Add more nodes for agents, loaders

packages/components/nodes/chains/ConversationalRetrievalQAChain/ConversationalRetrievalQAChain.ts

@@ -0,0 +1,74 @@
+import { ICommonObject, IMessage, INode, INodeData, INodeParams } from '../../../src/Interface'
+import { getBaseClasses } from '../../../src/utils'
+
+class ConversationalRetrievalQAChain_Chains implements INode {
+    label: string
+    name: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    description: string
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'Conversational Retrieval QA Chain'
+        this.name = 'conversationalRetrievalQAChain'
+        this.type = 'ConversationalRetrievalQAChain'
+        this.icon = 'chain.svg'
+        this.category = 'Chains'
+        this.description = 'Document QA - built on RetrievalQAChain to provide a chat history component'
+        this.inputs = [
+            {
+                label: 'LLM',
+                name: 'llm',
+                type: 'BaseLanguageModel'
+            },
+            {
+                label: 'Vector Store Retriever',
+                name: 'vectorStoreRetriever',
+                type: 'BaseRetriever'
+            }
+        ]
+    }
+
+    async getBaseClasses(): Promise<string[]> {
+        const { ConversationalRetrievalQAChain } = await import('langchain/chains')
+        return getBaseClasses(ConversationalRetrievalQAChain)
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const { ConversationalRetrievalQAChain } = await import('langchain/chains')
+
+        const llm = nodeData.inputs?.llm
+        const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever
+
+        const chain = ConversationalRetrievalQAChain.fromLLM(llm, vectorStoreRetriever)
+        return chain
+    }
+
+    async run(nodeData: INodeData, input: string, options: ICommonObject): Promise<string> {
+        const chain = nodeData.instance
+        let chatHistory = ''
+
+        if (options && options.chatHistory) {
+            const histories: IMessage[] = options.chatHistory
+            chatHistory = histories
+                .map((item) => {
+                    return item.message
+                })
+                .join('')
+        }
+
+        const obj = {
+            question: input,
+            chat_history: chatHistory ? chatHistory : []
+        }
+
+        const res = await chain.call(obj)
+
+        return res?.text
+    }
+}
+
+module.exports = { nodeClass: ConversationalRetrievalQAChain_Chains }

packages/components/nodes/chains/ConversationalRetrievalQAChain/chain.svg

@@ -0,0 +1,6 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-dna" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
+   <path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
+   <path d="M14.828 14.828a4 4 0 1 0 -5.656 -5.656a4 4 0 0 0 5.656 5.656z"></path>
+   <path d="M9.172 20.485a4 4 0 1 0 -5.657 -5.657"></path>
+   <path d="M14.828 3.515a4 4 0 0 0 5.657 5.657"></path>
+</svg>

packages/components/nodes/chains/LLMChain/LLMChain.ts

@@ -28,6 +28,17 @@ class LLMChain_Chains implements INode {
                 label: 'Prompt',
                 name: 'prompt',
                 type: 'BasePromptTemplate'
+            },
+            {
+                label: 'Format Prompt Values',
+                name: 'promptValues',
+                type: 'string',
+                rows: 5,
+                placeholder: `{
+  "input_language": "English",
+  "output_language": "French"
+}`,
+                optional: true
             }
         ]
     }
@@ -48,13 +59,39 @@ class LLMChain_Chains implements INode {
     }
 
     async run(nodeData: INodeData, input: string): Promise<string> {
-        const prompt = nodeData.instance.prompt.inputVariables // ["product"]
-        if (prompt.length > 1) throw new Error('Prompt can only contains 1 literal string {}. Multiples are found')
-
+        const inputVariables = nodeData.instance.prompt.inputVariables // ["product"]
         const chain = nodeData.instance
-        const res = await chain.run(input)
 
-        return res
+        if (inputVariables.length === 1) {
+            const res = await chain.run(input)
+            return res
+        } else if (inputVariables.length > 1) {
+            const promptValuesStr = nodeData.inputs?.promptValues as string
+            if (!promptValuesStr) throw new Error('Please provide Prompt Values')
+
+            const promptValues = JSON.parse(promptValuesStr.replace(/\s/g, ''))
+
+            let seen = []
+
+            for (const variable of inputVariables) {
+                seen.push(variable)
+                if (promptValues[variable]) {
+                    seen.pop()
+                }
+            }
+
+            if (seen.length === 1) {
+                const options = {
+                    ...promptValues,
+                    [seen.pop()]: input
+                }
+                const res = await chain.call(options)
+                return res?.text
+            } else throw new Error('Please provide Prompt Values')
+        } else {
+            const res = await chain.run(input)
+            return res
+        }
     }
 }
 

packages/components/nodes/chains/RetrievalQAChain/RetrievalQAChain.ts

@@ -0,0 +1,57 @@
+import { INode, INodeData, INodeParams } from '../../../src/Interface'
+
+class RetrievalQAChain_Chains implements INode {
+    label: string
+    name: string
+    type: string
+    icon: string
+    category: string
+    baseClasses: string[]
+    description: string
+    inputs: INodeParams[]
+
+    constructor() {
+        this.label = 'RetrievalQA Chain'
+        this.name = 'retrievalQAChain'
+        this.type = 'RetrievalQAChain'
+        this.icon = 'chain.svg'
+        this.category = 'Chains'
+        this.description = 'QA chain to answer a question based on the retrieved documents'
+        this.inputs = [
+            {
+                label: 'LLM',
+                name: 'llm',
+                type: 'BaseLanguageModel'
+            },
+            {
+                label: 'Vector Store Retriever',
+                name: 'vectorStoreRetriever',
+                type: 'BaseRetriever'
+            }
+        ]
+    }
+
+    async getBaseClasses(): Promise<string[]> {
+        return ['BaseChain']
+    }
+
+    async init(nodeData: INodeData): Promise<any> {
+        const { RetrievalQAChain } = await import('langchain/chains')
+        const llm = nodeData.inputs?.llm
+        const vectorStoreRetriever = nodeData.inputs?.vectorStoreRetriever
+
+        const chain = RetrievalQAChain.fromLLM(llm, vectorStoreRetriever)
+        return chain
+    }
+
+    async run(nodeData: INodeData, input: string): Promise<string> {
+        const chain = nodeData.instance
+        const obj = {
+            query: input
+        }
+        const res = await chain.call(obj)
+        return res?.text
+    }
+}
+
+module.exports = { nodeClass: RetrievalQAChain_Chains }

packages/components/nodes/chains/RetrievalQAChain/chain.svg

@@ -0,0 +1,6 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-dna" width="24" height="24" viewBox="0 0 24 24" stroke-width="2" stroke="currentColor" fill="none" stroke-linecap="round" stroke-linejoin="round">
+   <path stroke="none" d="M0 0h24v24H0z" fill="none"></path>
+   <path d="M14.828 14.828a4 4 0 1 0 -5.656 -5.656a4 4 0 0 0 5.656 5.656z"></path>
+   <path d="M9.172 20.485a4 4 0 1 0 -5.657 -5.657"></path>
+   <path d="M14.828 3.515a4 4 0 0 0 5.657 5.657"></path>
+</svg>