From caa6355865d9d00821da169606752fd393a311db Mon Sep 17 00:00:00 2001 From: HamaWhiteGG Date: Thu, 30 Nov 2023 17:35:13 +0800 Subject: [PATCH] Update Readme --- README.md | 278 ++++-------------- .../extras/modules/state_of_the_union.txt | 0 {docs => data}/extras/modules/titanic.csv | 0 .../extras/modules/titanic_flink.csv | 0 .../Auto-Evaluation of Metadata Filtering.md | 0 .../notion_db/images/Untitled 1.png | Bin .../notion_db/images/Untitled 2.png | Bin .../notion_db/images/Untitled.png | Bin .../notion_db/images/ret_trim.mov | Bin {docs => data}/images/Appreciation code.png | Bin data/images/Google agent example output.png | Bin 0 -> 280322 bytes data/images/Google agent example.png | Bin 0 -> 595107 bytes data/images/Langchain overview.png | Bin 0 -> 295043 bytes data/images/SQL chains.png | Bin 0 -> 100084 bytes .../sql/toolkit/FlinkSqlToolkitTest.java | 2 +- .../sql/toolkit/SparkSqlToolkitTest.java | 2 +- .../directory/DirectoryLoaderTest.java | 2 +- .../document/loaders/helpers/HelpersTest.java | 2 +- .../notion/NotionDirectoryLoaderTest.java | 2 +- .../vectorstores/pinecone/PineconeTest.java | 2 +- .../examples/chains/MilvusExample.java | 2 +- .../chains/RetrievalMarkdownExample.java | 2 +- .../examples/chains/RetrievalQaExample.java | 2 +- 23 files changed, 68 insertions(+), 228 deletions(-) rename {docs => data}/extras/modules/state_of_the_union.txt (100%) rename {docs => data}/extras/modules/titanic.csv (100%) rename {docs => data}/extras/modules/titanic_flink.csv (100%) rename {docs => data}/extras/use_cases/question_answering/notion_db/Auto-Evaluation of Metadata Filtering.md (100%) rename {docs => data}/extras/use_cases/question_answering/notion_db/images/Untitled 1.png (100%) rename {docs => data}/extras/use_cases/question_answering/notion_db/images/Untitled 2.png (100%) rename {docs => data}/extras/use_cases/question_answering/notion_db/images/Untitled.png (100%) rename {docs => data}/extras/use_cases/question_answering/notion_db/images/ret_trim.mov (100%) rename {docs => data}/images/Appreciation code.png (100%) create mode 100644 data/images/Google agent example output.png create mode 100644 data/images/Google agent example.png create mode 100644 data/images/Langchain overview.png create mode 100644 data/images/SQL chains.png diff --git a/README.md b/README.md index 84369a1f0..b4bc4fda3 100644 --- a/README.md +++ b/README.md @@ -6,47 +6,37 @@ Java version of LangChain, while empowering LLM for Big Data. ## 1. What is this? -This is the Java language implementation of LangChain. +This is the Java language implementation of LangChain, which makes it as easy as possible to develop LLM-powered applications. +![Langchain overview.png](https://github.com/HamaWhiteGG/langchain-java/blob/dev/data/images/Langchain%20overview.png) -Large language models (LLMs) are emerging as a transformative technology, enabling developers to build applications that they previously could not. But using these LLMs in isolation is often not enough to create a truly powerful app - the real power comes when you can combine them with other sources of computation or knowledge. +The following example in the [langchain-example](langchain-examples/src/main/java/com/hw/langchain/examples). -This library is aimed at assisting in the development of those types of applications. - - -The following example can view in the [langchain-example](langchain-examples/src/main/java/com/hw/langchain/examples) - -## 2. User cases - [SQL Chains](langchain-examples/src/main/java/com/hw/langchain/examples/chains/SqlChainExample.java) - [API Chains](langchain-examples/src/main/java/com/hw/langchain/examples/chains/ApiChainExample.java) -- [QA-Milvus-Text](langchain-examples/src/main/java/com/hw/langchain/examples/chains/MilvusExample.java) -- [QA-Pinecone-Text](langchain-examples/src/main/java/com/hw/langchain/examples/chains/RetrievalQaExample.java) -- [QA-Pinecone-Markdown](langchain-examples/src/main/java/com/hw/langchain/examples/chains/RetrievalMarkdownExample.java) +- [RAG