using the Bedrock models and embeddings. Please use the
[Bedrock Home Page](https://aws.amazon.com/bedrock) to request access
and to learn more about the models available in Bedrock.

<!-- For a quicker response, figure out the right person to tag with @

  @hwchase17 - project lead

  Tracing / Callbacks
  - @agola11

  Async
  - @agola11

  DataLoaders
  - @eyurtsev

  Models
  - @hwchase17
  - @agola11

  Agents / Tools / Toolkits
  - @vowelparrot

  VectorStores / Retrievers / Memory
  - @dev2049

 -->
2023-05-31 07:17:01 -07:00
Jeff Vestal
46e181aa8b
Allow ElasticsearchEmbeddings to create a connection with ES Client object (#5321)
This PR adds a new method `from_es_connection` to the
`ElasticsearchEmbeddings` class allowing users to use Elasticsearch
clusters outside of Elastic Cloud.

Users can create an Elasticsearch Client object and pass that to the new
function.
The returned object is identical to the one returned by calling
`from_credentials`

```
# Create Elasticsearch connection
es_connection = Elasticsearch(
    hosts=['https://es_cluster_url:port'], 
    basic_auth=('user', 'password')
)

# Instantiate ElasticsearchEmbeddings using es_connection
embeddings = ElasticsearchEmbeddings.from_es_connection(
  model_id,
  es_connection,
)
```

I also added examples to the elasticsearch jupyter notebook

Fixes # https://github.com/hwchase17/langchain/issues/5239

---------

Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-05-30 17:26:30 -07:00
Harrison Chase
d6fb25c439
Harrison/prediction guard update (#5404)
Co-authored-by: Daniel Whitenack <whitenack.daniel@gmail.com>
2023-05-29 07:14:59 -07:00
Harrison Chase
416c8b1da3
Harrison/deep infra (#5403)
Co-authored-by: Yessen Kanapin <yessenzhar@gmail.com>
Co-authored-by: Yessen Kanapin <yessen@deepinfra.com>
2023-05-29 07:10:50 -07:00
Timothy Ji
100d6655df
Reformat openai proxy setting as code (#5330)
# Reformat the openai proxy setting as code


  Only affect the doc for openai Model
  - @hwchase17
  - @agola11
2023-05-29 07:02:47 -07:00
Oleh Kuznetsov
f6615cac41
Update llamacpp demonstration notebook (#5344)
# Update llamacpp demonstration notebook

Add instructions to install with BLAS backend, and update the example of
model usage.

Fixes #5071. However, it is more like a prevention of similar issues in
the future, not a fix, since there was no problem in the framework
functionality

## Who can review?

Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:

- @hwchase17 
- @agola11
2023-05-29 06:43:26 -07:00
Michael Landis
f75f0dbad6
docs: improve flow of llm caching notebook (#5309)
# docs: improve flow of llm caching notebook

The notebook `llm_caching` demos various caching providers. In the
previous version, there was setup common to all examples but under the
`In Memory Caching` heading.

If a user comes and only wants to try a particular example, they will
run the common setup, then the cells for the specific provider they are
interested in. Then they will get import and variable reference errors.
This commit moves the common setup to the top to avoid this.

## Who can review?

Community members can review the PR once tests pass. Tag
maintainers/contributors who might be interested:

@dev2049
2023-05-26 13:34:11 -04:00
Xiangrui Meng
aec642febb
LLM wrapper for Databricks (#5142)
This PR adds LLM wrapper for Databricks. It supports two endpoint types:
* serving endpoint
* cluster driver proxy app

An integration notebook is included to show how it works.


Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Gengliang Wang <gengliang@apache.org>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-05-25 19:19:37 -07:00
Michael Landis
7047a2c1af
feat: add Momento as a standard cache and chat message history provider (#5221)
# Add Momento as a standard cache and chat message history provider

This PR adds Momento as a standard caching provider. Implements the
interface, adds integration tests, and documentation. We also add
Momento as a chat history message provider along with integration tests,
and documentation.

[Momento](https://www.gomomento.com/) is a fully serverless cache.
Similar to S3 or DynamoDB, it requires zero configuration,
infrastructure management, and is instantly available. Users sign up for
free and get 50GB of data in/out for free every month.

