change name to context_length

Co-authored-by: Michael Yang <mxyng@pm.me>

README.md

@@ -369,6 +369,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [Minima](https://github.com/dmayboroda/minima) (RAG with on-premises or fully local workflow)
 - [aidful-ollama-model-delete](https://github.com/AidfulAI/aidful-ollama-model-delete) (User interface for simplified model cleanup)
 - [Perplexica](https://github.com/ItzCrazyKns/Perplexica) (An AI-powered search engine & an open-source alternative to Perplexity AI)
+- [AI Toolkit for Visual Studio Code](https://aka.ms/ai-tooklit/ollama-docs) (Microsoft-official VSCode extension to chat, test, evaluate models with Ollama support, and use them in your AI applications.)
 
 ### Cloud
 
@@ -481,6 +482,7 @@ See the [API documentation](./docs/api.md) for all endpoints.
 - [GoLamify](https://github.com/prasad89/golamify)
 - [Ollama for Haskell](https://github.com/tusharad/ollama-haskell)
 - [multi-llm-ts](https://github.com/nbonamy/multi-llm-ts) (A Typescript/JavaScript library allowing access to different LLM in unified API)
+- [LlmTornado](https://github.com/lofcz/llmtornado) (C# library providing a unified interface for major FOSS & Commercial inference APIs)
 
 ### Mobile
 
@@ -539,4 +541,5 @@ See the [API documentation](./docs/api.md) for all endpoints.
 ### Observability
 
 - [OpenLIT](https://github.com/openlit/openlit) is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics.
-- [HoneyHive](https://docs.honeyhive.ai/integrations/ollama) is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production. 
+- [HoneyHive](https://docs.honeyhive.ai/integrations/ollama) is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production.
+- [Langfuse](https://langfuse.com/docs/integrations/ollama) is an open source LLM observability platform that enables teams to collaboratively monitor, evaluate and debug AI applications.

docs/gpu.md

@@ -38,7 +38,7 @@ Numeric IDs may be used, however ordering may vary, so UUIDs are more reliable.
 You can discover the UUID of your GPUs by running `nvidia-smi -L` If you want to
 ignore the GPUs and force CPU usage, use an invalid GPU ID (e.g., "-1")
 
-### Laptop Suspend Resume
+### Linux Suspend Resume
 
 On linux, after a suspend/resume cycle, sometimes Ollama will fail to discover
 your NVIDIA GPU, and fallback to running on the CPU.  You can workaround this

docs/modelfile.md

@@ -67,8 +67,6 @@ To use this:
 3. `ollama run choose-a-model-name`
 4. Start using the model!
 
-More examples are available in the [examples directory](../examples).
-
 To view the Modelfile of a given model, use the `ollama show --modelfile` command.
 
   ```bash
@@ -155,7 +153,6 @@ PARAMETER <parameter> <parametervalue>
 | temperature    | The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)                                                                                                                                     | float      | temperature 0.7      |
 | seed           | Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0)                                                                                       | int        | seed 42              |
 | stop           | Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate `stop` parameters in a modelfile.                                      | string     | stop "AI assistant:" |
-| tfs_z          | Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)                                               | float      | tfs_z 1              |
 | num_predict    | Maximum number of tokens to predict when generating text. (Default: -1, infinite generation)                                                                                                                                   | int        | num_predict 42       |
 | top_k          | Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)                                                                        | int        | top_k 40             |
 | top_p          | Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)                                                                 | float      | top_p 0.9            |

docs/openai.md

@@ -204,6 +204,45 @@ curl http://localhost:11434/v1/embeddings \
     }'
 ```
 
+## Extra arguments
+
+### Setting context length
+- `context_length` parameter can be used to set the context length for the model
+
+#### OpenAI python library 
+- OpenAI python library does not support setting context length, however this can be set for Ollama through the `extra_body` parameter
+
+```py
+completion = client.chat.completions.create(
+    model="llama3.1:8b",
+    messages=[{"role": "user", "content": "Say this is a test"}],
+    extra_body={"context_length": 4096},
+)
+```
+
+#### OpenAI JavaScript library
+- OpenAI JavaScript library does not support setting context length, however this can be set for Ollama by passing `context_length` directly with a `@ts-expect-error` as an undocumented parameter in the OpenAI JavaScript library. [See documentation here](https://github.com/openai/openai-node?tab=readme-ov-file#making-customundocumented-requests)
+
+```ts
+const chatCompletion = await openai.chat.completions.create({
+    messages: [{ role: 'user', content: 'Say this is a test' }],
+    model: 'llama3.2',
+    // @ts-expect-error context_length is an additional parameter 
+    context_length: 4096,
+})
+```
+
+#### `curl`
+```shell
+curl http://localhost:11434/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "llama3.2",
+        "messages": [{"role": "user", "content": "Say this is a test"}],
+        "context_length": 4096
+    }'
+```
+
 ## Endpoints
 
