LangChain Integration

1import { ChatOpenAI } from '@langchain/openai';
2import { DynamicTool } from '@langchain/core/tools';
3import { AgentExecutor, createOpenAIFunctionsAgent } from 'langchain/agents';
4import { ChatPromptTemplate, MessagesPlaceholder } from '@langchain/core/prompts';
5import { Cognitora } from 'cognitora';
6
7const cognitora = new Cognitora({ apiKey: process.env.COGNITORA_API_KEY });
8
9async function createLangChainCodeInterpreter() {
10  const session = await cognitora.codeInterpreter.createSession({
11    language: 'python',
12    timeout_minutes: 30,
13    resources: {
14      cpu_cores: 1.0,
15      memory_mb: 512,
16      storage_gb: 5
17    }
18  });
19
20  const codeExecutionTool = new DynamicTool({
21    name: "execute_python_code",
22    description: "Execute Python code in a secure sandbox environment.",
23    func: async (code: string) => {
24      try {
25        const execution = await cognitora.codeInterpreter.execute({
26          code: code,
27          language: 'python',
28          session_id: session.data.session_id,
29          networking: true  // Enable networking for code interpreter
30        });
31        
32        if (execution.data.status === 'completed') {
33          const outputs = execution.data.outputs;
34          const stdout = outputs.filter(o => o.type === 'stdout').map(o => o.content).join('\n');
35          return `Execution successful:\n${stdout}`;
36        } else {
37          const stderr = execution.data.outputs.filter(o => o.type === 'stderr').map(o => o.content).join('\n');
38          return `Execution failed:\n${stderr}`;
39        }
40      } catch (error) {
41        return `Error executing code: ${error.message}`;
42      }
43    },
44  });
45
46  return { session, codeExecutionTool };
47}
48
49async function runLangChainAgent(userQuery: string) {
50  const { session, codeExecutionTool } = await createLangChainCodeInterpreter();
51  
52  const model = new ChatOpenAI({ modelName: "gpt-4", temperature: 0 });
53
54  const prompt = ChatPromptTemplate.fromMessages([
55    ["system", "You are a helpful AI assistant with access to a Python code execution environment."],
56    ["human", "{input}"],
57    new MessagesPlaceholder("agent_scratchpad"),
58  ]);
59
60  const agent = await createOpenAIFunctionsAgent({
61    llm: model,
62    tools: [codeExecutionTool],
63    prompt,
64  });
65
66  const agentExecutor = new AgentExecutor({
67    agent,
68    tools: [codeExecutionTool],
69    verbose: true,
70  });
71
72  const result = await agentExecutor.invoke({ input: userQuery });
73  
74  return {
75    output: result.output,
76    sessionId: session.id
77  };
78}