Clean up legacy retrieval QA chain code in docs (#4384)

docs/core_docs/docs/integrations/document_loaders/web_loaders/searchapi.mdx

@@ -35,4 +35,4 @@ npm install @langchain/openai
 
 <CodeBlock language="typescript">{Example}</CodeBlock>
 
-In this example, the `SearchApiLoader` is used to load web search results, which are then stored in memory using `MemoryVectorStore`. The `RetrievalQAChain` is then used to retrieve the most relevant documents from the memory and answer the question based on these documents. This demonstrates how the `SearchApiLoader` can streamline the process of loading and processing web search results.
+In this example, the `SearchApiLoader` is used to load web search results, which are then stored in memory using `MemoryVectorStore`. A retrieval chain is then used to retrieve the most relevant documents from the memory and answer the question based on these documents. This demonstrates how the `SearchApiLoader` can streamline the process of loading and processing web search results.

docs/core_docs/docs/integrations/document_loaders/web_loaders/serpapi.mdx

@@ -33,4 +33,4 @@ npm install @langchain/openai
 
 <CodeBlock language="typescript">{Example}</CodeBlock>
 
-In this example, the `SerpAPILoader` is used to load web search results, which are then stored in memory using `MemoryVectorStore`. The `RetrievalQAChain` is then used to retrieve the most relevant documents from the memory and answer the question based on these documents. This demonstrates how the `SerpAPILoader` can streamline the process of loading and processing web search results.
+In this example, the `SerpAPILoader` is used to load web search results, which are then stored in memory using `MemoryVectorStore`. A retrieval chain is then used to retrieve the most relevant documents from the memory and answer the question based on these documents. This demonstrates how the `SerpAPILoader` can streamline the process of loading and processing web search results.

docs/core_docs/docs/integrations/retrievers/chaindesk-retriever.mdx

@@ -1,6 +1,6 @@
 # Chaindesk Retriever
 
-This example shows how to use the Chaindesk Retriever in a `RetrievalQAChain` to retrieve documents from a Chaindesk.ai datastore.
+This example shows how to use the Chaindesk Retriever in a retrieval chain to retrieve documents from a Chaindesk.ai datastore.
 
 ## Usage
 

docs/core_docs/docs/integrations/retrievers/metal-retriever.mdx

@@ -4,7 +4,7 @@ hide_table_of_contents: true
 
 # Metal Retriever
 
-This example shows how to use the Metal Retriever in a `RetrievalQAChain` to retrieve documents from a Metal index.
+This example shows how to use the Metal Retriever in a retrieval chain to retrieve documents from a Metal index.
 
 ## Setup
 

docs/core_docs/docs/integrations/retrievers/zep-retriever.mdx

@@ -4,7 +4,7 @@ hide_table_of_contents: true
 
 # Zep Retriever
 
-This example shows how to use the Zep Retriever in a `RetrievalQAChain` to retrieve documents from Zep memory store.
+This example shows how to use the Zep Retriever in a retrieval chain to retrieve documents from Zep memory store.
 
 ## Setup
 

docs/core_docs/docs/modules/data_connection/retrievers/index.mdx

@@ -93,7 +93,7 @@ Let's walk through what's happening here.
 
 2. Though we can query the vector store directly, we convert the vector store into a retriever to return retrieved documents in the right format for the question answering chain.
 
-3. We initialize a `RetrievalQAChain` with the `.fromLLM` method, which we'll call later in step 4.
+3. We initialize a retrieval chain, which we'll call later in step 4.
 