## Before submitting

 We have added documentation, notebooks, and integration tests
demonstrating usage.

Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-05-25 19:13:21 -07:00
Ravindra Marella
b3988621c5
Add C Transformers for GGML Models (#5218)
# Add C Transformers for GGML Models
I created Python bindings for the GGML models:
https://github.com/marella/ctransformers

Currently it supports GPT-2, GPT-J, GPT-NeoX, LLaMA, MPT, etc. See
[Supported
Models](https://github.com/marella/ctransformers#supported-models).


It provides a unified interface for all models:

```python
from langchain.llms import CTransformers

llm = CTransformers(model='/path/to/ggml-gpt-2.bin', model_type='gpt2')

print(llm('AI is going to'))
```

It can be used with models hosted on the Hugging Face Hub:

```py
llm = CTransformers(model='marella/gpt-2-ggml')
```

It supports streaming:

```py
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler

llm = CTransformers(model='marella/gpt-2-ggml', callbacks=[StreamingStdOutCallbackHandler()])
```

Please see [README](https://github.com/marella/ctransformers#readme) for
more details.
---------

Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-05-25 13:42:44 -07:00
Yves Maurer
88ed8e1cd6
Added the option of specifying a proxy for the OpenAI API (#5246)
# Added the option of specifying a proxy for the OpenAI API

Fixes #5243

Co-authored-by: Yves Maurer <>
2023-05-25 09:50:25 -07:00
Archon
5cdd9ab7e1
Add MiniMax embeddings (#5174)
- Add support for MiniMax embeddings

Doc: [MiniMax
embeddings](https://api.minimax.chat/document/guides/embeddings?id=6464722084cdc277dfaa966a)

---------

Co-authored-by: Archon <archongum@outlook.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-05-25 06:57:49 -07:00
Harrison Chase
a775aa6389
Harrison/vertex (#5049)
Co-authored-by: Leonid Kuligin <kuligin@google.com>
Co-authored-by: Leonid Kuligin <lkuligin@yandex.ru>
Co-authored-by: sasha-gitg <44654632+sasha-gitg@users.noreply.github.com>
Co-authored-by: Justin Flick <Justinjayflick@gmail.com>
Co-authored-by: Justin Flick <jflick@homesite.com>
2023-05-24 15:51:12 -07:00
Harrison Chase
11c26ebb55
Harrison/modelscope (#5156)
Co-authored-by: thomas-yanxin <yx20001210@163.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-05-24 08:06:45 -07:00
Jeff Vestal
cf19a2a59f
example usage (#5182)
Adding example usage for elasticsearch knn embeddings
[per](https://github.com/hwchase17/langchain/pull/3401#issuecomment-1548518389)


https://github.com/hwchase17/langchain/blob/master/langchain/embeddings/elasticsearch.py
2023-05-24 07:47:15 -07:00
Ikko Eltociear Ashimine
fff21a0b35
Update rellm_experimental.ipynb (#5189)
# Your PR Title (What it does)

HuggingFace -> Hugging Face
2023-05-24 11:41:00 +00:00
Nolan Tremelling
faa26650c9
Beam (#4996)
# Beam

Calls the Beam API wrapper to deploy and make subsequent calls to an
instance of the gpt2 LLM in a cloud deployment. Requires installation of
the Beam library and registration of Beam Client ID and Client Secret.
Additional calls can then be made through the instance of the large
language model in your code or by calling the Beam API.

---------

Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-05-24 01:25:18 -07:00
Daniel King
de6e6c764e
Add MosaicML inference endpoints (#4607)
# Add MosaicML inference endpoints
This PR adds support in langchain for MosaicML inference endpoints. We
both serve a select few open source models, and allow customers to
deploy their own models using our inference service. Docs are here
(https://docs.mosaicml.com/en/latest/inference.html), and sign up form
is here (https://forms.mosaicml.com/demo?utm_source=langchain). I'm not
intimately familiar with the details of langchain, or the contribution
process, so please let me know if there is anything that needs fixing or
this is the wrong way to submit a new integration, thanks!

