released 0.19.0
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deep-learning-dynamo
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README.md
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README.md
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@ -20,12 +20,20 @@ This can be achieved thanks to:
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## News
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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/InProcessEmbeddingModelExamples.java).
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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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@ -76,11 +84,12 @@ 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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Assistant assistant = AiServices.create(Assistant.class, model);
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String answer = assistant.chat("Hello");
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String answer = assistant.chat("Hello");
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System.out.println(answer); // Hello! How can I assist you today?
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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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@ -99,13 +108,13 @@ interface SentimentAnalyzer {
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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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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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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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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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@ -126,14 +135,14 @@ interface PersonExtractor {
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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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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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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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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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@ -146,10 +155,10 @@ interface Translator {
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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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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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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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@ -215,6 +224,8 @@ Please note that the library is in active development and:
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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 (ChatGPT)](https://platform.openai.com/docs/introduction) for:
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@ -231,13 +242,14 @@ Please note that the library is in active development and:
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- Embeddings
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- [Memory for Chats](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ChatMemoryExamples.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 [Pinecone](https://docs.pinecone.io/docs/overview) embedding store
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- In-memory embedding store (for prototyping and testing)
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- [Integration](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/embedding/store/PineconeEmbeddingStoreExample.java) with [Pinecone](https://www.pinecone.io/)
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- [Integration](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/embedding/store/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) (for prototyping and testing)
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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 text and PDF documents from the file system and via URL](https://github.com/langchain4j/langchain4j-examples/blob/main/other-examples/src/main/java/ChatWithDocumentsExamples.java)
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- [Loading txt, 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 paragraph
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- by sentence
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@ -247,8 +259,8 @@ Please note that the library is in active development and:
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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 open)
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- Integrations with more embedding stores (commercial and open)
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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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@ -301,7 +313,7 @@ Here are some best practices for using LLMs:
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- Be responsible. Use AI for Good.
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- Be specific. The more specific your query, the best results you will get.
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- Add [magical "Let’s think step by step" instruction](https://arxiv.org/pdf/2205.11916.pdf) to your prompt.
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- Add a ["Let’s think step by step" instruction](https://arxiv.org/pdf/2205.11916.pdf) to your prompt.
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- Specify steps to achieve the desired goal yourself. This will make the LLM do what you want it to do.
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- Provide examples. Sometimes it is best to show LLM a few examples of what you want instead of trying to explain it.
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- Ask LLM to provide structured output (JSON, XML, etc). This way you can parse response more easily and distinguish
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