 ### `/v1/chat/completions`
@@ -213,6 +252,7 @@ curl http://localhost:11434/v1/embeddings \
 - [x] Chat completions
 - [x] Streaming
 - [x] JSON mode
+- [x] Structured outputs
 - [x] Reproducible outputs
 - [x] Vision
 - [x] Tools
@@ -339,27 +379,3 @@ curl http://localhost:11434/v1/chat/completions \
     }'
 ```
 
-### Setting the context size
-
-The OpenAI API does not have a way of setting the context size for a model. If you need to change the context size, create a `Modelfile` which looks like:
-
-```modelfile
-FROM <some model>
-PARAMETER num_ctx <context size>
-```
-
-Use the `ollama create mymodel` command to create a new model with the updated context size. Call the API with the updated model name:
-
-```shell
-curl http://localhost:11434/v1/chat/completions \
-    -H "Content-Type: application/json" \
-    -d '{
-        "model": "mymodel",
-        "messages": [
-            {
-                "role": "user",
-                "content": "Hello!"
-            }
-        ]
-    }'
-```

openai/openai.go

@@ -80,10 +80,12 @@ type StreamOptions struct {
 }
 
 type ChatCompletionRequest struct {
-	Model            string          `json:"model"`
-	Messages         []Message       `json:"messages"`
-	Stream           bool            `json:"stream"`
-	StreamOptions    *StreamOptions  `json:"stream_options"`
+	Model               string         `json:"model"`
+	Messages            []Message      `json:"messages"`
+	Stream              bool           `json:"stream"`
+	StreamOptions       *StreamOptions `json:"stream_options"`
+	MaxCompletionTokens *int           `json:"max_completion_tokens"`
+	// Deprecated: Use [ChatCompletionRequest.MaxCompletionTokens]
 	MaxTokens        *int            `json:"max_tokens"`
 	Seed             *int            `json:"seed"`
 	Stop             any             `json:"stop"`
@@ -93,6 +95,7 @@ type ChatCompletionRequest struct {
 	TopP             *float64        `json:"top_p"`
 	ResponseFormat   *ResponseFormat `json:"response_format"`
 	Tools            []api.Tool      `json:"tools"`
+	ContextLength    *int            `json:"context_length"`
 }
 
 type ChatCompletion struct {
@@ -475,8 +478,17 @@ func fromChatRequest(r ChatCompletionRequest) (*api.ChatRequest, error) {
 		options["stop"] = stops
 	}
 
+	if r.ContextLength != nil {
+		options["num_ctx"] = *r.ContextLength
+	}
+
+	// Deprecated: MaxTokens is deprecated, use MaxCompletionTokens instead
 	if r.MaxTokens != nil {
-		options["num_predict"] = *r.MaxTokens
+		r.MaxCompletionTokens = r.MaxTokens
+	}
+
+	if r.MaxCompletionTokens != nil {
+		options["num_predict"] = *r.MaxCompletionTokens
 	}
 
 	if r.Temperature != nil {
@@ -962,6 +974,7 @@ func ChatMiddleware() gin.HandlerFunc {
 			c.AbortWithStatusJSON(http.StatusBadRequest, NewError(http.StatusBadRequest, err.Error()))
 			return
 		}
+		slog.Info("num_ctx", "num_ctx", chatReq.Options["num_ctx"])
 
 		if err := json.NewEncoder(&b).Encode(chatReq); err != nil {
 			c.AbortWithStatusJSON(http.StatusInternalServerError, NewError(http.StatusInternalServerError, err.Error()))

openai/openai_test.go

@@ -7,7 +7,6 @@ import (
 	"io"
 	"net/http"
 	"net/http/httptest"
-	"reflect"
 	"strings"
 	"testing"
 	"time"
@@ -315,6 +314,42 @@ func TestChatMiddleware(t *testing.T) {
 				Stream: &True,
 			},
 		},
+		{
+			name: "chat handler with context_length",
+			body: `{
+				"model": "test-model",
+				"messages": [{"role": "user", "content": "Hello"}],
+				"context_length": 4096 
+			}`,
+			req: api.ChatRequest{
+				Model:    "test-model",
+				Messages: []api.Message{{Role: "user", Content: "Hello"}},
+				Options: map[string]any{
