docs: misc modelIO fixes (#9734)

Various improvements to the Model I/O section of the documentation

- Changed "Chat Model" to "chat model" in a few spots for internal
consistency
- Minor spelling & grammar fixes to improve readability & comprehension

docs/extras/integrations/callbacks/context.ipynb

@@ -93,7 +93,7 @@
    "metadata": {},
    "source": [
     "## Usage\n",
-    "### Using the Context callback within a Chat Model\n",
+    "### Using the Context callback within a chat model\n",
     "\n",
     "The Context callback handler can be used to directly record transcripts between users and AI assistants.\n",
     "\n",

docs/extras/integrations/providers/arangodb.mdx

@@ -11,7 +11,7 @@ pip install python-arango
 
 ## Graph QA Chain
 
-Connect your ArangoDB Database with a Chat Model to get insights on your data. 
+Connect your ArangoDB Database with a chat model to get insights on your data. 
 
 See the notebook example [here](/docs/use_cases/more/graph/graph_arangodb_qa.html).
 

docs/extras/integrations/providers/datadog.mdx

@@ -4,12 +4,12 @@
 
 Key features of the ddtrace integration for LangChain:
 - Traces: Capture LangChain requests, parameters, prompt-completions, and help visualize LangChain operations.
-- Metrics: Capture LangChain request latency, errors, and token/cost usage (for OpenAI LLMs and Chat Models).
+- Metrics: Capture LangChain request latency, errors, and token/cost usage (for OpenAI LLMs and chat models).
 - Logs: Store prompt completion data for each LangChain operation.
 - Dashboard: Combine metrics, logs, and trace data into a single plane to monitor LangChain requests.
 - Monitors: Provide alerts in response to spikes in LangChain request latency or error rate.
 
-Note: The ddtrace LangChain integration currently provides tracing for LLMs, Chat Models, Text Embedding Models, Chains, and Vectorstores.
+Note: The ddtrace LangChain integration currently provides tracing for LLMs, chat models, Text Embedding Models, Chains, and Vectorstores.
 
 ## Installation and Setup