davidmigloz · davidmigloz · May 11, 2024 · May 11, 2024
diff --git a/docs/modules/model_io/models/chat_models/integrations/ollama.md b/docs/modules/model_io/models/chat_models/integrations/ollama.md
@@ -2,7 +2,7 @@
 
 Wrapper around [Ollama](https://ollama.ai) Completions API that enables to interact with the LLMs in a chat-like fashion.
 
-Ollama allows you to run open-source large language models, such as Llama 2, locally.
+Ollama allows you to run open-source large language models, such as Llama 3, locally.
 
 Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile.
 
@@ -16,22 +16,19 @@ Follow [these instructions](https://github.com/jmorganca/ollama) to set up and r
 
 1. Download and install [Ollama](https://ollama.ai)
 2. Fetch a model via `ollama pull <model family>`
-  * e.g., for `Llama-7b`: `ollama pull llama2`
+  * e.g., for Llama 3: `ollama pull llama3`
 
 ## Usage
 
 ```dart
 final promptTemplate = ChatPromptTemplate.fromTemplates([
-  (
-  ChatMessageType.system,
-  'You are a helpful assistant that translates {input_language} to {output_language}.',
-  ),
+  (ChatMessageType.system, 'You are a helpful assistant that translates {input_language} to {output_language}.'),
   (ChatMessageType.human, '{text}'),
 ]);
 
 final chatModel = ChatOllama(
   defaultOptions: ChatOllamaOptions(
-    model: 'llama2',
+    model: 'llama3',
     temperature: 0,
   ),
 );
@@ -51,16 +48,12 @@ print(res);
 
 ```dart
 final promptTemplate = ChatPromptTemplate.fromTemplates([
-  (
-  ChatMessageType.system,
-  'You are a helpful assistant that replies only with numbers '
-      'in order without any spaces or commas',
-  ),
-  (ChatMessageType.human,    'List the numbers from 1 to {max_num}'),
+  (ChatMessageType.system, 'You are a helpful assistant that replies only with numbers in order without any spaces or commas'),
+  (ChatMessageType.human, 'List the numbers from 1 to {max_num}'),
 ]);
 final chat = ChatOllama(
   defaultOptions: ChatOllamaOptions(
-    model: 'llama2',
+    model: 'llama3',
     temperature: 0,
   ),
 );
@@ -77,28 +70,31 @@ await stream.forEach(print);
 
 ## JSON mode
 
-You can enforce the model to produce a JSON output, useful for extracting structured data.
+You can force the model to produce JSON output that you can easily parse using `JsonOutputParser`, useful for extracting structured data.
 
 ```dart
 final promptTemplate = ChatPromptTemplate.fromTemplates(const [
-  (ChatMessageType.system, 'Respond using JSON'),
+  (ChatMessageType.system, 'You are an assistant that respond question using JSON format.'),
   (ChatMessageType.human, '{question}'),
 ]);
 final chat = ChatOllama(
-  defaultOptions: const ChatOllamaOptions(
-    model: 'llama2',
+  defaultOptions: ChatOllamaOptions(
+    model: 'llama3',
     temperature: 0,
     format: OllamaResponseFormat.json,
   ),
 );
 
-final chain = promptTemplate.pipe(chat);
+final chain = Runnable.getMapFromInput<String>('question')
+    .pipe(promptTemplate)
+    .pipe(chat)
+    .pipe(JsonOutputParser());
 
 final res = await chain.invoke(
-  {'question': 'What color is the sky at different times of the day?'},
+  'What is the population of Spain, The Netherlands, and France?',
 );
-print(res.output.content);
-// {"morning": {"sky": "pink", "sun": "rise"}, "daytime": {"sky": "blue", "sun": "high"}, "afternoon": ...}
+print(res);
+// {Spain: 46735727, The Netherlands: 17398435, France: 65273538}
 ```
 
 ## Multimodal support
@@ -109,7 +105,7 @@ You can provide several base64-encoded `png` or `jpeg` images. Images up to 100M
 
 ```dart
 final chatModel = ChatOllama(
-  defaultOptions: const ChatOllamaOptions(
+  defaultOptions: ChatOllamaOptions(
     model: 'llava',
     temperature: 0,
   ),
@@ -136,7 +132,7 @@ We can easily create a fully local RAG pipeline using `OllamaEmbeddings` and `Ch
 ```dart
 // 1. Create a vector store and add documents to it
 final vectorStore = MemoryVectorStore(
-  embeddings: OllamaEmbeddings(model: 'llama2'),
+  embeddings: OllamaEmbeddings(model: 'llama3'),
 );
 await vectorStore.addDocuments(
   documents: [
@@ -153,7 +149,7 @@ final promptTemplate = ChatPromptTemplate.fromTemplates([
 
 // 3. Define the model to use and the vector store retriever
 final chatModel = ChatOllama(
-  defaultOptions: ChatOllamaOptions(model: 'llama2'),
+  defaultOptions: ChatOllamaOptions(model: 'llama3'),
 );
 final retriever = vectorStore.asRetriever(
   defaultOptions: VectorStoreRetrieverOptions(

diff --git a/docs/modules/model_io/models/llms/integrations/ollama.md b/docs/modules/model_io/models/llms/integrations/ollama.md
@@ -2,7 +2,7 @@
 
 Wrapper around [Ollama](https://ollama.ai) Completions API.
 
