Announcing Xata's integrations with LangChain, available both as a vector store and a memory store.
Written by
Tudor Golubenco
Published on
August 29, 2023
Over the past few weeks, we’ve merged four Xata integrations to the LangChain repositories, and today we’re happy to unveil them as part of the Xata launch week! In this blog post, we’ll look into what LangChain is and why Xata is a good data companion for AI applications.
LangChain is a popular open-source framework for developing AI applications powered by Large Language Models (LLMs). You can think of it as a collection of composable components implemented in Python and TypeScript that you can combine to implement various AI use cases.
The components typically offer a common API for different models (OpenAI, Llama, Replicate, etc.), vector stores (Pinecone, Weaviate, Chroma, etc.), databases as memory stores (DynamoDB, Redis, Planetscale, etc.) and more. By offering a common API across different models, for example, LangChain makes it easy to switch between models and compare results or use different models for different parts of the app.
These components can then be “chained together” in more complex applications, and LangChain comes with off-the-shelf implementations for several popular AI use cases (Q&A chat bots, summarization, autonomous agents, etc).
While Xata has a built-in API for the “ChatGPT on your data” use case, in this blog post we’ll see how one can implement similar functionality using LangChain and Xata integrations. This can allow for more flexibility in the details of the implementation (for example, you can chose a non-OpenAI model) at the cost of having more code to write and maintain.
As of today, the following integrations are available :
Each integration comes with one or two code examples in the doc pages linked above.
The four integrations already make Xata one of the most comprehensive data solutions for LangChain, and we’re just getting started! For the near future, we’re planning to add custom retrievers for the Xata keyword and hybrid search and the Xata Ask AI endpoint.
As we’ve pointed out above, a key benefit of LangChain is that it supports a lot of solutions for each integration type. For example, at the moment, the Python LangChain integrates with 47 (!) vector stores, while LangChain.js integrates with 24 vector stores.
So what makes Xata different and why should you consider it for your AI apps?
Xata is a serverless data platform that stores data in PostgreSQL, but also replicates it automatically to Elasticsearch. This means that it offers functionality from both Postgres (ACID transactions, constraints, etc.) and from Elasticsearch (full-text search, vector search, hybrid search) behind the same simple serverless API.
Here is why you should consider Xata for you LangChain application:
To get started with Xata and LangChain, you can use the minimal code samples from each of the integrations above. If you are looking for more complex examples, for Python, there is a more complete example in this Jupyter Notebook. For TypeScript, check out the announcement blog post on the LangChain blog.
While the integrations added so far already make Xata one of the most comprehensive and easy to use data solutions for LangChain and AI applications, this is only the beginning! We’re planning to add more components that take advantage of Xata’s BM25 search and the Ask endpoint.
If you have any questions or ideas or if you need help implementing Xata with LangChain, reach out to us on Discord or join us on Twitter.
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