402 lines
22 KiB
Markdown
402 lines
22 KiB
Markdown
# LangChain for Java: Supercharge your Java application with the power of LLMs
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[](https://twitter.com/intent/follow?screen_name=langchain4j)
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[](https://discord.gg/JzTFvyjG6R)
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## Project goals
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The goal of this project is to simplify the integration of AI/LLM capabilities into your Java application.
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This can be achieved thanks to:
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- **A simple and coherent layer of abstractions**, designed to ensure that your code does not depend on concrete implementations such as LLM providers, embedding store providers, etc. This allows for easy swapping of components.
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- **Numerous implementations of the above-mentioned abstractions**, providing you with a variety of LLMs and embedding stores to choose from.
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- **Range of in-demand features on top of LLMs, such as:**
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- The capability to **ingest your own data** (documentation, codebase, etc.), allowing the LLM to act and respond based on your data.
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- **Autonomous agents** for delegating tasks (defined on the fly) to the LLM, which will strive to complete them.
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- **Prompt templates** to help you achieve the highest possible quality of LLM responses.
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- **Memory** to provide context to the LLM for your current and past conversations.
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- **Structured outputs** for receiving responses from the LLM with a desired structure as Java POJOs.
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- **"AI Services"** for declaratively defining complex AI behavior behind a simple API.
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- **Chains** to reduce the need for extensive boilerplate code in common use-cases.
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- **Auto-moderation** to ensure that all inputs and outputs to/from the LLM are not harmful.
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## Code examples
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Please see examples of how LangChain4j can be used in `langchain4j-examples` repo:
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- [Examples in plain Java](https://github.com/langchain4j/langchain4j-examples/tree/main/other-examples/src/main/java)
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- [Example with Spring Boot](https://github.com/langchain4j/langchain4j-examples/blob/main/spring-boot-example/src/test/java/dev/example/CustomerSupportApplicationTest.java)
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Quarkus specific examples (leveraging the [quarkus-langchain4j](https://github.com/quarkiverse/quarkus-langchain4j) dependency which builds on this project) can be found [here](https://github.com/quarkiverse/quarkus-langchain4j/tree/main/samples)
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## News
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12 November:
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- Integration with [OpenSearch](https://opensearch.org/) by [@riferrei](https://github.com/riferrei)
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- Add support for loading documents from S3 by [@jmgang](https://github.com/jmgang)
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- Integration with [PGVector](https://github.com/pgvector/pgvector) by [@kevin-wu-os](https://github.com/kevin-wu-os)
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- Integration with [Ollama](https://ollama.ai/) by [@Martin7-1](https://github.com/Martin7-1)
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- Integration with [Amazon Bedrock](https://aws.amazon.com/bedrock/) by [@pascalconfluent](https://github.com/pascalconfluent)
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- Adding Memory Id to Tool Method Call by [@benedictstrube](https://github.com/benedictstrube)
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- [And more](https://github.com/langchain4j/langchain4j/releases/tag/0.24.0)
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29 September:
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- Updates to models API: return `Response<T>` instead of `T`. `Response<T>` contains token usage and finish reason.