-Ollama allows you to run open-source large language models, such as Llama 2, locally.
+Ollama allows you to run open-source large language models, such as Llama 3, locally.
 
 Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile.
 
@@ -12,11 +12,11 @@ For a complete list of supported models and model variants, see the [Ollama mode
 
 ## Setup
 
-Rollow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:
+Follow [these instructions](https://github.com/jmorganca/ollama) to set up and run a local Ollama instance:
 
 1. Download and install [Ollama](https://ollama.ai)
 2. Fetch a model via `ollama pull <model family>`
-  * e.g., for `Llama-7b`: `ollama pull llama2`
+  * e.g., for Llama 3: `ollama pull llama3`
 
 ## Usage
 
@@ -25,8 +25,8 @@ final prompt = PromptTemplate.fromTemplate(
   'What is a good name for a company that makes {product}?',
 );
 final llm = Ollama(
-  defaultOptions: const OllamaOptions(
-    model: 'llama2',
+  defaultOptions: OllamaOptions(
+    model: 'llama3',
   ),
 );
 final chain = prompt | llm | StringOutputParser();
@@ -42,12 +42,11 @@ final promptTemplate = PromptTemplate.fromTemplate(
   'List the numbers from 1 to {max_num} in order without any spaces or commas',
 );
 final llm = Ollama(
-  defaultOptions: const OllamaOptions(
-    model: 'llama2',
+  defaultOptions: OllamaOptions(
+    model: 'llama3',
   ),
 );
-const stringOutputParser = StringOutputParser<LLMResult>();
-final chain = promptTemplate | llm | stringOutputParser;
+final chain = promptTemplate | llm | StringOutputParser();
 final stream = chain.stream({'max_num': '9'});
 await stream.forEach(print);
 // 1

