Merge branch 'cc/1.0/standard_content' into mdrxy/openai-rfc-1-0

Author: mdrxy

docs/docs/concepts/messages.mdx

@@ -147,7 +147,7 @@ An `AIMessage` has the following attributes. The attributes which are **standard
 | `tool_calls`         | Standardized     | Tool calls associated with the message. See [tool calling](/docs/concepts/tool_calling) for details.                                                                                                                    |
 | `invalid_tool_calls` | Standardized     | Tool calls with parsing errors associated with the message. See [tool calling](/docs/concepts/tool_calling) for details.                                                                                                |
 | `usage_metadata`     | Standardized     | Usage metadata for a message, such as [token counts](/docs/concepts/tokens). See [Usage Metadata API Reference](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.ai.UsageMetadata.html). |
-| `id`                 | Standardized     | An optional unique identifier for the message, ideally provided by the provider/model that created the message.                                                                                                         |
+| `id`                 | Standardized     | An optional unique identifier for the message, ideally provided by the provider/model that created the message. See [Message IDs](#message-ids) for details.                                                                                                         |
 | `response_metadata`  | Raw              | Response metadata, e.g., response headers, logprobs, token counts.                                                                                                                                                      |
 
 #### content
@@ -243,3 +243,37 @@ At the moment, the output of the model will be in terms of LangChain messages, s
 need OpenAI format for the output as well.
 
 The [convert_to_openai_messages](https://python.langchain.com/api_reference/core/messages/langchain_core.messages.utils.convert_to_openai_messages.html) utility function can be used to convert from LangChain messages to OpenAI format.
+
+## Message IDs
+
+LangChain messages include an optional `id` field that serves as a unique identifier. Understanding when and how these IDs are assigned can be helpful for debugging, tracing, and working with message history.
+
+### When Messages Get IDs
+
+Messages receive IDs in the following scenarios:
+
+**Automatically assigned by LangChain:**
+- When generated through chat model invocation (`.invoke()`, `.stream()`, `.astream()`) with an active run manager/tracing context
+- IDs follow the format:
+    - `run-$RUN_ID` (e.g., `run-ba48f958-6402-41a5-b461-5e250a4ebd36-0`)
+    - `run-$RUN_ID-$IDX` (e.g., `run-ba48f958-6402-41a5-b461-5e250a4ebd36-1`) when there are multiple generations from a single chat model invocation.
+
+**Provider-assigned IDs (highest priority):**
+- When the model provider assigns its own ID to the message
+- These take precedence over LangChain-generated run IDs
+- Format varies by provider
+
+### When Messages Don't Get IDs
+
+Messages will **not** receive IDs in these situations:
+
+- **Manual message creation**: Messages created directly (e.g., `AIMessage(content="hello")`) without going through chat models
+- **No run manager context**: When there's no active callback/tracing infrastructure
+
+### ID Priority System
+
+LangChain follows a clear precedence system for message IDs:
+
+1. **Provider-assigned IDs** (highest priority): IDs from the model provider
+2. **LangChain run IDs** (medium priority): IDs starting with `run-`
+3. **Manual IDs** (lowest priority): IDs explicitly set by users