I'm also not sure what the procedure is for integration tests. I have
tested locally with my api key.

## Who can review?
@hwchase17

---------

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-05-23 15:59:08 -07:00
Jeff Vestal
0b542a9706
Add ElasticsearchEmbeddings class for generating embeddings using Elasticsearch models (#3401)
This PR introduces a new module, `elasticsearch_embeddings.py`, which
provides a wrapper around Elasticsearch embedding models. The new
ElasticsearchEmbeddings class allows users to generate embeddings for
documents and query texts using a [model deployed in an Elasticsearch
cluster](https://www.elastic.co/guide/en/machine-learning/current/ml-nlp-model-ref.html#ml-nlp-model-ref-text-embedding).

### Main features:

1. The ElasticsearchEmbeddings class initializes with an Elasticsearch
connection object and a model_id, providing an interface to interact
with the Elasticsearch ML client through
[infer_trained_model](https://elasticsearch-py.readthedocs.io/en/v8.7.0/api.html?highlight=trained%20model%20infer#elasticsearch.client.MlClient.infer_trained_model)
.
2. The `embed_documents()` method generates embeddings for a list of
documents, and the `embed_query()` method generates an embedding for a
single query text.
3. The class supports custom input text field names in case the deployed
model expects a different field name than the default `text_field`.
4. The implementation is compatible with any model deployed in
Elasticsearch that generates embeddings as output.

### Benefits:

1. Simplifies the process of generating embeddings using Elasticsearch
models.
2. Provides a clean and intuitive interface to interact with the
Elasticsearch ML client.
3. Allows users to easily integrate Elasticsearch-generated embeddings.

Related issue https://github.com/hwchase17/langchain/issues/3400

---------

Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-05-23 14:50:33 -07:00
Myeongseop Kim
7a75bb2121
docs: fix minor typo + add wikipedia package installation part in human_input_llm.ipynb (#5118)
# Fix typo + add wikipedia package installation part in
human_input_llm.ipynb
This PR
1. Fixes typo ("the the human input LLM"), 
2. Addes wikipedia package installation part (in accordance with
`WikipediaQueryRun`
[documentation](https://python.langchain.com/en/latest/modules/agents/tools/examples/wikipedia.html))

in `human_input_llm.ipynb`
(`docs/modules/models/llms/examples/human_input_llm.ipynb`)
2023-05-23 10:59:30 -07:00
Matt Rickard
de6a401a22
Add OpenLM LLM multi-provider (#4993)
OpenLM is a zero-dependency OpenAI-compatible LLM provider that can call
different inference endpoints directly via HTTP. It implements the
OpenAI Completion class so that it can be used as a drop-in replacement
for the OpenAI API. This changeset utilizes BaseOpenAI for minimal added
code.

---------

Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-05-22 18:09:53 -07:00
SimFG
f07b9fde74
Update the GPTCache example (#4985)
# Update the GPTCache example

Fixes #4757
2023-05-19 16:35:36 -07:00
so2liu
3002c1d508
fix: error in gptcache example nb (#4930) 2023-05-18 11:49:45 -07:00
Alexey Nominas
c9e2a01875
Update GPT4ALL integration (#4567)
# Update GPT4ALL integration

GPT4ALL have completely changed their bindings. They use a bit odd
implementation that doesn't fit well into base.py and it will probably
be changed again, so it's a temporary solution.

Fixes #3839, #4628
2023-05-18 09:38:54 -07:00
Ismael G Serrano
41e2394c9c
Fix AzureOpenAI embeddings documentation example. model -> deployment (#4389)
# Documentation for Azure OpenAI embeddings model

- OPENAI_API_VERSION environment variable is needed for the endpoint
- The constructor does not work with model, it works with deployment.

I fixed it in the notebook.