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- All model and embedding store integrations now live in their own modules
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- Integration with [Vespa](https://vespa.ai/) by [@Heezer](https://github.com/Heezer)
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- Integration with [Elasticsearch](https://www.elastic.co/) by [@Martin7-1](https://github.com/Martin7-1)
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- Integration with [Redis](https://redis.io/) by [@Martin7-1](https://github.com/Martin7-1)
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- Integration with [Milvus](https://milvus.io/) by [@IuriiKoval](https://github.com/IuriiKoval)
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- Integration with [Astra DB](https://www.datastax.com/products/datastax-astra) and [Cassandra](https://cassandra.apache.org/) by [@clun](https://github.com/clun)
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- Added support for overlap in document splitters
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- Some bugfixes and smaller improvements
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29 August:
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- Offline [text classification with embeddings](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/embedding/classification/EmbeddingModelTextClassifierExample.java)
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- Integration with [Google Vertex AI](https://cloud.google.com/vertex-ai) by [@kuraleta](https://github.com/kuraleta)
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- Reworked [document splitters](https://github.com/langchain4j/langchain4j/blob/main/langchain4j/src/main/java/dev/langchain4j/data/document/splitter/DocumentSplitters.java)
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- In-memory embedding store can now be easily persisted
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- [And more](https://github.com/langchain4j/langchain4j/releases/tag/0.22.0)
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19 August:
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- Integration with [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview) by [@kuraleta](https://github.com/kuraleta)
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- Integration with Qwen models (DashScope) by [@jiangsier-xyz](https://github.com/jiangsier-xyz)
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- [Integration with Chroma](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/embedding/store/ChromaEmbeddingStoreExample.java) by [@kuraleta](https://github.com/kuraleta)
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- [Support for persistent ChatMemory](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ServiceWithPersistentMemoryForEachUserExample.java)
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10 August:
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- [Integration with Weaviate](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/embedding/store/WeaviateEmbeddingStoreExample.java) by [@Heezer](https://github.com/Heezer)
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- [Support for DOC, XLS and PPT document types](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/DocumentLoaderExamples.java) by [@oognuyh](https://github.com/oognuyh)
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- [Separate chat memory for each user](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ServiceWithMemoryForEachUserExample.java)
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- [Custom in-process embedding models](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/embedding/model/InProcessEmbeddingModelExamples.java)
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- Added lots of Javadoc
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- [And more](https://github.com/langchain4j/langchain4j/releases/tag/0.19.0)
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26 July:
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- We've added integration with [LocalAI](https://localai.io/). Now, you can use LLMs hosted locally!
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- Added support for [response streaming in AI Services](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ServiceWithStreamingExample.java).
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21 July:
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- Now, you can do [text embedding inside your JVM](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/embedding/model/InProcessEmbeddingModelExamples.java).
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17 July:
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- You can now try out OpenAI's `gpt-3.5-turbo` and `text-embedding-ada-002` models with LangChain4j for free, without needing an OpenAI account and keys! Simply use the API key "demo".
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15 July:
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- Added EmbeddingStoreIngestor
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- Redesigned document loaders (see FileSystemDocumentLoader)
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- Simplified ConversationalRetrievalChain
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- Renamed DocumentSegment into TextSegment
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- Added output parsers for numeric types
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- Added @UserName for AI Services
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- Fixed [23](https://github.com/langchain4j/langchain4j/issues/23) and [24](https://github.com/langchain4j/langchain4j/issues/24)
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11 July:
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- Added ["Dynamic Tools"](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ServiceWithDynamicToolsExample.java):
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Now, the LLM can generate code for tasks that require precise calculations, such as math and string manipulation. This will be dynamically executed in a style akin to GPT-4's code interpreter!
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We use [Judge0, hosted by Rapid API](https://rapidapi.com/judge0-official/api/judge0-ce/pricing), for code execution. You can subscribe and receive 50 free executions per day.
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5 July:
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- Now you can [add your custom knowledge base to "AI Services"](https://github.com/langchain4j/langchain4j-examples/blob/main/spring-boot-example/src/test/java/dev/example/CustomerSupportApplicationTest.java).
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Relevant information will be automatically retrieved and injected into the prompt. This way, the LLM will have a
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context of your data and will answer based on it!
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- The current date and time can now be automatically injected into the prompt using
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special `{{current_date}}`, `{{current_time}}` and `{{current_date_time}}` placeholders.
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3 July:
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- Added support for Spring Boot 3
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2 July:
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- [Added Spring Boot Starter](https://github.com/langchain4j/langchain4j-examples/blob/main/spring-boot-example/src/test/java/dev/example/CustomerSupportApplicationTest.java)
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- Added support for HuggingFace models
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1 July:
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- [Added "Tools"](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ServiceWithToolsExample.java) (support for OpenAI functions)
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## Highlights
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You can declaratively define concise "AI Services" that are powered by LLMs:
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```java
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interface Assistant {
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String chat(String userMessage);
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}
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Assistant assistant = AiServices.create(Assistant.class, model);
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String answer = assistant.chat("Hello");
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System.out.println(answer);
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// Hello! How can I assist you today?