Milvus](langchain-examples/src/main/java/com/hw/langchain/examples/chains/MilvusExample.java) +- [RAG Pinecone](langchain-examples/src/main/java/com/hw/langchain/examples/chains/RetrievalQaExample.java) - [Summarization](langchain-examples/src/main/java/com/hw/langchain/examples/chains/SummarizationExample.java) -- [Agent with Google Search](langchain-examples/src/main/java/com/hw/langchain/examples/agents/LlmAgentExample.java) -- [Spark SQL AI](langchain-bigdata/langchain-spark/src/test/java/com/hw/langchain/agents/toolkits/spark/sql/toolkit/SparkSqlToolkitTest.java) -- [Flink SQL AI](langchain-bigdata/langchain-flink/src/test/java/com/hw/langchain/agents/toolkits/flink/sql/toolkit/FlinkSqlToolkitTest.java) +- [Google Search Agent](langchain-examples/src/main/java/com/hw/langchain/examples/agents/ChatAgentExample.java) +- [Spark SQL Agent](langchain-bigdata/langchain-spark/src/test/java/com/hw/langchain/agents/toolkits/spark/sql/toolkit/SparkSqlToolkitTest.java) +- [Flink SQL Agent](langchain-bigdata/langchain-flink/src/test/java/com/hw/langchain/agents/toolkits/flink/sql/toolkit/FlinkSqlToolkitTest.java) -## 3. Integrations +## 2. Integrations -### 3.1 LLMs -- [OpenAI](langchain-examples/src/main/java/com/hw/langchain/examples/llms/OpenAIExample.java), - - support [stream](langchain-examples/src/main/java/com/hw/langchain/examples/llms/StreamOpenAIExample.java) - - support [functions](openai-client/src/test/java/com/hw/openai/function/ChatFunctionTest.java) +### 2.1 LLMs +- [OpenAI](langchain-examples/src/main/java/com/hw/langchain/examples/llms/OpenAIExample.java) [[stream](langchain-examples/src/main/java/com/hw/langchain/examples/llms/StreamOpenAIExample.java)] - [Azure OpenAI](openai-client/src/test/java/com/hw/openai/AzureOpenAiClientTest.java) - [ChatGLM2](langchain-examples/src/main/java/com/hw/langchain/examples/llms/ChatGLMExample.java) - [Ollama](langchain-examples/src/main/java/com/hw/langchain/examples/llms/OllamaExample.java) -### 3.2 Vector stores +### 2.2 Vector stores - [Pinecone](langchain-examples/src/main/java/com/hw/langchain/examples/vectorstores/PineconeExample.java) - [Milvus](langchain-examples/src/main/java/com/hw/langchain/examples/chains/MilvusExample.java) -## 4. Quickstart Guide -This tutorial gives you a quick walkthrough about building an end-to-end language model application with LangChain. - +## 3. Quickstart Guide The API documentation is available at the following link: [https://hamawhitegg.github.io/langchain-java](https://hamawhitegg.github.io/langchain-java) -### 4.1 Maven Repository +### 3.1 Maven Repository Prerequisites for building: * Java 17 or later * Unix-like environment (we use Linux, Mac OS X) @@ -61,7 +51,7 @@ Prerequisites for building: ``` -### 4.2 Environment Setup +### 3.2 Environment Setup Using LangChain will usually require integrations with one or more model providers, data stores, apis, etc. For this example, we will be using OpenAI’s APIs. @@ -84,7 +74,7 @@ var llm = OpenAI.builder() .init(); ``` -### 4.3 LLMs +### 3.3 LLMs Get predictions from a language model. The basic building block of LangChain is the LLM, which takes in text and generates more text. ```java @@ -100,7 +90,7 @@ And now we can pass in text and get predictions! ```shell Feetful of Fun ``` -### 4.4 Chat models +### 3.4 Chat models Chat models are a variation on language models. While chat models use language models under the hood, the interface they expose is a bit different: rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs. @@ -129,53 +119,11 @@ println(output); J'adore la programmation. ``` -### 4.5 Prompt Templates -Most LLM applications do not pass user input directly into an LLM. Usually they will add the user input to a larger piece of text, called a prompt template, that provides additional context on the specific task at hand. - -In the previous example, the text we passed to the model contained instructions to generate a company name. For our application, it'd be great if the user only had to provide the description of a company/product, without having to worry about giving