[Go] weave models.md

docs-go/generate.sh

@@ -6,6 +6,7 @@ if [[ ! -f $weave ]]; then
   go -C ../go install ./internal/cmd/weave
 fi
 
-$weave flows > flows.md
-
+for file in flows models; do
+  $weave $file > $file.md
+done
 

docs-go/models

@@ -0,0 +1,159 @@
+# Generating content
+
+Firebase Genkit provides an easy interface for generating content with LLMs.
+
+## Models
+
+Models in Firebase Genkit are libraries and abstractions that provide access to
+various Google and non-Google LLMs.
+
+Models are fully instrumented for observability and come with tooling
+integrations provided by the Genkit Developer UI -- you can try any model using
+the model runner.
+
+When working with models in Genkit, you first need to configure the model you
+want to work with. Model configuration is performed by the plugin system. In
+this example you are configuring the Vertex AI plugin, which provides Gemini
+models.
+
+- {Go}
+
+  %include ../go/internal/doc-snippets/models.go import
+
+  %include ../go/internal/doc-snippets/models.go init
+
+Note: Different plugins and models use different methods of
+authentication. For example, Vertex API uses the Google Auth Library so it can
+pull required credentials using Application Default Credentials.
+
+To use models provided by the plugin, you need a reference to the specific model
+and version:
+
+- {Go}
+
+  %include ../go/internal/doc-snippets/models.go model
+
+## Supported models
+
+Genkit provides model support through its plugin system. The following plugins
+are officially supported:
+
+| Plugin                    | Models                                                                   |
+| ------------------------- | ------------------------------------------------------------------------ |
+| [Google Generative AI][1] | Gemini Pro, Gemini Pro Vision                                            |
+| [Google Vertex AI][2]     | Gemini Pro, Gemini Pro Vision, Gemini 1.5 Flash, Gemini 1.5 Pro, Imagen2 |
+| [Ollama][3]               | Many local models, including Gemma, Llama 2, Mistral, and more           |
+
+[1]: plugins/google-genai.md
+[2]: plugins/vertex-ai.md
+[3]: plugins/ollama.md
+
+See the docs for each plugin for setup and usage information.
+
+<!-- TODO: There's also a wide variety of community supported models available
+you can discover by ... -->
+
+## How to generate content
+
+Genkit provides a simple helper function for generating content with models.
+
+To just call the model:
+
+- {Go}
+
+  %include ../go/internal/doc-snippets/models.go call
+
+You can pass options along with the model call. The options that are supported
+depend on the model and its API.
+
+- {Go}
+
+  %include ../go/internal/doc-snippets/models.go options
+
+### Streaming responses
+
+Genkit supports chunked streaming of model responses:
+
+- {Go}
+
+  To use chunked streaming, pass a callback function to `Generate()`:
+
+  %include ../go/internal/doc-snippets/models.go streaming
+
+## Multimodal input
+
+If the model supports multimodal input, you can pass image prompts:
+
+- {Go}
+
+  %include ../go/internal/doc-snippets/models.go multimodal
+
+  <!-- TODO: gs:// wasn't working for me. HTTP? -->
+
+The exact format of the image prompt (`https` URL, `gs` URL, `data` URI) is
+model-dependent.
+
+## Function calling (tools)
+
+Genkit models provide an interface for function calling, for models that support
+it.
+
+- {Go}
+
+  %include ../go/internal/doc-snippets/models.go tools
+
+This will automatically call the tools in order to fulfill the user prompt.
+
+<!-- TODO: returnToolRequests: true` -->
+
+<!--
+
+### Adding retriever context
+
+Documents from a retriever can be passed directly to `generate` to provide
+grounding context:
+
+```javascript
+const docs = await companyPolicyRetriever({ query: question });
+
+await generate({
+  model: geminiPro,
+  prompt: `Answer using the available context from company policy: ${question}`,
+
+  context: docs,
+});
+```
+
+The document context is automatically appended to the content of the prompt
+sent to the model.
+
+-->
+
+### Recording message history
+
+Genkit models support maintaining a history of the messages sent to the model
+and its responses, which you can use to build interactive experiences, such as
+chatbots.
+
+- {Go}
+
+  In the first prompt of a session, the "history" is simply the user prompt:
+
+  %include ../go/internal/doc-snippets/models.go hist1
+
+  When you get a response, add it to the history:
+
+  %include ../go/internal/doc-snippets/models.go hist2
+
+  You can serialize this history and persist it in a database or session storage.
+  For subsequent user prompts, add them to the history before calling
+  `Generate()`:
+
+  %include ../go/internal/doc-snippets/models.go hist3
+
+If the model you're using supports the system role, you can use the initial
+history to set the system message:
+
+- {Go}
+
+  %include ../go/internal/doc-snippets/models.go hist4