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```
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You can use LLM as a classifier:
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```java
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enum Sentiment {
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POSITIVE, NEUTRAL, NEGATIVE
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}
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interface SentimentAnalyzer {
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@UserMessage("Analyze sentiment of {{it}}")
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Sentiment analyzeSentimentOf(String text);
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@UserMessage("Does {{it}} have a positive sentiment?")
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boolean isPositive(String text);
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}
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SentimentAnalyzer sentimentAnalyzer = AiServices.create(SentimentAnalyzer.class, model);
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Sentiment sentiment = sentimentAnalyzer.analyzeSentimentOf("It is good!");
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// POSITIVE
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boolean positive = sentimentAnalyzer.isPositive("It is bad!");
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// false
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```
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You can easily extract structured information from unstructured data:
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```java
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class Person {
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private String firstName;
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private String lastName;
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private LocalDate birthDate;
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public String toString() {...}
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}
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interface PersonExtractor {
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@UserMessage("Extract information about a person from {{it}}")
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Person extractPersonFrom(String text);
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}
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PersonExtractor extractor = AiServices.create(PersonExtractor.class, model);
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String text = "In 1968, amidst the fading echoes of Independence Day, "
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+ "a child named John arrived under the calm evening sky. "
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+ "This newborn, bearing the surname Doe, marked the start of a new journey.";
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Person person = extractor.extractPersonFrom(text);
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// Person { firstName = "John", lastName = "Doe", birthDate = 1968-07-04 }
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```
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You can define more sophisticated prompt templates using mustache syntax:
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```java
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interface Translator {
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@SystemMessage("You are a professional translator into {{language}}")
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@UserMessage("Translate the following text: {{text}}")
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String translate(@V("text") String text, @V("language") String language);
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}
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Translator translator = AiServices.create(Translator.class, model);
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String translation = translator.translate("Hello, how are you?", "Italian");
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// Ciao, come stai?
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```
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You can provide tools that LLMs can use! Can be anything: retrieve information from DB, call APIs, etc.
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See example [here](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ServiceWithToolsExample.java).
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## Compatibility
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- Java: 8 or higher
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- Spring Boot: 2 or 3
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## Getting started
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1. Add LangChain4j OpenAI dependency to your project:
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- Maven:
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```
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<dependency>
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<groupId>dev.langchain4j</groupId>
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<artifactId>langchain4j-open-ai</artifactId>
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<version>0.24.0</version>
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</dependency>
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```
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- Gradle:
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```
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implementation 'dev.langchain4j:langchain4j-open-ai:0.24.0'
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```
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2. Import your OpenAI API key:
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```java
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String apiKey = System.getenv("OPENAI_API_KEY");
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```
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You can use the API key "demo" to test OpenAI, which we provide for free.
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[How to gen an API key?](https://github.com/langchain4j/langchain4j#how-to-get-an-api-key)
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3. Create an instance of a model and start interacting:
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```java
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OpenAiChatModel model = OpenAiChatModel.withApiKey(apiKey);
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String answer = model.generate("Hello world!");
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System.out.println(answer); // Hello! How can I assist you today?
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```
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## Disclaimer
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Please note that the library is in active development and:
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- Many features are still missing. We are working hard on implementing them ASAP.
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- API might change at any moment. At this point, we prioritize good design in the future over backward compatibility
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now. We hope for your understanding.
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- We need your input! Please [let us know](https://github.com/langchain4j/langchain4j/issues/new/choose) what features you need and your concerns about the current implementation.