 4. We ask questions!
 

docs/core_docs/docs/modules/data_connection/retrievers/vectorstore.mdx

@@ -22,7 +22,6 @@ npm install @langchain/openai
 
 ```typescript
 import { OpenAI, OpenAIEmbeddings } from "@langchain/openai";
-import { RetrievalQAChain } from "langchain/chains";
 import { HNSWLib } from "langchain/vectorstores/hnswlib";
 import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
 import * as fs from "fs";

examples/src/document_loaders/apify_dataset_existing.ts

@@ -1,8 +1,10 @@
 import { ApifyDatasetLoader } from "langchain/document_loaders/web/apify_dataset";
 import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";
-import { OpenAIEmbeddings, OpenAI } from "@langchain/openai";
-import { RetrievalQAChain } from "langchain/chains";
+import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
 import { Document } from "@langchain/core/documents";
+import { ChatPromptTemplate } from "@langchain/core/prompts";
+import { createRetrievalChain } from "langchain/chains/retrieval";
+import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
 
 /*
  * datasetMappingFunction is a function that maps your Apify dataset format to LangChain documents.
@@ -27,17 +29,32 @@ const docs = await loader.load();
 
 const vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());
 
-const model = new OpenAI({
+const model = new ChatOpenAI({
   temperature: 0,
 });
 
-const chain = RetrievalQAChain.fromLLM(model, vectorStore.asRetriever(), {
-  returnSourceDocuments: true,
+const questionAnsweringPrompt = ChatPromptTemplate.fromMessages([
+  [
+    "system",
+    "Answer the user's questions based on the below context:\n\n{context}",
+  ],
+  ["human", "{input}"],
+]);
+
+const combineDocsChain = await createStuffDocumentsChain({
+  llm: model,
+  prompt: questionAnsweringPrompt,
 });
-const res = await chain.call({ query: "What is LangChain?" });
 
-console.log(res.text);
-console.log(res.sourceDocuments.map((d: Document) => d.metadata.source));
+const chain = await createRetrievalChain({
+  retriever: vectorStore.asRetriever(),
+  combineDocsChain,
+});
+
+const res = await chain.invoke({ input: "What is LangChain?" });
+
+console.log(res.answer);
+console.log(res.context.map((doc) => doc.metadata.source));
 
 /*
   LangChain is a framework for developing applications powered by language models.

examples/src/document_loaders/apify_dataset_new.ts

@@ -1,8 +1,10 @@
 import { ApifyDatasetLoader } from "langchain/document_loaders/web/apify_dataset";
 import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";
-import { OpenAIEmbeddings, OpenAI } from "@langchain/openai";
-import { RetrievalQAChain } from "langchain/chains";
+import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";
 import { Document } from "@langchain/core/documents";
+import { ChatPromptTemplate } from "@langchain/core/prompts";
+import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
+import { createRetrievalChain } from "langchain/chains/retrieval";
 
 /*
  * datasetMappingFunction is a function that maps your Apify dataset format to LangChain documents.
@@ -33,17 +35,32 @@ const docs = await loader.load();
 
 const vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());
 
-const model = new OpenAI({
+const model = new ChatOpenAI({
   temperature: 0,
 });
 
-const chain = RetrievalQAChain.fromLLM(model, vectorStore.asRetriever(), {
-  returnSourceDocuments: true,
+const questionAnsweringPrompt = ChatPromptTemplate.fromMessages([
+  [
+    "system",
+    "Answer the user's questions based on the below context:\n\n{context}",
+  ],
+  ["human", "{input}"],
+]);
+
+const combineDocsChain = await createStuffDocumentsChain({
+  llm: model,
+  prompt: questionAnsweringPrompt,
 });
-const res = await chain.call({ query: "What is LangChain?" });
 
-console.log(res.text);
-console.log(res.sourceDocuments.map((d: Document) => d.metadata.source));
+const chain = await createRetrievalChain({
+  retriever: vectorStore.asRetriever(),
+  combineDocsChain,
+});
+
+const res = await chain.invoke({ input: "What is LangChain?" });
+
+console.log(res.answer);