+					"num_ctx":     4096.0, // float because JSON doesn't distinguish between float and int
+					"temperature": 1.0,
+					"top_p":       1.0,
+				},
+				Stream: &False,
+			},
+		},
+		{
+			name: "chat handler with max_completion_tokens",
+			body: `{
+				"model": "test-model",
+				"messages": [{"role": "user", "content": "Hello"}],
+				"max_completion_tokens": 2
+			}`,
+			req: api.ChatRequest{
+				Model:    "test-model",
+				Messages: []api.Message{{Role: "user", Content: "Hello"}},
+				Options: map[string]any{
+					"num_predict": 2.0, // float because JSON doesn't distinguish between float and int
+					"temperature": 1.0,
+					"top_p":       1.0,
+				},
+				Stream: &False,
+			},
+		},
 		{
 			name: "chat handler error forwarding",
 			body: `{
@@ -359,7 +394,7 @@ func TestChatMiddleware(t *testing.T) {
 				return
 			}
 			if diff := cmp.Diff(&tc.req, capturedRequest); diff != "" {
-				t.Fatalf("requests did not match: %+v", diff)
+				t.Fatalf("requests did not match (-want +got):\n%s", diff)
 			}
 			if diff := cmp.Diff(tc.err, errResp); diff != "" {
 				t.Fatalf("errors did not match for %s:\n%s", tc.name, diff)
@@ -493,12 +528,14 @@ func TestCompletionsMiddleware(t *testing.T) {
 				}
 			}
 
-			if capturedRequest != nil && !reflect.DeepEqual(tc.req, *capturedRequest) {
-				t.Fatal("requests did not match")
+			if capturedRequest != nil {
+				if diff := cmp.Diff(tc.req, *capturedRequest); diff != "" {
+					t.Fatalf("requests did not match (-want +got):\n%s", diff)
+				}
 			}
 
-			if !reflect.DeepEqual(tc.err, errResp) {
-				t.Fatal("errors did not match")
+			if diff := cmp.Diff(tc.err, errResp); diff != "" {
+				t.Fatalf("errors did not match (-want +got):\n%s", diff)
 			}
 
 			capturedRequest = nil
@@ -577,12 +614,14 @@ func TestEmbeddingsMiddleware(t *testing.T) {
 				}
 			}
 
-			if capturedRequest != nil && !reflect.DeepEqual(tc.req, *capturedRequest) {
-				t.Fatal("requests did not match")
+			if capturedRequest != nil {
+				if diff := cmp.Diff(tc.req, *capturedRequest); diff != "" {
+					t.Fatalf("requests did not match (-want +got):\n%s", diff)
+				}
 			}
 
-			if !reflect.DeepEqual(tc.err, errResp) {
-				t.Fatal("errors did not match")
+			if diff := cmp.Diff(tc.err, errResp); diff != "" {
+				t.Fatalf("errors did not match (-want +got):\n%s", diff)
 			}
 
 			capturedRequest = nil
@@ -656,8 +695,8 @@ func TestListMiddleware(t *testing.T) {
 			t.Fatalf("failed to unmarshal actual response: %v", err)
 		}
 
-		if !reflect.DeepEqual(expected, actual) {
-			t.Errorf("responses did not match\nExpected: %+v\nActual: %+v", expected, actual)
+		if diff := cmp.Diff(expected, actual); diff != "" {
+			t.Errorf("responses did not match (-want +got):\n%s", diff)
 		}
 	}
 }
@@ -722,8 +761,8 @@ func TestRetrieveMiddleware(t *testing.T) {
 			t.Fatalf("failed to unmarshal actual response: %v", err)
 		}
 
-		if !reflect.DeepEqual(expected, actual) {
-			t.Errorf("responses did not match\nExpected: %+v\nActual: %+v", expected, actual)
+		if diff := cmp.Diff(expected, actual); diff != "" {
+			t.Errorf("responses did not match (-want +got):\n%s", diff)
 		}
 	}
 }

parser/parser_test.go

@@ -490,7 +490,6 @@ func TestParseFileParameters(t *testing.T) {
 		"top_k 1":                      {"top_k", "1"},
 		"top_p 1.0":                    {"top_p", "1.0"},
 		"min_p 0.05":                   {"min_p", "0.05"},
-		"tfs_z 1.0":                    {"tfs_z", "1.0"},
 		"typical_p 1.0":                {"typical_p", "1.0"},
 		"repeat_last_n 1":              {"repeat_last_n", "1"},
 		"temperature 1.0":              {"temperature", "1.0"},