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## Current capabilities:
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- AI Services:
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- [Simple](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/SimpleServiceExample.java)
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- [With Memory](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ServiceWithMemoryExample.java)
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- [With Tools](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ServiceWithToolsExample.java)
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- [With Streaming](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ServiceWithStreamingExample.java)
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- [With Retriever](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ServiceWithRetrieverExample.java)
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- [With Auto-Moderation](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ServiceWithAutoModerationExample.java)
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- [With Structured Outputs, Structured Prompts, etc](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/OtherServiceExamples.java)
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- Integration with [OpenAI](https://platform.openai.com/docs/introduction) and [Azure OpenAI](https://learn.microsoft.com/en-us/azure/ai-services/openai/overview) for:
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- [Chats](https://platform.openai.com/docs/guides/chat) (sync + streaming + functions)
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- [Completions](https://platform.openai.com/docs/guides/completion) (sync + streaming)
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- [Embeddings](https://platform.openai.com/docs/guides/embeddings)
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- Integration with [Google Vertex AI](https://cloud.google.com/vertex-ai) for:
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- [Chats](https://cloud.google.com/vertex-ai/docs/generative-ai/chat/chat-prompts)
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- [Completions](https://cloud.google.com/vertex-ai/docs/generative-ai/text/text-overview)
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- [Embeddings](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings)
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- Integration with [HuggingFace Inference API](https://huggingface.co/docs/api-inference/index) for:
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- [Chats](https://huggingface.co/docs/api-inference/detailed_parameters#text-generation-task)
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- [Completions](https://huggingface.co/docs/api-inference/detailed_parameters#text-generation-task)
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- [Embeddings](https://huggingface.co/docs/api-inference/detailed_parameters#feature-extraction-task)
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- Integration with [LocalAI](https://localai.io/) for:
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- Chats (sync + streaming + functions)
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- Completions (sync + streaming)
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- Embeddings
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- Integration with [DashScope](https://dashscope.aliyun.com/) for:
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- Chats (sync + streaming)
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- Completions (sync + streaming)
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- Embeddings
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- [Chat memory](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ChatMemoryExamples.java)
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- [Persistent chat memory](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ServiceWithPersistentMemoryForEachUserExample.java)
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- [Chat with Documents](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ChatWithDocumentsExamples.java)
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- Integration with [Astra DB](https://www.datastax.com/products/datastax-astra) and [Cassandra](https://cassandra.apache.org/)
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- [Integration](https://github.com/langchain4j/langchain4j-examples/blob/main/chroma-example/src/main/java/ChromaEmbeddingStoreExample.java) with [Chroma](https://www.trychroma.com/)
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- [Integration](https://github.com/langchain4j/langchain4j-examples/blob/main/elasticsearch-example/src/main/java/ElasticsearchEmbeddingStoreExample.java) with [Elasticsearch](https://www.elastic.co/)
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- [Integration](https://github.com/langchain4j/langchain4j-examples/blob/main/milvus-example/src/main/java/MilvusEmbeddingStoreExample.java) with [Milvus](https://milvus.io/)
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- [Integration](https://github.com/langchain4j/langchain4j-examples/blob/main/pinecone-example/src/main/java/PineconeEmbeddingStoreExample.java) with [Pinecone](https://www.pinecone.io/)
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- [Integration](https://github.com/langchain4j/langchain4j-examples/blob/main/redis-example/src/main/java/RedisEmbeddingStoreExample.java) with [Redis](https://redis.io/)
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- [Integration](https://github.com/langchain4j/langchain4j-examples/blob/main/vespa-example/src/main/java/VespaEmbeddingStoreExample.java) with [Vespa](https://vespa.ai/)
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- [Integration](https://github.com/langchain4j/langchain4j-examples/blob/main/weaviate-example/src/main/java/WeaviateEmbeddingStoreExample.java) with [Weaviate](https://weaviate.io/)
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- [In-memory embedding store](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/embedding/store/InMemoryEmbeddingStoreExample.java) (can be persisted)
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- [Structured outputs](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/OtherServiceExamples.java)
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- [Prompt templates](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/PromptTemplateExamples.java)
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- [Structured prompt templates](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/StructuredPromptTemplateExamples.java)
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- [Streaming of LLM responses](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/StreamingExamples.java)
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- [Loading txt, html, pdf, doc, xls and ppt documents from the file system and via URL](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/DocumentLoaderExamples.java)
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- [Splitting documents into segments](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ChatWithDocumentsExamples.java):
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- by paragraphs, lines, sentences, words, etc
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- recursively
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- with overlap
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- Token count estimation (so that you can predict how much you will pay)
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## Coming soon:
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- Extending "AI Service" features
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- Integration with more LLM providers (commercial and free)
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- Integrations with more embedding stores (commercial and free)
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- Support for more document types
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- Long-term memory for chatbots and agents
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- Chain-of-Thought and Tree-of-Thought
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## Request features
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Please [let us know](https://github.com/langchain4j/langchain4j/issues/new/choose) what features you need!