(This is my first contribution)

## Who can review?

@hwchase17 
@agola

Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-05-17 21:05:53 -07:00
Leonid Ganeline
b96ab4b763
docs retriever improvements (#4430)
# Docs: improvements in the `retrievers/examples/` notebooks

Its primary purpose is to make the Jupyter notebook examples
**consistent** and more suitable for first-time viewers.
- add links to the integration source (if applicable) with a short
description of this source;
- removed `_retriever` suffix from the file names (where it existed) for
consistency;
- removed ` retriever` from the notebook title (where it existed) for
consistency;
- added code to install necessary Python package(s);
- added code to set up the necessary API Key.
- very small fixes in notebooks from other folders (for consistency):
  - docs/modules/indexes/vectorstores/examples/elasticsearch.ipynb
  - docs/modules/indexes/vectorstores/examples/pinecone.ipynb
  - docs/modules/models/llms/integrations/cohere.ipynb
- fixed misspelling in langchain/retrievers/time_weighted_retriever.py
comment (sorry, about this change in a .py file )

## Who can review
@dev2049
2023-05-17 15:29:22 -07:00
Sean Morgan
5372a06a8c
DOC: Fix SageMaker example (#4598)
# Fix SageMaker example typing

Since https://github.com/hwchase17/langchain/pull/3249 a new type
`LLMContentHandler` is enforced for SageMaker Endpoints

Fixes #4168
2023-05-16 17:28:16 -07:00
Harrison Chase
a48810fb21
dont have openai_api_version by default (#4687)
an alternative to https://github.com/hwchase17/langchain/pull/4234/files
2023-05-14 18:26:08 -07:00
Zander Chase
d85b04be7f
Add RELLM and JSONFormer experimental LLM decoding (#4185)
[RELLM](https://github.com/r2d4/rellm) is a library that wraps local
HuggingFace pipeline models for structured decoding.

RELLM works by generating tokens one at a time. At each step, it masks
tokens that don't conform to the provided partial regular expression.

[JSONFormer](https://github.com/1rgs/jsonformer) is a bit different, where it sequentially adds the keys then decodes each value directly
2023-05-14 22:40:03 +00:00
Harrison Chase
1e322ffc1c change heading 2023-05-13 09:52:23 -07:00
Harrison Chase
6265cbfb11
Harrison/standard llm interface (#4615) 2023-05-13 09:05:31 -07:00
Sai Vinay G
cf4c1394a2
feat: Added class to support huggingface text generation inference server (#4447)
[Text Generation
Inference](https://github.com/huggingface/text-generation-inference) is
a Rust, Python and gRPC server for generating text using LLMs.

This pull request add support for self hosted Text Generation Inference
servers.

feature: #4280

---------

Co-authored-by: Your Name <you@example.com>
Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-05-12 07:32:37 -07:00
SimFG
7bcf238a1a
Optimize the initialization method of GPTCache (#4522)
Optimize the initialization method of GPTCache, so that users can use GPTCache more quickly.
2023-05-11 16:15:23 -07:00
kYLe
446b60d803
Fix a typo in langchain/docs/modules/models/llms/integrations/anyscale.ipynb (#4526) 2023-05-11 09:03:04 -07:00
kYLe
0d51a1f12b
Add LLMs support for Anyscale Service (#4350)
Add Anyscale service integration under LLM

Co-authored-by: Dev 2049 <dev.dev2049@gmail.com>
2023-05-11 00:39:59 -07:00
Ankush Gola
b3ecce0545
fix json saving, update docs to reference anthropic chat model (#4364)
Fixes # (issue)
https://github.com/hwchase17/langchain/issues/4085
2023-05-08 15:30:52 -07:00
PawelFaron
04b74d0446
Adjusted GPT4All llm to streaming API and added support for GPT4All_J (#4131)
Fix for these issues:
https://github.com/hwchase17/langchain/issues/4126

https://github.com/hwchase17/langchain/issues/3839#issuecomment-1534258559

---------

Co-authored-by: Pawel Faron <ext-pawel.faron@vaisala.com>
2023-05-06 15:14:09 -07:00
Harrison Chase
64940e9d0f
docs for azure (#4238) 2023-05-06 10:16:00 -07:00
Myeongseop Kim
747b5f87c2
Add HumanInputLLM (#4160)
Related: #4028, I opened a new PR because (1) I was unable to unstage
mistakenly committed files (I'm not familiar with git enough to resolve
this issue), (2) I felt closing the original PR and opening a new PR
would be more appropriate if I changed the class name.

