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## Issue Closes #1722 ## Change Parameterized the field name used to look up the text value in Weaviate. ## General checklist <!-- Please double-check the following points and mark them like this: [X] --> - [x ] There are no breaking changes - [? ] I have added unit and integration tests for my change (Based on the test that are there I am not really sure what to add that would really test it well) - [x ] I have manually run all the unit and integration tests in the module I have added/changed, and they are all green - [x ] I have manually run all the unit and integration tests in the [core](https://github.com/langchain4j/langchain4j/tree/main/langchain4j-core) and [main](https://github.com/langchain4j/langchain4j/tree/main/langchain4j) modules, and they are all green <!-- Before adding documentation and example(s) (below), please wait until the PR is reviewed and approved. --> - [ ] I have added/updated the [documentation](https://github.com/langchain4j/langchain4j/tree/main/docs/docs) - [ ] I have added an example in the [examples repo](https://github.com/langchain4j/langchain4j-examples) (only for "big" features) - [ ] I have added/updated [Spring Boot starter(s)](https://github.com/langchain4j/langchain4j-spring) (if applicable) |
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README.md
LangChain for Java: Supercharge your Java application with the power of LLMs
Introduction
Welcome!
The goal of LangChain4j is to simplify integrating LLMs into Java applications.
Here's how:
- Unified APIs: LLM providers (like OpenAI or Google Vertex AI) and embedding (vector) stores (such as Pinecone or Milvus) use proprietary APIs. LangChain4j offers a unified API to avoid the need for learning and implementing specific APIs for each of them. To experiment with different LLMs or embedding stores, you can easily switch between them without the need to rewrite your code. LangChain4j currently supports 15+ popular LLM providers and 15+ embedding stores.
- Comprehensive Toolbox: During the past year, the community has been building numerous LLM-powered applications, identifying common abstractions, patterns, and techniques. LangChain4j has refined these into practical code. Our toolbox includes tools ranging from low-level prompt templating, chat memory management, and output parsing to high-level patterns like AI Services and RAG. For each abstraction, we provide an interface along with multiple ready-to-use implementations based on common techniques. Whether you're building a chatbot or developing a RAG with a complete pipeline from data ingestion to retrieval, LangChain4j offers a wide variety of options.
- Numerous Examples: These examples showcase how to begin creating various LLM-powered applications, providing inspiration and enabling you to start building quickly.
LangChain4j began development in early 2023 amid the ChatGPT hype. We noticed a lack of Java counterparts to the numerous Python and JavaScript LLM libraries and frameworks, and we had to fix that! Although "LangChain" is in our name, the project is a fusion of ideas and concepts from LangChain, Haystack, LlamaIndex, and the broader community, spiced up with a touch of our own innovation.
We actively monitor community developments, aiming to quickly incorporate new techniques and integrations, ensuring you stay up-to-date. The library is under active development. While some features are still being worked on, the core functionality is in place, allowing you to start building LLM-powered apps now!
Documentation
Documentation can be found here.
Getting Started
Getting started guide can be found here.
Code Examples
Please see examples of how LangChain4j can be used in langchain4j-examples repo:
- Examples in plain Java
- Examples with Quarkus (uses quarkus-langchain4j dependency)
- Example with Spring Boot
Useful Materials
Useful materials can be found here.
Get Help
Please use Discord or GitHub discussions to get help.
Request Features
Please let us know what features you need by opening an issue.
Contribute
Contribution guidelines can be found here.