docs: ollama nits (#31714)

Author: mdrxy

docs/docs/integrations/chat/ollama.ipynb

@@ -258,7 +258,7 @@
    "source": [
     "## Tool calling\n",
     "\n",
-    "We can use [tool calling](https://blog.langchain.dev/improving-core-tool-interfaces-and-docs-in-langchain/) with an LLM [that has been fine-tuned for tool use](https://ollama.com/library/llama3.1):\n",
+    "We can use [tool calling](https://blog.langchain.dev/improving-core-tool-interfaces-and-docs-in-langchain/) with an LLM [that has been fine-tuned for tool use](https://ollama.com/search?&c=tools) such as `llama3.1`:\n",
     "\n",
     "```\n",
     "ollama pull llama3.1\n",

docs/docs/integrations/providers/ollama.mdx

@@ -23,13 +23,15 @@ Ollama will start as a background service automatically, if this is disabled, ru
 ollama serve
 ```
 
-After starting ollama, run `ollama pull <model_checkpoint>` to download a model 
-from the [Ollama model library](https://ollama.ai/library).
+After starting ollama, run `ollama pull <name-of-model>` to download a model from the [Ollama model library](https://ollama.ai/library):
 
 ```bash
 ollama pull llama3.1
 ```
 
+- This will download the default tagged version of the model. Typically, the default points to the latest, smallest sized-parameter model.
+- To view all pulled (downloaded) models, use `ollama list`
+
 We're now ready to install the `langchain-ollama` partner package and run a model.
 
 ### Ollama LangChain partner package install

docs/docs/integrations/text_embedding/ollama.ipynb

@@ -55,7 +55,9 @@
       "cell_type": "markdown",
       "id": "c84fb993",
       "metadata": {},
-      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      "source": [
+        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
     },
     {
       "cell_type": "code",
@@ -108,7 +110,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 2,
       "id": "9ea7a09b",
       "metadata": {},
       "outputs": [],
@@ -127,7 +129,7 @@
       "source": [
         "## Indexing and Retrieval\n",
         "\n",
-        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
+        "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/rag/).\n",
         "\n",
         "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
       ]
@@ -139,14 +141,11 @@
       "metadata": {},
       "outputs": [
         {
-          "data": {
-            "text/plain": [
-              "'LangChain is the framework for building context-aware reasoning applications'"
-            ]
-          },
-          "execution_count": 4,
-          "metadata": {},
-          "output_type": "execute_result"
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "LangChain is the framework for building context-aware reasoning applications\n"
+          ]
         }
       ],
       "source": [
@@ -166,8 +165,8 @@
         "# Retrieve the most similar text\n",
         "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
         "\n",
-        "# show the retrieved document's content\n",
-        "retrieved_documents[0].page_content"
+        "# Show the retrieved document's content\n",
+        "print(retrieved_documents[0].page_content)"
       ]
     },
     {
@@ -252,7 +251,7 @@
   ],
   "metadata": {
     "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
+      "display_name": ".venv",
       "language": "python",
       "name": "python3"
     },
@@ -266,7 +265,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.9.6"
+      "version": "3.13.5"
     }
   },
   "nbformat": 4,

libs/partners/ollama/README.md

@@ -8,8 +8,21 @@ This package contains the LangChain integration with Ollama
 pip install -U langchain-ollama
 ```
 
-You will also need to run the Ollama server locally. 
-You can download it [here](https://ollama.com/download).
+For the package to work, you will need to install and run the Ollama server locally ([download](https://ollama.com/download)).
+
+To run integration tests (`make integration_tests`), you will need the following models installed in your Ollama server:
+
+- `llama3`
+- `llama3:latest`
+- `lamma3.1`
+- `gemma3:4b`
+- `deepseek-r1:1.5b`
+
+Install these models by running:
+
+```bash
+ollama pull <name-of-model>
+```
 
 ## Chat Models
 
@@ -34,6 +47,7 @@ embeddings.embed_query("What is the meaning of life?")
 ```
 
 ## LLMs
+
 `OllamaLLM` class exposes LLMs from Ollama.
 
 ```python

libs/partners/ollama/langchain_ollama/llms.py

@@ -28,7 +28,7 @@ class OllamaLLM(BaseLLM):
             from langchain_ollama import OllamaLLM
 
             model = OllamaLLM(model="llama3")
-            model.invoke("Come up with 10 names for a song about parrots")
+            print(model.invoke("Come up with 10 names for a song about parrots"))
     """
 
     model: str