Update expression language docs, add RunnableMap.from method (langcha…

…in-ai#2806)

docs/docs_skeleton/docs/expression_language/index.mdx

@@ -13,7 +13,3 @@ The base interface shared by all LCEL objects
 
 #### [Cookbook](/docs/expression_language/cookbook)
 Examples of common LCEL usage patterns
-
-You can also peruse this high-level overview/cheatsheet made by [@zhanghaili0610](https://twitter.com/zhanghaili0610):
-
-![](/img/langchain-js-runnable-cheatsheet.png)

docs/docs_skeleton/sidebars.js

@@ -35,23 +35,23 @@
     },
     {
       type: "category",
-      label: "Modules",
+      label: "LangChain Expression Language",
       collapsed: false,
-      collapsible: false,
-      items: [{ type: "autogenerated", dirName: "modules" } ],
+      items: [{ type: "autogenerated", dirName: "expression_language" } ],
       link: {
         type: 'doc',
-        id: "modules/index"
+        id: "expression_language/index"
       },
     },
     {
       type: "category",
-      label: "LangChain Expression Language",
+      label: "Modules",
       collapsed: false,
-      items: [{ type: "autogenerated", dirName: "expression_language" } ],
+      collapsible: false,
+      items: [{ type: "autogenerated", dirName: "modules" } ],
       link: {
         type: 'doc',
-        id: "expression_language/index"
+        id: "modules/index"
       },
     },
     {

docs/extras/additional_resources/expression_language_cheatsheet.mdx

@@ -0,0 +1,5 @@
+# LangChain Expression Language Cheatsheet
+
+For a quick reference for LangChain Expression Language overview/cheatsheet made by [@zhanghaili0610](https://twitter.com/zhanghaili0610):
+
+![](/img/langchain-js-runnable-cheatsheet.png)

docs/extras/expression_language/how_to/map.mdx

@@ -0,0 +1,22 @@
+# Use RunnableMaps
+
+RunnableMaps allow you to execute multiple Runnables in parallel, and to return the output of these Runnables as a map.
+
+import CodeBlock from "@theme/CodeBlock";
+import BasicExample from "@examples/guides/expression_language/runnable_maps_basic.ts";
+
+<CodeBlock language="typescript">{BasicExample}</CodeBlock>
+
+## Manipulating outputs/inputs
+
+Maps can be useful for manipulating the output of one Runnable to match the input format of the next Runnable in a sequence.
+
+Note below that the object within the `RunnableSequence.from()` call is automatically coerced into a runnable map. All keys of the object must 
+have values that are runnables or can be themselves coerced to runnables (functions to `RunnableLambda`s or objects to `RunnableMap`s).
+This coercion will also occur when composing chains via the `.pipe()` method.
+
+import SequenceExample from "@examples/guides/expression_language/runnable_maps_sequence.ts";
+
+<CodeBlock language="typescript">{SequenceExample}</CodeBlock>
+
+Here the input to prompt is expected to be a map with keys "context" and "question". The user input is just the question. So we need to get the context using our retriever and passthrough the user input under the "question" key.

examples/src/guides/expression_language/runnable_maps_basic.ts

@@ -0,0 +1,37 @@
+import { ChatAnthropic } from "langchain/chat_models/anthropic";
+import { PromptTemplate } from "langchain/prompts";
+import { RunnableMap } from "langchain/schema/runnable";
+
+const model = new ChatAnthropic({});
+const jokeChain = PromptTemplate.fromTemplate(
+  "Tell me a joke about {topic}"
+).pipe(model);
+const poemChain = PromptTemplate.fromTemplate(
+  "write a 2-line poem about {topic}"
+).pipe(model);
+
+const mapChain = RunnableMap.from({
+  joke: jokeChain,
+  poem: poemChain,
+});
+
+const result = await mapChain.invoke({ topic: "bear" });
+console.log(result);
+/*
+  {
+    joke: AIMessage {
+      content: " Here's a silly joke about a bear:\n" +
+        '\n' +
+        'What do you call a bear with no teeth?\n' +
+        'A gummy bear!',
+      additional_kwargs: {}
+    },
+    poem: AIMessage {
+      content: ' Here is a 2-line poem about a bear:\n' +
+        '\n' +
+        'Furry and wild, the bear roams free  \n' +
+        'Foraging the forest, strong as can be',
+      additional_kwargs: {}
+    }
+  }
+*/

examples/src/guides/expression_language/runnable_maps_sequence.ts

@@ -0,0 +1,41 @@
+import { ChatAnthropic } from "langchain/chat_models/anthropic";
+import { CohereEmbeddings } from "langchain/embeddings/cohere";
+import { PromptTemplate } from "langchain/prompts";
+import { StringOutputParser } from "langchain/schema/output_parser";
+import {
+  RunnablePassthrough,
+  RunnableSequence,
+} from "langchain/schema/runnable";
+import { HNSWLib } from "langchain/vectorstores/hnswlib";
+import type { Document } from "langchain/document";
+
+const model = new ChatAnthropic();
+const vectorstore = await HNSWLib.fromDocuments(
+  [{ pageContent: "mitochondria is the powerhouse of the cell", metadata: {} }],
+  new CohereEmbeddings()
+);
+const retriever = vectorstore.asRetriever();
+const template = `Answer the question based only on the following context:
+{context}
+
+Question: {question}`;
+
+const prompt = PromptTemplate.fromTemplate(template);
+
+const formatDocs = (docs: Document[]) => docs.map((doc) => doc.pageContent);
+
+const retrievalChain = RunnableSequence.from([
+  { context: retriever.pipe(formatDocs), question: new RunnablePassthrough() },
+  prompt,
+  model,
+  new StringOutputParser(),
+]);
+
+const result = await retrievalChain.invoke(
+  "what is the powerhouse of the cell?"
+);
+console.log(result);
+
+/*
+ Based on the given context, the powerhouse of the cell is mitochondria.
+*/

langchain/src/schema/runnable/base.ts

@@ -1252,6 +1252,10 @@ export class RunnableMap<RunInput> extends Runnable<
     }
   }
 
+  static from<RunInput>(steps: Record<string, RunnableLike<RunInput>>) {
+    return new RunnableMap<RunInput>({ steps });
+  }
+
   async invoke(
     input: RunInput,
     options?: Partial<BaseCallbackConfig>