LCEL nits (#13155)

docs/docs/expression_language/how_to/configure.ipynb

@@ -5,7 +5,7 @@
    "id": "39eaf61b",
    "metadata": {},
    "source": [
-    "# Configuration\n",
+    "# Configure chain internals at runtime\n",
     "\n",
     "Oftentimes you may want to experiment with, or even expose to the end user, multiple different ways of doing things.\n",
     "In order to make this experience as easy as possible, we have defined two methods.\n",
@@ -594,7 +594,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/functions.ipynb

@@ -5,7 +5,7 @@
    "id": "fbc4bf6e",
    "metadata": {},
    "source": [
-    "# Run arbitrary functions\n",
+    "# Run custom functions\n",
     "\n",
     "You can use arbitrary functions in the pipeline\n",
     "\n",
@@ -175,7 +175,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/generators.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Custom generator functions\n",
+    "# Stream custom generator functions\n",
     "\n",
     "You can use generator functions (ie. functions that use the `yield` keyword, and behave like iterators) in a LCEL pipeline.\n",
     "\n",
@@ -21,15 +21,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "lion, tiger, wolf, gorilla, panda\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from typing import Iterator, List\n",
     "\n",
@@ -43,16 +35,51 @@
     ")\n",
     "model = ChatOpenAI(temperature=0.0)\n",
     "\n",
-    "\n",
-    "str_chain = prompt | model | StrOutputParser()\n",
-    "\n",
-    "print(str_chain.invoke({\"animal\": \"bear\"}))"
+    "str_chain = prompt | model | StrOutputParser()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lion, tiger, wolf, gorilla, panda"
+     ]
+    }
+   ],
+   "source": [
+    "for chunk in str_chain.stream({\"animal\": \"bear\"}):\n",
+    "    print(chunk, end=\"\", flush=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'lion, tiger, wolf, gorilla, panda'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str_chain.invoke({\"animal\": \"bear\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [],
    "source": [
     "# This is a custom parser that splits an iterator of llm tokens\n",
@@ -77,22 +104,61 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_chain = str_chain | split_into_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['lion', 'tiger', 'wolf', 'gorilla', 'panda']\n"
+      "['lion']\n",
+      "['tiger']\n",
+      "['wolf']\n",
+      "['gorilla']\n",
+      "['panda']\n"
      ]
     }
    ],
    "source": [
-    "list_chain = str_chain | split_into_list\n",
-    "\n",
-    "print(list_chain.invoke({\"animal\": \"bear\"}))"
+    "for chunk in list_chain.stream({\"animal\": \"bear\"}):\n",
+    "    print(chunk, flush=True)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['lion', 'tiger', 'wolf', 'gorilla', 'panda']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list_chain.invoke({\"animal\": \"bear\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -111,9 +177,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }

docs/docs/expression_language/how_to/map.ipynb

@@ -5,7 +5,7 @@
    "id": "b022ab74-794d-4c54-ad47-ff9549ddb9d2",
    "metadata": {},
    "source": [
-    "# Use RunnableParallel/RunnableMap\n",
+    "# Parallelize steps\n",
     "\n",
     "RunnableParallel (aka. RunnableMap) makes it easy to execute multiple Runnables in parallel, and to return the output of these Runnables as a map."
    ]
@@ -195,7 +195,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.1"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,

docs/docs/expression_language/how_to/routing.ipynb

@@ -5,7 +5,7 @@
    "id": "4b47436a",
    "metadata": {},
    "source": [
-    "# Route between multiple Runnables\n",
+    "# Dynamically route logic based on input\n",
     "\n",
     "This notebook covers how to do routing in the LangChain Expression Language.\n",
     "\n",

docs/docs/expression_language/interface.ipynb

@@ -8,7 +8,7 @@
     "---\n",
     "sidebar_position: 0\n",
     "title: Interface\n",
-    "---\n"
+    "---"
    ]
   },
   {
@@ -31,26 +31,17 @@
     "- [`abatch`](#async-batch): call the chain on a list of inputs async\n",
     "- [`astream_log`](#async-stream-intermediate-steps): stream back intermediate steps as they happen, in addition to the final response\n",
     "\n",
-    "The **input type** varies by component:\n",
+    "The **input type** and **output type** varies by component:\n",
     "\n",
-    "| Component | Input Type |\n",
-    "| --- | --- |\n",
-    "|Prompt|Dictionary|\n",
-    "|Retriever|Single string|\n",
-    "|LLM, ChatModel| Single string, list of chat messages or a PromptValue|\n",
-    "|Tool|Single string, or dictionary, depending on the tool|\n",
-    "|OutputParser|The output of an LLM or ChatModel|\n",
+    "| Component | Input Type | Output Type |\n",
+    "| --- | --- | --- |\n",
+    "| Prompt | Dictionary | PromptValue |\n",
+    "| ChatModel | Single string, list of chat messages or a PromptValue | ChatMessage |\n",
+    "| LLM | Single string, list of chat messages or a PromptValue | String |\n",
+    "| OutputParser | The output of an LLM or ChatModel | Depends on the parser |\n",
+    "| Retriever | Single string | List of Documents |\n",
+    "| Tool | Single string or dictionary, depending on the tool | Depends on the tool |\n",
     "\n",
-    "The **output type** also varies by component:\n",
-    "\n",
-    "| Component | Output Type |\n",
-    "| --- | --- |\n",
-    "| LLM | String |\n",
-    "| ChatModel | ChatMessage |\n",
-    "| Prompt | PromptValue |\n",
-    "| Retriever | List of documents |\n",
-    "| Tool | Depends on the tool |\n",
-    "| OutputParser | Depends on the parser |\n",
     "\n",
     "All runnables expose input and output **schemas** to inspect the inputs and outputs:\n",
     "- [`input_schema`](#input-schema): an input Pydantic model auto-generated from the structure of the Runnable\n",
@@ -1161,7 +1152,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,