docs/docs/how_to/chat_models_universal_init.ipynb

@@ -159,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "321e3036-abd2-4e1f-bcc6-606efd036954",
    "metadata": {
     "execution": {
@@ -183,7 +183,7 @@
    ],
    "source": [
     "configurable_model.invoke(\n",
-    "    \"what's your name\", config={\"configurable\": {\"model\": \"claude-3-5-sonnet-20240620\"}}\n",
+    "    \"what's your name\", config={\"configurable\": {\"model\": \"claude-3-5-sonnet-latest\"}}\n",
     ")"
    ]
   },
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "6c8755ba-c001-4f5a-a497-be3f1db83244",
    "metadata": {
     "execution": {
@@ -261,7 +261,7 @@
     "    \"what's your name\",\n",
     "    config={\n",
     "        \"configurable\": {\n",
-    "            \"first_model\": \"claude-3-5-sonnet-20240620\",\n",
+    "            \"first_model\": \"claude-3-5-sonnet-latest\",\n",
     "            \"first_temperature\": 0.5,\n",
     "            \"first_max_tokens\": 100,\n",
     "        }\n",
@@ -336,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
    "id": "e57dfe9f-cd24-4e37-9ce9-ccf8daf78f89",
    "metadata": {
     "execution": {
@@ -368,14 +368,14 @@
    "source": [
     "llm_with_tools.invoke(\n",
     "    \"what's bigger in 2024 LA or NYC\",\n",
-    "    config={\"configurable\": {\"model\": \"claude-3-5-sonnet-20240620\"}},\n",
+    "    config={\"configurable\": {\"model\": \"claude-3-5-sonnet-latest\"}},\n",
     ").tool_calls"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "langchain",
+   "display_name": "langchain-monorepo",
    "language": "python",
    "name": "python3"
   },
@@ -389,7 +389,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.16"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,

docs/docs/how_to/custom_tools.ipynb

@@ -741,13 +741,13 @@
     "\n",
     "If you're using tools with agents, you will likely need an error handling strategy, so the agent can recover from the error and continue execution.\n",
     "\n",
-    "A simple strategy is to throw a `ToolException` from inside the tool and specify an error handler using `handle_tool_error`. \n",
+    "A simple strategy is to throw a `ToolException` from inside the tool and specify an error handler using `handle_tool_errors`. \n",
     "\n",
     "When the error handler is specified, the exception will be caught and the error handler will decide which output to return from the tool.\n",
     "\n",
-    "You can set `handle_tool_error` to `True`, a string value, or a function. If it's a function, the function should take a `ToolException` as a parameter and return a value.\n",
+    "You can set `handle_tool_errors` to `True`, a string value, or a function. If it's a function, the function should take a `ToolException` as a parameter and return a value.\n",
     "\n",
-    "Please note that only raising a `ToolException` won't be effective. You need to first set the `handle_tool_error` of the tool because its default value is `False`."
+    "Please note that only raising a `ToolException` won't be effective. You need to first set the `handle_tool_errors` of the tool because its default value is `False`."
    ]
   },
   {
@@ -777,7 +777,7 @@
    "id": "9d93b217-1d44-4d31-8956-db9ea680ff4f",
    "metadata": {},
    "source": [
-    "Here's an example with the default `handle_tool_error=True` behavior."
+    "Here's an example with the default `handle_tool_errors=True` behavior."
    ]
   },
   {
@@ -807,7 +807,7 @@
    "source": [
     "get_weather_tool = StructuredTool.from_function(\n",
     "    func=get_weather,\n",
-    "    handle_tool_error=True,\n",
+    "    handle_tool_errors=True,\n",
     ")\n",
     "\n",
     "get_weather_tool.invoke({\"city\": \"foobar\"})"
@@ -818,7 +818,7 @@
    "id": "f91d6dc0-3271-4adc-a155-21f2e62ffa56",
    "metadata": {},
    "source": [
-    "We can set `handle_tool_error` to a string that will always be returned."
+    "We can set `handle_tool_errors` to a string that will always be returned."
    ]
   },
   {
@@ -848,7 +848,7 @@
    "source": [
     "get_weather_tool = StructuredTool.from_function(\n",
     "    func=get_weather,\n",
-    "    handle_tool_error=\"There is no such city, but it's probably above 0K there!\",\n",
+    "    handle_tool_errors=\"There is no such city, but it's probably above 0K there!\",\n",
     ")\n",
     "\n",
     "get_weather_tool.invoke({\"city\": \"foobar\"})"
@@ -893,7 +893,7 @@
     "\n",
     "get_weather_tool = StructuredTool.from_function(\n",
     "    func=get_weather,\n",
-    "    handle_tool_error=_handle_error,\n",
+    "    handle_tool_errors=_handle_error,\n",
     ")\n",
     "\n",
     "get_weather_tool.invoke({\"city\": \"foobar\"})"

docs/docs/how_to/index.mdx

@@ -345,7 +345,7 @@ LangGraph is an extension of LangChain aimed at
 building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph.
 