diff --git a/docs/modules/retrieval/text_embedding/integrations/ollama.md b/docs/modules/retrieval/text_embedding/integrations/ollama.md
@@ -1,7 +1,7 @@
 # OllamaEmbeddings
 
 ```dart
-final embeddings = OllamaEmbeddings(model: 'llama2');
+final embeddings = OllamaEmbeddings(model: 'llama3');
 const text = 'This is a test document.';
 final res = await embeddings.embedQuery(text);
 final res = await embeddings.embedDocuments([text]);

diff --git a/examples/docs_examples/bin/modules/model_io/models/chat_models/integrations/ollama.dart b/examples/docs_examples/bin/modules/model_io/models/chat_models/integrations/ollama.dart
@@ -24,7 +24,7 @@ Future<void> _chatOllama() async {
 
   final chatModel = ChatOllama(
     defaultOptions: const ChatOllamaOptions(
-      model: 'llama2',
+      model: 'llama3',
       temperature: 0,
     ),
   );
@@ -51,7 +51,7 @@ Future<void> _chatOllamaStreaming() async {
   ]);
   final chat = ChatOllama(
     defaultOptions: const ChatOllamaOptions(
-      model: 'llama2',
+      model: 'llama3',
       temperature: 0,
     ),
   );
@@ -68,24 +68,30 @@ Future<void> _chatOllamaStreaming() async {
 
 Future<void> _chatOllamaJsonMode() async {
   final promptTemplate = ChatPromptTemplate.fromTemplates(const [
-    (ChatMessageType.system, 'Respond using JSON'),
+    (
+      ChatMessageType.system,
+      'You are an assistant that respond question using JSON format.'
+    ),
     (ChatMessageType.human, '{question}'),
   ]);
   final chat = ChatOllama(
     defaultOptions: const ChatOllamaOptions(
-      model: 'llama2',
+      model: 'llama3',
       temperature: 0,
       format: OllamaResponseFormat.json,
     ),
   );
 
-  final chain = promptTemplate.pipe(chat);
+  final chain = Runnable.getMapFromInput<String>('question')
+      .pipe(promptTemplate)
+      .pipe(chat)
+      .pipe(JsonOutputParser());
 
   final res = await chain.invoke(
-    {'question': 'What color is the sky at different times of the day?'},
+    'What is the population of Spain, The Netherlands, and France?',
   );
-  print(res.output.content);
-  // {"morning": {"sky": "pink", "sun": "rise"}, "daytime": {"sky": "blue", "sun": "high"}, "afternoon": ...}
+  print(res);
+  // {Spain: 46735727, The Netherlands: 17398435, France: 65273538}
 }
 
 Future<void> _chatOllamaMultimodal() async {
@@ -113,7 +119,7 @@ Future<void> _chatOllamaMultimodal() async {
 Future<void> _rag() async {
   // 1. Create a vector store and add documents to it
   final vectorStore = MemoryVectorStore(
-    embeddings: OllamaEmbeddings(model: 'llama2'),
+    embeddings: OllamaEmbeddings(model: 'llama3'),
   );
   await vectorStore.addDocuments(
     documents: [
@@ -135,7 +141,7 @@ Future<void> _rag() async {
 
   // 3. Define the model to use and the vector store retriever
   final chatModel = ChatOllama(
-    defaultOptions: const ChatOllamaOptions(model: 'llama2'),
+    defaultOptions: const ChatOllamaOptions(model: 'llama3'),
   );
   final retriever = vectorStore.asRetriever(
     defaultOptions: const VectorStoreRetrieverOptions(

diff --git a/examples/docs_examples/bin/modules/model_io/models/llms/integrations/ollama.dart b/examples/docs_examples/bin/modules/model_io/models/llms/integrations/ollama.dart
@@ -13,7 +13,7 @@ Future<void> _ollama() async {
   );
   final llm = Ollama(
     defaultOptions: const OllamaOptions(
-      model: 'llama2',
+      model: 'llama3',
     ),
   );
 
@@ -29,7 +29,7 @@ Future<void> _ollamaStreaming() async {
   );
   final llm = Ollama(
     defaultOptions: const OllamaOptions(
-      model: 'llama2',
+      model: 'llama3',
     ),
   );
   const stringOutputParser = StringOutputParser<LLMResult>();

diff --git a/packages/langchain/README.md b/packages/langchain/README.md
@@ -51,18 +51,18 @@ LangChain.dart has a modular design that allows developers to import only the co
     <img src="https://raw.githubusercontent.com/davidmigloz/langchain_dart/main/docs/img/langchain_packages.png" width="500">
 </p>
 
-| Package                                                             | Version                                                                                                                      | Description                                                                                                                                                 |
-|---------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| [langchain_core](https://pub.dev/packages/langchain_core)           | [![langchain_core](https://img.shields.io/pub/v/langchain_core.svg)](https://pub.dev/packages/langchain_core)                | Core abstractions and LCEL                                                                                                                                  |
-| [langchain](https://pub.dev/packages/langchain)                     | [![langchain](https://img.shields.io/pub/v/langchain.svg)](https://pub.dev/packages/langchain)                               | Higher-level and use-case specific chains, agents, and retrieval algorithms                                                                                 |
-| [langchain_community](https://pub.dev/packages/langchain_community) | [![langchain_community](https://img.shields.io/pub/v/langchain_community.svg)](https://pub.dev/packages/langchain_community) | Third-party integrations (without specific packages) and community-contributed components                                                                   |
-| [langchain_openai](https://pub.dev/packages/langchain_openai)       | [![langchain_openai](https://img.shields.io/pub/v/langchain_openai.svg)](https://pub.dev/packages/langchain_openai)          | OpenAI integration (GPT-3.5, GPT-4, Embeddings, Functions, Vision, DALL·E 3, etc.) and OpenAI Compatible services (Together AI, Anyscale, OpenRouter, etc.) |
-| [langchain_google](https://pub.dev/packages/langchain_google)       | [![langchain_google](https://img.shields.io/pub/v/langchain_google.svg)](https://pub.dev/packages/langchain_google)          | Google integration (GoogleAI, VertexAI, Gemini, PaLM 2, Embeddings, Vector Search, etc.)                                                                    |
-| [langchain_ollama](https://pub.dev/packages/langchain_ollama)       | [![langchain_ollama](https://img.shields.io/pub/v/langchain_ollama.svg)](https://pub.dev/packages/langchain_ollama)          | Ollama integration (Llama 2, Code Llama, Mistral, LLaVA, Phi, Vicuna, Orca, Starling, etc.)                                                                 |