the model instructions. - -#### 4.5.1 LLMs - -With PromptTemplates this is easy! In this case our template would be very simple: - -```java -var prompt = PromptTemplate.fromTemplate("What is a good name for a company that makes {product}?"); -var output = prompt.format(Map.of("product", "colorful socks")); -println(output) -``` -```shell -What is a good name for a company that makes colorful socks? -``` - -#### 4.5.2 Chat models - -Similar to LLMs, you can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplate`s. You can use `ChatPromptTemplate`'s `formatMessages` method to generate the formatted messages. - -```java -var template = "You are a helpful assistant that translates {input_language} to {output_language}."; -var systemMessagePrompt = SystemMessagePromptTemplate.fromTemplate(template); - -var humanTemplate = "{text}"; -var humanMessagePrompt = HumanMessagePromptTemplate.fromTemplate(humanTemplate); - -var chatPrompt = ChatPromptTemplate.fromMessages(List.of(systemMessagePrompt, humanMessagePrompt)); -var output = chatPrompt.formatMessages(Map.of("input_language", "English", "output_language", "French", - "text", "I love programming.")); -println(output); -``` - -```shell -[ - SystemMessage{content='You are a helpful assistant that translates English to French.', additionalKwargs={}}, - HumanMessage{content='I love programming.', additionalKwargs={}} -] -``` - -### 4.6 Chains +### 3.5 Chains Now that we've got a model and a prompt template, we'll want to combine the two. Chains give us a way to link (or chain) together multiple primitives, like models, prompts, and other chains. -#### 4.6.1 LLMs +#### 3.5.1 LLMs The simplest and most common type of chain is an LLMChain, which passes an input first to a PromptTemplate and then to an LLM. We can construct an LLM chain from our existing model and prompt template. ```java var chain = new LLMChain(llm, prompt); @@ -185,7 +133,7 @@ println(result); ```shell Feetful of Fun ``` -#### 4.6.2 Chat models +#### 3.5.2 Chat models The `LLMChain` can be used with chat models as well: ```java var chain = new LLMChain(chat, chatPrompt); @@ -196,158 +144,68 @@ println(result); J'adore la programmation. ``` -### 4.7 Agents -Our first chain ran a pre-determined sequence of steps. To handle complex workflows, we need to be able to dynamically choose actions based on inputs. +#### 3.5.3 SQL Chains Example +LLMs make it possible to interact with SQL databases using natural language, and LangChain offers SQL Chains to build and run SQL queries based on natural language prompts. -Agents do just this: they use a language model to determine which actions to take and in what order. Agents are given access to tools, and they repeatedly choose a tool, run the tool, and observe the output until they come up with a final answer. - -Set the appropriate environment variables. -```shell -export SERPAPI_API_KEY=xxx -``` - -#### 4.7.1 LLMs +![SQL chains.png](https://github.com/HamaWhiteGG/langchain-java/blob/dev/data/images/SQL%20chains.png) +[SQL Chains](langchain-examples/src/main/java/com/hw/langchain/examples/chains/SqlChainExample.java) ```java -// The language model we're going to use to control the agent. -var llm = OpenAI.builder().temperature(0).build().init(); +var database = SQLDatabase.fromUri("jdbc:mysql://127.0.0.1:3306/demo", "xxx", "xxx"); -// The tools we'll give the Agent access to. Note that the 'llm-math' tool uses an LLM, so we need to pass that in. -var tools = loadTools(List.of("serpapi", "llm-math"), llm); - -// Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use. -var agent = initializeAgent(tools, llm, AgentType.ZERO_SHOT_REACT_DESCRIPTION); +var chain = SQLDatabaseChain.fromLLM(llm, database); +var result = chain.run("How many students are there?"); +println(result); -// Let's test it out! -agent.run("What was the high temperature in SF yesterday in Fahrenheit? What is that number raised to the .023 power?"); +result = chain.run("Who got zero score? Show me her parent's contact information."); +println(result); ``` ```shell -Thought: I need to find the temperature first, then use the calculator to raise it to the .023 power. -Action: Search -Action Input: "High temperature in SF yesterday" -Observation: High: 69.8ºf @12:30 PM Low: 55.94ºf @3:56 AM Approx. Precipitation / Rain Total: in. 1hr. - -Thought: I now need to use the calculator to raise 69.8 to the .023 power -Action: Calculator -Action Input: 69.8^.023 -Observation: Answer: 1.10257635505 - -Thought: I now know the final answer -Final Answer: 1.10257635505 -``` - -#### 4.7.2 Chat models - -Agents can also be used with chat models, you can initialize one using `AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION` as the agent type. +There are 6 students. -```java -// The language model we're going to use to control the agent. -var chat = ChatOpenAI.builder().temperature(0).build().init(); - -// The tools we'll give the Agent access to. Note that the 'llm-math' tool uses an LLM, so we need to pass that in. -var llm = OpenAI.builder().temperature(0).build().init(); -var tools = loadTools(List.of("serpapi", "llm-math"), llm); - -// Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use. -var agent = initializeAgent(tools, chat, AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION); - -// Now let's test it out! -agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?"); +The parent of the student who got zero score is Tracy and their contact information is 088124. ``` -### 4.8 Memory - -The chains and agents we've looked at so far have been stateless, but for many applications it's necessary to reference past interactions. This is clearly the case with a chatbot for example, where you want it to understand new messages in the context of past messages. - -The Memory module gives you a way to maintain application state. The base Memory interface is simple: it lets you update state given the latest run inputs and outputs and it lets you modify (or contextualize) the next input using the stored state. - -There are a number of built-in memory systems. The simplest of these is a buffer memory which just prepends the last few inputs/outputs to the current input - we will use this in the example below. - -#### 4.8.1 LLMs - -```java -var llm = OpenAI.builder().temperature(0).build().init(); -var conversation = new ConversationChain(llm); +Available Languages are as follows. -var output = conversation.run("Hi there!"); -println(output); -``` -here's what's going on under the hood +| Language | Value | +|--------------------|-------| +| English(default) | en_US | +| Portuguese(Brazil) | pt_BR | +If you want to choose other language instead english, just set environment variable on your host. If you not set, then **en-US** will be default ```shell -The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. - -Current conversation: - -Human: Hi there! -AI: - Hi there! It's nice to meet you. How can I help you today? +export USE_LANGUAGE=pt_BR ``` -Now if we run the chain again - -```java -output = conversation.run("I'm doing well! Just having a conversation with an AI."); -println(output); -``` +### 3.6 Agents +Our first chain ran a pre-determined sequence of steps. To handle complex workflows, we need to be able to dynamically choose actions based on inputs. -we'll see that the full prompt that's passed to the model contains the input and output of our first interaction, along with our latest input +Agents do just this: they use a language model to determine which actions to take and in what order. Agents are given access to tools, and they repeatedly choose a tool, run the tool, and observe the output until they come up with a final answer. +Set the appropriate environment variables. ```shell -The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know. - -Current conversation: -Human: Hi there! -AI: Hi there! It's nice to meet you. How can I help you today? -Human: I'm doing well! Just having a conversation with an AI. -AI: - That's great! It's always nice to have a conversation with someone new. What would you like to talk about? -``` - -#### 