 LangGraph documentation is currently hosted on a separate site.
-You can peruse [LangGraph how-to guides here](https://langchain-ai.github.io/langgraph/how-tos/).
+You can find the [LangGraph guides here](https://langchain-ai.github.io/langgraph/guides/).
 
 ## [LangSmith](https://docs.smith.langchain.com/)
 

docs/docs/how_to/tool_artifacts.ipynb

@@ -147,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "id": "74de0286-b003-4b48-9cdd-ecab435515ca",
    "metadata": {},
    "outputs": [],
@@ -157,7 +157,7 @@
     "\n",
     "from langchain_anthropic import ChatAnthropic\n",
     "\n",
-    "llm = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\", temperature=0)"
+    "llm = ChatAnthropic(model=\"claude-3-5-sonnet-latest\", temperature=0)"
    ]
   },
   {

docs/docs/how_to/tool_stream_events.ipynb

@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
     "if \"ANTHROPIC_API_KEY\" not in os.environ:\n",
     "    os.environ[\"ANTHROPIC_API_KEY\"] = getpass()\n",
     "\n",
-    "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\", temperature=0)"
+    "model = ChatAnthropic(model=\"claude-3-5-sonnet-latest\", temperature=0)"
    ]
   },
   {

docs/docs/integrations/chat/anthropic.ipynb

@@ -124,15 +124,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_anthropic import ChatAnthropic\n",
     "\n",
     "llm = ChatAnthropic(\n",
-    "    model=\"claude-3-5-sonnet-20240620\",\n",
+    "    model=\"claude-3-5-sonnet-latest\",\n",
     "    temperature=0,\n",
     "    max_tokens=1024,\n",
     "    timeout=None,\n",

docs/docs/integrations/chat/bedrock.ipynb

@@ -129,15 +129,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "cb09c344-1836-4e0c-acf8-11d13ac1dbae",
    "metadata": {},
    "outputs": [],
    "source": [
     "from langchain_aws import ChatBedrockConverse\n",
     "\n",
     "llm = ChatBedrockConverse(\n",
-    "    model_id=\"anthropic.claude-3-5-sonnet-20240620-v1:0\",\n",
+    "    model_id=\"anthropic.claude-3-5-sonnet-latest-v1:0\",\n",
     "    # region_name=...,\n",
     "    # aws_access_key_id=...,\n",
     "    # aws_secret_access_key=...,\n",

docs/docs/integrations/tools/stripe.ipynb

@@ -153,7 +153,7 @@
     "from langgraph.prebuilt import create_react_agent\n",
     "\n",
     "llm = ChatAnthropic(\n",
-    "    model=\"claude-3-5-sonnet-20240620\",\n",
+    "    model=\"claude-3-5-sonnet-latest\",\n",
     ")\n",
     "\n",
     "langgraph_agent_executor = create_react_agent(llm, stripe_agent_toolkit.get_tools())\n",

docs/docs/troubleshooting/errors/MESSAGE_COERCION_FAILURE.ipynb

@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -74,7 +74,7 @@
     "\n",
     "uncoercible_message = {\"role\": \"HumanMessage\", \"random_field\": \"random value\"}\n",
     "\n",
-    "model = ChatAnthropic(model=\"claude-3-5-sonnet-20240620\")\n",
+    "model = ChatAnthropic(model=\"claude-3-5-sonnet-latest\")\n",
     "\n",
     "model.invoke([uncoercible_message])"
    ]