-| [langchain_mistralai](https://pub.dev/packages/langchain_mistralai) | [![langchain_mistralai](https://img.shields.io/pub/v/langchain_mistralai.svg)](https://pub.dev/packages/langchain_mistralai) | Mistral AI integration (Mistral-7B, Mixtral 8x7B, embeddings, etc.).                                                                                        |
-| [langchain_pinecone](https://pub.dev/packages/langchain_pinecone)   | [![langchain_pinecone](https://img.shields.io/pub/v/langchain_pinecone.svg)](https://pub.dev/packages/langchain_pinecone)    | Pinecone vector database integration                                                                                                                        |
-| [langchain_chroma](https://pub.dev/packages/langchain_chroma)       | [![langchain_chroma](https://img.shields.io/pub/v/langchain_chroma.svg)](https://pub.dev/packages/langchain_chroma)          | Chroma vector database integration                                                                                                                          |
-| [langchain_supabase](https://pub.dev/packages/langchain_supabase)   | [![langchain_supabase](https://img.shields.io/pub/v/langchain_supabase.svg)](https://pub.dev/packages/langchain_supabase)    | Supabase Vector database integration                                                                                                                        |
+| Package                                                             | Version                                                                                                                      | Description                                                                                                                                                                                                  |
+|---------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| [langchain_core](https://pub.dev/packages/langchain_core)           | [![langchain_core](https://img.shields.io/pub/v/langchain_core.svg)](https://pub.dev/packages/langchain_core)                | Core abstractions and LCEL                                                                                                                                                                                   |
+| [langchain](https://pub.dev/packages/langchain)                     | [![langchain](https://img.shields.io/pub/v/langchain.svg)](https://pub.dev/packages/langchain)                               | Higher-level and use-case specific chains, agents, and retrieval algorithms                                                                                                                                  |
+| [langchain_community](https://pub.dev/packages/langchain_community) | [![langchain_community](https://img.shields.io/pub/v/langchain_community.svg)](https://pub.dev/packages/langchain_community) | Third-party integrations (without specific packages) and community-contributed components                                                                                                                    |
+| [langchain_openai](https://pub.dev/packages/langchain_openai)       | [![langchain_openai](https://img.shields.io/pub/v/langchain_openai.svg)](https://pub.dev/packages/langchain_openai)          | OpenAI integration (GPT-3.5 Turbo, GPT-4, GPT-4 Turbo, Embeddings, Tools, Vision, DALL·E 3, etc.) and OpenAI Compatible services (TogetherAI, Anyscale, OpenRouter, One API, Groq, Llamafile, GPT4All, etc.) |
+| [langchain_google](https://pub.dev/packages/langchain_google)       | [![langchain_google](https://img.shields.io/pub/v/langchain_google.svg)](https://pub.dev/packages/langchain_google)          | Google integration (GoogleAI, VertexAI, Gemini, PaLM 2, Embeddings, Vector Search, etc.)                                                                                                                     |
+| [langchain_ollama](https://pub.dev/packages/langchain_ollama)       | [![langchain_ollama](https://img.shields.io/pub/v/langchain_ollama.svg)](https://pub.dev/packages/langchain_ollama)          | Ollama integration (Llama 3, Phi-3, WizardLM-2, Mistral 7B, Gemma, CodeGemma, Command R, LLaVA, DBRX, Qwen 1.5, Dolphin, DeepSeek Coder, Vicuna, Orca, etc.)                                                 |
+| [langchain_mistralai](https://pub.dev/packages/langchain_mistralai) | [![langchain_mistralai](https://img.shields.io/pub/v/langchain_mistralai.svg)](https://pub.dev/packages/langchain_mistralai) | Mistral AI integration (Mistral-7B, Mixtral 8x7B, Mixtral 8x22B, Mistral Small, Mistral Large, embeddings, etc.).                                                                                            |
+| [langchain_pinecone](https://pub.dev/packages/langchain_pinecone)   | [![langchain_pinecone](https://img.shields.io/pub/v/langchain_pinecone.svg)](https://pub.dev/packages/langchain_pinecone)    | Pinecone vector database integration                                                                                                                                                                         |
+| [langchain_chroma](https://pub.dev/packages/langchain_chroma)       | [![langchain_chroma](https://img.shields.io/pub/v/langchain_chroma.svg)](https://pub.dev/packages/langchain_chroma)          | Chroma vector database integration                                                                                                                                                                           |
+| [langchain_supabase](https://pub.dev/packages/langchain_supabase)   | [![langchain_supabase](https://img.shields.io/pub/v/langchain_supabase.svg)](https://pub.dev/packages/langchain_supabase)    | Supabase Vector database integration                                                                                                                                                                         |
 
 Functionality provided by each integration package:
 

diff --git a/packages/langchain_ollama/lib/langchain_ollama.dart b/packages/langchain_ollama/lib/langchain_ollama.dart
@@ -1,4 +1,4 @@
-/// LangChain.dart integration module for Ollama (run Llama 2, Mistral, Vicuna and other models locally).
+/// LangChain.dart integration module for Ollama (run Llama 3, Mistral, Vicuna and other models locally).
 library;
 
 export 'src/chat_models/chat_models.dart';