4.8.2 Chat models - -You can use Memory with chains and agents initialized with chat models. The main difference between this and Memory for LLMs is that rather than trying to condense all previous messages into a string, we can keep them as their own unique memory object. - -```java -var prompt = ChatPromptTemplate.fromMessages(List.of( - SystemMessagePromptTemplate.fromTemplate( - "The following is a friendly conversation between a human and an AI. The AI is talkative and " + - "provides lots of specific details from its context. If the AI does not know the " + - "answer to a question, it truthfully says it does not know."), - new MessagesPlaceholder("history"), - HumanMessagePromptTemplate.fromTemplate("{input}"))); - -var chat = ChatOpenAI.builder().temperature(0).build().init(); -var memory = new ConversationBufferMemory(true); -var conversation = new ConversationChain(chat, prompt, memory); - -var output = conversation.predict(Map.of("input", "Hi there!")); -println(output); +export SERPAPI_API_KEY=xxx ``` -```shell -Hello! How can I assist you today? -``` +#### 3.6.1 Google Search Agent Example +To augment OpenAI's knowledge beyond 2021 and computational abilities through the use of the Search and Calculator tools. +![Google agent example.png](https://github.com/HamaWhiteGG/langchain-java/blob/dev/data/images/Google%20agent%20example.png) +[Google Search Agent](langchain-examples/src/main/java/com/hw/langchain/examples/agents/ChatAgentExample.java) ```java -output = conversation.predict(Map.of("input", "I'm doing well! Just having a conversation with an AI.")); -println(output); -``` - -```shell -That's great to hear! I'm here to chat and answer any questions you may have. What's on your mind? -``` +// the 'llm-math' tool uses an LLM +var tools = loadTools(List.of("serpapi", "llm-math"), llm); -```java -output = conversation.predict(Map.of("input", "Tell me about yourself.")); -println(output); -``` +var agent = initializeAgent(tools, chat, AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION); +var query = "How many countries and regions participated in the 2023 Hangzhou Asian Games?" + + "What is that number raised to the .023 power?"; -```shell -Certainly! I am an AI language model developed by OpenAI called GPT-3. I have been trained on a vast amount of text data from the internet, which allows me to generate human-like responses to a wide range of queries and engage in conversations. My purpose is to assist and provide information to the best of my abilities. Is there anything specific you would like to know about me? +agent.run(query); ``` +![Google agent example output.png](https://github.com/HamaWhiteGG/langchain-java/blob/dev/data/images/Google%20agent%20example%20output.png) -## 5. Run Test Cases from Source +## 4. Run Test Cases from Source ```shell git clone https://github.com/HamaWhiteGG/langchain-java.git @@ -364,30 +222,12 @@ This project uses Spotless to format the code. If you make any modifications, pl mvn spotless:apply ``` -## 6. i18n for SQLDatabaseChain - -If you want to choose other language instead english, just set environment variable on your host. If you not set, then **en-US** will be default -```shell -export USE_LANGUAGE=pt_BR -``` - -#### 6.1 Available Languages -| Language | Value | -|--------------------|-------| -| English(default) | en_US | -| Portuguese(Brazil) | pt_BR | - -## 7. Support +## 5. Support Don’t hesitate to ask! [Open an issue](https://github.com/HamaWhiteGG/langchain-java/issues) if you find a bug in langchain-java. -## 8. Fork and Contribute -This is an active open-source project. We are always open to people who want to use the system or contribute to it. Please note that pull requests should be merged into the **dev** branch. - -Contact [me](mailto:baisongxx@gmail.com) if you are looking for implementation tasks that fit your skills. - -## 9. Reward +## 6. Reward If the project has been helpful to you, you can treat me to a cup of coffee. -Appreciation code +Appreciation code > This is a WeChat appreciation code. \ No newline at end of file diff --git a/docs/extras/modules/state_of_the_union.txt b/data/extras/modules/state_of_the_union.txt similarity index 100% rename from docs/extras/modules/state_of_the_union.txt rename to data/extras/modules/state_of_the_union.txt diff --git a/docs/extras/modules/titanic.csv b/data/extras/modules/titanic.csv similarity index 100% rename from docs/extras/modules/titanic.csv rename to data/extras/modules/titanic.csv diff --git a/docs/extras/modules/titanic_flink.csv b/data/extras/modules/titanic_flink.csv similarity index 100% rename from docs/extras/modules/titanic_flink.csv rename to data/extras/modules/titanic_flink.csv diff --git a/docs/extras/use_cases/question_answering/notion_db/Auto-Evaluation of Metadata Filtering.md b/data/extras/use_cases/question_answering/notion_db/Auto-Evaluation of 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HcmV?d00001 diff --git a/langchain-bigdata/langchain-flink/src/test/java/com/hw/langchain/agents/toolkits/flink/sql/toolkit/FlinkSqlToolkitTest.java b/langchain-bigdata/langchain-flink/src/test/java/com/hw/langchain/agents/toolkits/flink/sql/toolkit/FlinkSqlToolkitTest.java index c4e167989..f7c8041ef 100644 --- a/langchain-bigdata/langchain-flink/src/test/java/com/hw/langchain/agents/toolkits/flink/sql/toolkit/FlinkSqlToolkitTest.java +++ b/langchain-bigdata/langchain-flink/src/test/java/com/hw/langchain/agents/toolkits/flink/sql/toolkit/FlinkSqlToolkitTest.java @@ -83,7 +83,7 @@ CREATE TABLE IF NOT EXISTS titanic ( Embarked STRING ) WITH ( 'connector' = 'filesystem', - 'path' = '../../docs/extras/modules/titanic_flink.csv', + 'path' = '../../data/extras/modules/titanic_flink.csv', 'format' = 'csv' )"""); } diff --git a/langchain-bigdata/langchain-spark/src/test/java/com/hw/langchain/agents/toolkits/spark/sql/toolkit/SparkSqlToolkitTest.java b/langchain-bigdata/langchain-spark/src/test/java/com/hw/langchain/agents/toolkits/spark/sql/toolkit/SparkSqlToolkitTest.java index 8eeccaa2b..0c2371734 100644 --- a/langchain-bigdata/langchain-spark/src/test/java/com/hw/langchain/agents/toolkits/spark/sql/toolkit/SparkSqlToolkitTest.java +++ b/langchain-bigdata/langchain-spark/src/test/java/com/hw/langchain/agents/toolkits/spark/sql/toolkit/SparkSqlToolkitTest.java @@ -66,7 +66,7 @@ static void setup() { spark.sql("CREATE DATABASE IF NOT EXISTS " + SCHEMA); spark.sql("USE " + SCHEMA); - String csvFilePath = "../../docs/extras/modules/titanic.csv"; + String csvFilePath = "../../data/extras/modules/titanic.csv"; Dataset df = spark.read() .option("header", "true") .option("inferSchema", "true") diff --git a/langchain-core/src/test/java/com/hw/langchain/document/loaders/directory/DirectoryLoaderTest.java b/langchain-core/src/test/java/com/hw/langchain/document/loaders/directory/DirectoryLoaderTest.java index e73d594d3..46b0d5dca 100644 --- a/langchain-core/src/test/java/com/hw/langchain/document/loaders/directory/DirectoryLoaderTest.java +++ b/langchain-core/src/test/java/com/hw/langchain/document/loaders/directory/DirectoryLoaderTest.java @@ -33,7 +33,7 @@ class DirectoryLoaderTest { @Test void testLoad() { - String path = "../docs/extras/modules/"; + String path = "../data/extras/modules/"; var loader = DirectoryLoader.builder() .path(Path.of(path)) diff --git a/langchain-core/src/test/java/com/hw/langchain/document/loaders/helpers/HelpersTest.java b/langchain-core/src/test/java/com/hw/langchain/document/loaders/helpers/HelpersTest.java index 2837f3d77..13d6813ba 100644 --- a/langchain-core/src/test/java/com/hw/langchain/document/loaders/helpers/HelpersTest.java +++ b/langchain-core/src/test/java/com/hw/langchain/document/loaders/helpers/HelpersTest.java @@ -33,7 +33,7 @@ class HelpersTest { @Test void testDetectFileEncodings() throws IOException { - String filePath = "../docs/extras/modules/state_of_the_union.txt"; + String filePath = "../data/extras/modules/state_of_the_union.txt"; var detectedEncoding = detectFileEncodings(filePath); var expectedEncoding = new FileEncoding(UTF_8, 100, null); diff --git