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## Contribute
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Please help us make this open-source library better by contributing.
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Some guidelines:
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1. Follow [Google's Best Practices for Java Libraries](https://jlbp.dev/).
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2. Keep the code compatible with Java 8.
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3. Avoid adding new dependencies as much as possible. If absolutely necessary, try to (re)use the same libraries which are already present.
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4. Follow existing code styles present in the project.
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5. Ensure to add Javadoc where necessary.
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6. Provide unit and/or integration tests for your code.
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7. Large features should be discussed with maintainers before implementation.
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## Use cases
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You might ask why would I need all of this?
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Here are a couple of examples:
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- You want to implement a custom AI-powered chatbot that has access to your data and behaves the way you want it:
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- Customer support chatbot that can:
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- politely answer customer questions
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- take /change/cancel orders
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- Educational assistant that can:
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- Teach various subjects
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- Explain unclear parts
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- Assess user's understanding/knowledge
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- You want to process a lot of unstructured data (files, web pages, etc) and extract structured information from them.
|
||
For example:
|
||
- extract insights from customer reviews and support chat history
|
||
- extract interesting information from the websites of your competitors
|
||
- extract insights from CVs of job applicants
|
||
- You want to generate information, for example:
|
||
- Emails tailored for each of your customers
|
||
- Content for your app/website:
|
||
- Blog posts
|
||
- Stories
|
||
- You want to transform information, for example:
|
||
- Summarize
|
||
- Proofread and rewrite
|
||
- Translate
|
||
|
||
## Best practices
|
||
|
||
We highly recommend
|
||
watching [this amazing 90-minute tutorial](https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/)
|
||
on prompt engineering best practices, presented by Andrew Ng (DeepLearning.AI) and Isa Fulford (OpenAI).
|
||
This course will teach you how to use LLMs efficiently and achieve the best possible results. Good investment of your
|
||
time!
|
||
|
||
Here are some best practices for using LLMs:
|
||
|
||
- Be responsible. Use AI for Good.
|
||
- Be specific. The more specific your query, the best results you will get.
|
||
- Add a ["Let’s think step by step" instruction](https://arxiv.org/pdf/2205.11916.pdf) to your prompt.
|
||
- Specify steps to achieve the desired goal yourself. This will make the LLM do what you want it to do.
|
||
- Provide examples. Sometimes it is best to show LLM a few examples of what you want instead of trying to explain it.
|
||
- Ask LLM to provide structured output (JSON, XML, etc). This way you can parse response more easily and distinguish
|
||
different parts of it.
|
||
- Use unusual delimiters, such as \```triple backticks``` to help the LLM distinguish
|
||
data or input from instructions.
|
||
|
||
## How to get an API key
|
||
You will need an API key from OpenAI (paid) or HuggingFace (free) to use LLMs hosted by them.
|
||
|
||
We recommend using OpenAI LLMs (`gpt-3.5-turbo` and `gpt-4`) as they are by far the most capable and are reasonably priced.
|
||
|
||
It will cost approximately $0.01 to generate 10 pages (A4 format) of text with `gpt-3.5-turbo`. With `gpt-4`, the cost will be $0.30 to generate the same amount of text. However, for some use cases, this higher cost may be justified.
|
||
|
||
[How to get OpenAI API key](https://www.howtogeek.com/885918/how-to-get-an-openai-api-key/).
|
||
|
||
For embeddings, we recommend using one of the models from the [HuggingFace MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
|
||
You'll have to find the best one for your specific use case.
|
||
|
||
Here's how to get a HuggingFace API key:
|
||
- Create an account on https://huggingface.co
|
||
- Go to https://huggingface.co/settings/tokens
|
||
- Generate a new access token
|