+console.log(res.context.map((doc) => doc.metadata.source));
 
 /*
   LangChain is a framework for developing applications powered by language models.

examples/src/document_loaders/searchapi.ts

@@ -1,17 +1,21 @@
-import { OpenAI, OpenAIEmbeddings } from "@langchain/openai";
-import { RetrievalQAChain } from "langchain/chains";
+import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
 import { MemoryVectorStore } from "langchain/vectorstores/memory";
 import { TokenTextSplitter } from "langchain/text_splitter";
 import { SearchApiLoader } from "langchain/document_loaders/web/searchapi";
+import { ChatPromptTemplate } from "@langchain/core/prompts";
+import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
+import { createRetrievalChain } from "langchain/chains/retrieval";
 
 // Initialize the necessary components
-const llm = new OpenAI();
+const llm = new ChatOpenAI({
+  modelName: "gpt-3.5-turbo-1106",
+});
 const embeddings = new OpenAIEmbeddings();
 const apiKey = "Your SearchApi API key";
 
 // Define your question and query
 const question = "Your question here";
-const query = "Your question here";
+const query = "Your query here";
 
 // Use SearchApiLoader to load web search results
 const loader = new SearchApiLoader({ q: query, apiKey, engine: "google" });
@@ -21,17 +25,35 @@ const textSplitter = new TokenTextSplitter({
   chunkSize: 800,
   chunkOverlap: 100,
 });
+
 const splitDocs = await textSplitter.splitDocuments(docs);
 
 // Use MemoryVectorStore to store the loaded documents in memory
 const vectorStore = await MemoryVectorStore.fromDocuments(
   splitDocs,
   embeddings
 );
-// Use RetrievalQAChain to retrieve documents and answer the question
-const chain = RetrievalQAChain.fromLLM(llm, vectorStore.asRetriever(), {
-  verbose: true,
+
+const questionAnsweringPrompt = ChatPromptTemplate.fromMessages([
+  [
+    "system",
+    "Answer the user's questions based on the below context:\n\n{context}",
+  ],
+  ["human", "{input}"],
+]);
+
+const combineDocsChain = await createStuffDocumentsChain({
+  llm,
+  prompt: questionAnsweringPrompt,
+});
+
+const chain = await createRetrievalChain({
+  retriever: vectorStore.asRetriever(),
+  combineDocsChain,
+});
+
+const res = await chain.invoke({
+  input: question,
 });
-const answer = await chain.call({ query: question });
 
-console.log(answer.text);
+console.log(res.answer);

examples/src/document_loaders/serpapi.ts

@@ -1,10 +1,12 @@
-import { OpenAI, OpenAIEmbeddings } from "@langchain/openai";
-import { RetrievalQAChain } from "langchain/chains";
+import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";
 import { MemoryVectorStore } from "langchain/vectorstores/memory";
 import { SerpAPILoader } from "langchain/document_loaders/web/serpapi";
+import { ChatPromptTemplate } from "@langchain/core/prompts";
+import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
+import { createRetrievalChain } from "langchain/chains/retrieval";
 
 // Initialize the necessary components
-const llm = new OpenAI();
+const llm = new ChatOpenAI();
 const embeddings = new OpenAIEmbeddings();
 const apiKey = "Your SerpAPI API key";
 
@@ -19,8 +21,26 @@ const docs = await loader.load();
 // Use MemoryVectorStore to store the loaded documents in memory
 const vectorStore = await MemoryVectorStore.fromDocuments(docs, embeddings);
 
-// Use RetrievalQAChain to retrieve documents and answer the question
-const chain = RetrievalQAChain.fromLLM(llm, vectorStore.asRetriever());
-const answer = await chain.call({ query: question });
+const questionAnsweringPrompt = ChatPromptTemplate.fromMessages([
+  [
+    "system",
+    "Answer the user's questions based on the below context:\n\n{context}",
+  ],
+  ["human", "{input}"],
+]);
 
-console.log(answer.text);
+const combineDocsChain = await createStuffDocumentsChain({
+  llm,
+  prompt: questionAnsweringPrompt,
+});
+
+const chain = await createRetrievalChain({
+  retriever: vectorStore.asRetriever(),
+  combineDocsChain,
+});
+
+const res = await chain.invoke({
+  input: question,
+});
+
+console.log(res.answer);