This PR creates HumanInputLLM(HumanLLM in #4028), a simple LLM wrapper
class that returns user input as the response. I also added a simple
Jupyter notebook regarding how and why to use this LLM wrapper. In the
notebook, I went over how to use this LLM wrapper and showed example of
testing `WikipediaQueryRun` using HumanInputLLM.
 
I believe this LLM wrapper will be useful especially for debugging,
educational or testing purpose.
2023-05-06 09:48:40 -07:00
Gengliang Wang
8af25867cb
Simplify HumanMessages in the quick start guide (#4026)
In the section `Get Message Completions from a Chat Model` of the quick
start guide, the HumanMessage doesn't need to include `Translate this
sentence from English to French.` when there is a system message.

Simplify HumanMessages in these examples can further demonstrate the
power of LLM.
2023-05-03 22:45:03 -07:00
Harrison Chase
5f30cc8713
Harrison/knn retriever (#4083)
Co-authored-by: Yuichi Tateno (secon) <hotchpotch@users.noreply.github.com>
2023-05-03 21:21:58 -07:00
Davis Chase
df3bc707fc
Dev2049/callback example fix (#4010)
Closes #3997

---------

Co-authored-by: Akshaj Jain <akshaj.jain@gmail.com>
2023-05-02 16:20:16 -07:00
MichaelMDowling
36ee60c96c
Update \docs\modules\models\text_embedding\examples\openai.ipynb (#3976)
Single edit to: models/text_embedding/examples/openai.ipynb - Line 88:
changed from: "embeddings = OpenAIEmbeddings(model_name=\"ada\")" to
"embeddings = OpenAIEmbeddings()" as model_name is no longer part of the
OpenAIEmbeddings class.
2023-05-02 14:41:31 -07:00
liviuasnash1
6396a4ad8d
Fix documentation typos (#3870)
Co-authored-by: Liviu Asnash <liviua@maximallearning.com>
2023-05-01 20:58:38 -07:00
Samuel Dion-Girardeau
c5c33786a7
Fix bad spellings for 'convenience' (#3936)
Found in the docs for chat prompt templates:

https://python.langchain.com/en/latest/getting_started/getting_started.html#chat-prompt-templates

and fixed similar issues in neighboring notebooks.
2023-05-01 20:57:06 -07:00
Zander Chase
c4cb55a0c5
[Breaking] Migrate GPT4All to use PyGPT4All (#3934)
Seems the pyllamacpp package is no longer the supported bindings from
gpt4all. Tested that this works locally.

Given that the older models weren't very performant, I think it's better
to migrate now without trying to include a lot of try / except blocks

---------

Co-authored-by: Nissan Pow <npow@users.noreply.github.com>
Co-authored-by: Nissan Pow <pownissa@amazon.com>
2023-05-01 20:42:45 -07:00
Ankush Gola
d3ec00b566
Callbacks Refactor [base] (#3256)
Co-authored-by: Nuno Campos <nuno@boringbits.io>
Co-authored-by: Davis Chase <130488702+dev2049@users.noreply.github.com>
Co-authored-by: Zander Chase <130414180+vowelparrot@users.noreply.github.com>
Co-authored-by: Harrison Chase <hw.chase.17@gmail.com>
2023-04-30 11:14:09 -07:00
Harrison Chase
be7a8e0824
Harrison/redis cache (#3766)
Co-authored-by: Tyler Hutcherson <tyler.hutcherson@redis.com>
2023-04-28 20:47:18 -07:00
plutopulp
6d6fd1b9e1
Add PipelineAI LLM integration (#3644)
Add PipelineAI LLM integration
2023-04-27 08:22:26 -07:00
Ehsan M. Kermani
4a246e2fd6
Allow clearing cache and fix gptcache (#3493)
This PR

* Adds `clear` method for `BaseCache` and implements it for various
caches
* Adds the default `init_func=None` and fixes gptcache integtest
* Since right now integtest is not running in CI, I've verified the
changes by running `docs/modules/models/llms/examples/llm_caching.ipynb`
(until proper e2e integtest is done in CI)
2023-04-26 22:03:50 -07:00