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LangChain Profile picture
@LangChainAI
Aug 21 • 5 tweets • 2 min read Twitter logo Read on Twitter
Happy Monday 🌞 Here's what we've added to 🦜🔗 over the weekend:

✅ Pydantic v2 compatibility
🔢 Ernie model embeddings
🚿 Streaming support for text-generation-webui
👆 @SharePoint document loader
✅ Pydantic v2 compatibility

🔢 Ernie model embeddings

Use Ernie Embedding-V1 based on Baidu's Wenxin LLM thanks to GH user axiangcoding!

Docs: python.langchain.com/docs/integrati…
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🚿 Streaming support for text-generation-webui

Textgen is a Grade web UI and API for language models, that now supports model streaming thanks to GH user uetuluk!

Docs: python.langchain.com/docs/integrati…
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👆 @SharePoint document loader

Microsoft SharePoint makes it easy for teams to share and collaborate on documents.

Thanks to @zeneto you can now easily load all those documents into your LLM applications 🙌

Docs: python.langchain.com/docs/integrati…
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More from @LangChainAI

LangChain Profile picture
Aug 22
New in 🦜🔗 Python:

🌌 @ainetwork_ai agent toolkit
🐻‍❄️ @DataPolars data loader
🚿 @AzureML online endpoint deployment
🪐 @epsilla_inc vector store

a 🧵:
🌌 @ainetwork_ai agent toolkit

Enable an agent to to transfer AINetwork tokens, read and write values, create apps, and more using the AINetworkToolkit by GH user klae01!

Docs: python.langchain.com/docs/integrati…
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🐻‍❄️ @DataPolars data loader

Polars is a fast DataFrame interface on top of an OLAP Query Engine implemented in Rust using Apache Arrow Columnar Format as the memory model.

Thanks to GH toddkim95, you can easily load Polars data into your LLM apps🙏

Docs: python.langchain.com/docs/integrati…
Read 5 tweets
LangChain Profile picture
Aug 21
📰you can now subscribe to our blog!

in the last 2 weeks alone, we've had awesome posts about leveraging typescript, fine-tuning apps, RAG, Q&A over csv's, agents, and more (🧵)

if you don't want miss any future ones...

blog.langchain.dev
@MultiON_AI bringing the power of agents to the web

blog.langchain.dev/multion-x-lang…
a walkthrough of building three foundational LLM apps using TypeScript, LangChain.js, and @zep_ai

blog.langchain.dev/zep-x-langsmit…
Read 8 tweets
LangChain Profile picture
Aug 17
🗃️ Embeddings Cache 🗃️

This is one we've received a lot of requests for — caching embeddings to prevent redundant computation.

With the new ``CacheBackedEmbeddings`` you can wrap any existing embedding model, combine it with a storage mechanism, and voila!

Let's take a look:
Store embeddings in memory

The InMemoryStore is an easy, setup-free storage mechanism to use for testing and prototyping.

We just set our embedding model (OpenAIEmbeddings), set our cache store (InMemoryStore), and watch as our embedding time goes from 160 ms -> 2 ms ⚡️ Image
Use a local file store

For simple persistent storage, we can use the LocalFileStore, which saves our embeddings as files to our machine. Image
Read 5 tweets
LangChain Profile picture
Aug 16
The latest and greatest in 🦜🔗:

🧠 @neural_internet Bittensor LLM
⚡ DashVector store
🤖 @zep_ai vector store
♻️ @elastic search self-querying retriever

a 🧵:
🧠 @neural_internet Bittensor LLM

Run models on the Bittensor protocol using this new interface from Neural Internet.

s/o to GH user Kunj-2206 for the integration!

Docs: https://t.co/yMAXfahpc1python.langchain.com/docs/integrati…
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⚡ DashVector store

DashVector is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search.

Now in 🦜🔗 thanks to GH user xiaoyuxee!

Docs: https://t.co/5jaAojlEv4python.langchain.com/docs/integrati…
Image
Read 5 tweets
LangChain Profile picture
Aug 16
🛟Introducing Fallbacks in 🦜🔗

It’s not uncommon to encounter issues with LLM API's. In production, you need to gracefully handle such issues.

We’ve introduced Fallbacks to the LangChain Expression Language (LCEL) to help with just that.

Available in 🦜🔗 Python and JS! a 🧵: Image
🙅Handling API Errors

A request to an LLM API can fail for a variety of reasons - the API could be down, you could have hit rate limits, any number of things. Here’s how we can handle this with fallbacks:
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🚃Fallbacks for Sequences

When one model fails, you may need to adjust more than just the model for downstream code to work. For example, you may need to update the prompt as well as the output parsing. Luckily, you can create fallbacks for entire sequences of LCEL objects.

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Read 6 tweets
LangChain Profile picture
Aug 8
The latest in 🦜🔗:

We've got 4 new model integrations for you!

⏹️ @anyscalecompute chat models
🔢 BAAI general embedding models
🦙 Ollama LLMs
🌌 Nebula LLM by @symbldotai

a 🧵:
⏹️ @anyscalecompute chat models

Anyscale Endpoints is a fast and scalable API to integrate OSS LLMs into your app.

With the new chat model integration by GH user oshuasundance-swca, you can now use it when running models like llama-2!

Docs: https://t.co/rrep0nnKqtpython.langchain.com/docs/integrati…
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🔢 BAAI general embedding models

The BAAI general embedding (BGE) models are a recent set of models that currently rank first on the MTEB benchmark.

Thanks to @ManabChetia3, you can now load them in just a few LOC 🙏 Image
Read 5 tweets

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