a/langchain-core/src/test/java/com/hw/langchain/document/loaders/notion/NotionDirectoryLoaderTest.java b/langchain-core/src/test/java/com/hw/langchain/document/loaders/notion/NotionDirectoryLoaderTest.java index feb9884b1..ee6133e1c 100644 --- a/langchain-core/src/test/java/com/hw/langchain/document/loaders/notion/NotionDirectoryLoaderTest.java +++ b/langchain-core/src/test/java/com/hw/langchain/document/loaders/notion/NotionDirectoryLoaderTest.java @@ -30,7 +30,7 @@ class NotionDirectoryLoaderTest { @Test void testLoad() { - String path = "../docs/extras/use_cases/question_answering/notion_db/"; + String path = "../data/extras/use_cases/question_answering/notion_db/"; var loader = new NotionDirectoryLoader(path); var docs = loader.load(); diff --git a/langchain-core/src/test/java/com/hw/langchain/vectorstores/pinecone/PineconeTest.java b/langchain-core/src/test/java/com/hw/langchain/vectorstores/pinecone/PineconeTest.java index bacd98ec3..8761a2361 100644 --- a/langchain-core/src/test/java/com/hw/langchain/vectorstores/pinecone/PineconeTest.java +++ b/langchain-core/src/test/java/com/hw/langchain/vectorstores/pinecone/PineconeTest.java @@ -105,7 +105,7 @@ private void awaitIndexReady() { @Test @Order(1) public void testFromDocuments() { - String filePath = "../docs/extras/modules/state_of_the_union.txt"; + String filePath = "../data/extras/modules/state_of_the_union.txt"; var loader = new TextLoader(filePath); var documents = loader.load(); var textSplitter = CharacterTextSplitter.builder().chunkSize(1000).chunkOverlap(0).build(); diff --git a/langchain-examples/src/main/java/com/hw/langchain/examples/chains/MilvusExample.java b/langchain-examples/src/main/java/com/hw/langchain/examples/chains/MilvusExample.java index 8b02b0861..8fb3b89ba 100644 --- a/langchain-examples/src/main/java/com/hw/langchain/examples/chains/MilvusExample.java +++ b/langchain-examples/src/main/java/com/hw/langchain/examples/chains/MilvusExample.java @@ -38,7 +38,7 @@ public class MilvusExample { public static void main(String[] args) { - var filePath = "docs/extras/modules/state_of_the_union.txt"; + var filePath = "data/extras/modules/state_of_the_union.txt"; var loader = new TextLoader(filePath); var documents = loader.load(); var textSplitter = CharacterTextSplitter.builder().chunkSize(1000).chunkOverlap(0).build(); diff --git a/langchain-examples/src/main/java/com/hw/langchain/examples/chains/RetrievalMarkdownExample.java b/langchain-examples/src/main/java/com/hw/langchain/examples/chains/RetrievalMarkdownExample.java index 48852b267..dc0c5375c 100644 --- a/langchain-examples/src/main/java/com/hw/langchain/examples/chains/RetrievalMarkdownExample.java +++ b/langchain-examples/src/main/java/com/hw/langchain/examples/chains/RetrievalMarkdownExample.java @@ -49,7 +49,7 @@ public class RetrievalMarkdownExample { public static void main(String[] args) { // Load Notion page as a markdown file - String path = "docs/extras/use_cases/question_answering/notion_db/"; + String path = "data/extras/use_cases/question_answering/notion_db/"; var loader = new NotionDirectoryLoader(path); var docs = loader.load(); var mdFile = docs.get(0).getPageContent(); diff --git a/langchain-examples/src/main/java/com/hw/langchain/examples/chains/RetrievalQaExample.java b/langchain-examples/src/main/java/com/hw/langchain/examples/chains/RetrievalQaExample.java index 4ecd3c1fc..6d5cec0eb 100644 --- a/langchain-examples/src/main/java/com/hw/langchain/examples/chains/RetrievalQaExample.java +++ b/langchain-examples/src/main/java/com/hw/langchain/examples/chains/RetrievalQaExample.java @@ -40,7 +40,7 @@ public class RetrievalQaExample { public static final String NAMESPACE = "txt"; public static void main(String[] args) { - var filePath = "docs/extras/modules/state_of_the_union.txt"; + var filePath = "data/extras/modules/state_of_the_union.txt"; var loader = new TextLoader(filePath); var documents = loader.load(); var textSplitter = CharacterTextSplitter.builder().chunkSize(1000).chunkOverlap(0).build();

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