init

packages/genaiscript/genaisrc/deep-research.genai.mts

@@ -0,0 +1,281 @@
+import {task, entrypoint, interrupt, MemorySaver} from "@langchain/langgraph"
+import "./tools/searxng.genai.mjs"
+import {SearxngClient} from "@agentic/searxng";
+
+
+script({
+    title: "Deep Research Program",
+    description: "Researchers can use this program to conduct deep research on a topic",
+    model: "large",
+    cache: "ephemeral",
+})
+const {output, vars} = env
+
+
+const breakdownResearch = task(
+    "breakdown_research",
+    async (question: string) => {
+        const result = await runPrompt(
+            async (ctx) => {
+                ctx.$`You are an expert research strategist.
+
+Task: Break down the following research question into 3-5 focused sub-questions that would help comprehensively answer the main question.
+
+Research question: ${question}
+
+For each sub-question:
+1. Assign a unique ID (e.g., SQ1, SQ2)
+2. Explain the rationale for why this sub-question is important
+3. Ensure the sub-questions collectively cover the main research question
+
+Output the breakdown as a JSON object.`
+            },
+            {
+                label: "breakdown research",
+                responseSchema: {
+                    type: "object",
+                    properties: {
+                        mainQuestion: {type: "string"},
+                        subQuestions: {
+                            type: "array",
+                            items: {
+                                type: "object",
+                                properties: {
+                                    id: {type: "string"},
+                                    question: {type: "string"},
+                                    rationale: {type: "string"},
+                                },
+                            },
+                        },
+                    },
+                },
+            }
+        )
+
+        return result.json
+    }
+)
+
+const globalCtx = this;
+
+
+const researchSubQuestion = task(
+    "research_subquestion",
+    async (subQuestion: { id: string; question: string }) => {
+
+        const searxng = new SearxngClient({apiBaseUrl: "https://search-engine-gsio.fly.dev"});
+
+        const {text} = await runPrompt(
+            (_) => {
+                _.defTool(searxng)
+                _.$`You are an expert researcher with access to comprehensive information.
+    
+Task: Thoroughly research the following question and provide a detailed answer.
+
+Question ID: ${subQuestion.id}
+Question: ${subQuestion.question}
+
+Provide your findings in a structured format that includes:
+- Your answer to the sub-question
+- Relevant sources that support your answer
+- Your confidence level in the answer (0-1)`
+            },
+            {
+                model: "small",
+                label: `research subquestion ${subQuestion.id}`,
+                maxDataRepairs: 2,
+                responseSchema: {
+                    type: "object",
+                    properties: {
+                        subQuestionId: {type: "string"},
+                        answer: {type: "string"},
+                        sources: {
+                            type: "array",
+                            items: {
+                                type: "object",
+                                properties: {
+                                    title: {type: "string"},
+                                    url: {type: "string"},
+                                    relevance: {type: "string"},
+                                },
+                            },
+                        },
+                        confidence: {type: "number"},
+                    },
+                },
+            }
+        )
+        return text
+    }
+)
+
+
+const synthesizeFindings = task(
+    "synthesize_findings",
+    async (mainQuestion: string, findings: any[]) => {
+        const result = await runPrompt(
+            async (ctx) => {
+                ctx.$`You are an expert research synthesizer.
+    
+Task: Synthesize the following research findings into a coherent response to the main research question.
+
+Main Research Question: ${mainQuestion}
+
+Findings:
+${JSON.stringify(findings, null, 2)}
+
+Provide a synthesis that:
+1. Directly answers the main research question
+2. Integrates the findings from all sub-questions
+3. Identifies limitations in the current research
+4. Suggests next steps for further investigation`
+            },
+            {
+                label: "synthesize findings",
+                responseType: "markdown",
+                responseSchema: {
+                    type: "object",
+                    properties: {
+                        summary: {type: "string"},
+                        findings: {type: "array", items: {type: "string"}},
+                        limitations: {
+                            type: "array",
+                            items: {type: "string"},
+                        },
+                        nextSteps: {type: "array", items: {type: "string"}},
+                    },
+                },
+            }
+        )
+
+        return result.json
+    }
+)
+
+
+const summarizeAndIdentifyGaps = task(
+    "summarize_and_identify_gaps",
+    async (synthesis: any, findings: any[]) => {
+        const result = await runPrompt(
+            async (ctx) => {
+                ctx.$`You are an expert research evaluator.
+                        
+Task: Review the research synthesis and identify any gaps or areas that need deeper investigation.
+
+Current synthesis:
+${JSON.stringify(synthesis, null, 2)}
+
+Research findings:
+${JSON.stringify(findings, null, 2)}
+
+Please provide:
+1. A concise summary of current findings
+2. Identify 2-3 specific knowledge gaps
+3. Formulate follow-up questions to address these gaps`
+            },
+            {
+                label: "identify research gaps",
+                responseSchema: {
+                    type: "object",
+                    properties: {
+                        summary: {type: "string"},
+                        gaps: {
+                            type: "array",
+                            items: {type: "string"},
+                        },
+                        followUpQuestions: {
+                            type: "array",
+                            items: {
+                                type: "object",
+                                properties: {
+                                    id: {type: "string"},
+                                    question: {type: "string"},
+                                },
+                            },
+                        },
+                    },
+                },
+            }
+        )
+        return result.json
+    }
+)
+
+
+const researchWorkflow = entrypoint(
+    {checkpointer: new MemorySaver(), name: "research_workflow"},
+    async (input: { question: string; context?: string }) => {
+
+        const breakdown = await breakdownResearch(input.question)
+
+
+        const subQuestionFindings = []
+
+        for (const sq of breakdown.subQuestions) {
+            const analysis = await researchSubQuestion(sq);
+            console.log(analysis);
+            subQuestionFindings.push(analysis);
+        }
+
+
+        let synthesis = await synthesizeFindings(
+            input.question,
+            subQuestionFindings
+        )
+
+        const gapAnalysis = await summarizeAndIdentifyGaps(
+            synthesis,
+            subQuestionFindings
+        )
+
+
+        const followUpFindings = [];
+        for (const fq of gapAnalysis.followUpQuestions) {
+            const anwser = await researchSubQuestion(fq);
+            console.log(anwser);
+            followUpFindings.push(anwser);
+        }
+
+
+        const allFindings = [...subQuestionFindings, ...followUpFindings]
+        const finalSynthesis = await synthesizeFindings(
+            input.question,
+            allFindings
+        )
+
+
+        return {
+            question: input.question,
+            breakdown: breakdown,
+            initialFindings: subQuestionFindings,
+            gapAnalysis: gapAnalysis,
+            followUpFindings: followUpFindings,
+            synthesis: finalSynthesis,
+        }
+    }
+)
+
+
+const researchQuestion =
+    env.vars.question ||
+    "What are the most promising approaches to climate change mitigation?"
+
+
+const threadId = `research-${Date.now()}`
+
+
+const config = {
+    configurable: {
+        thread_id: threadId,
+    },
+}
+
+
+const results = await researchWorkflow.invoke(
+    {
+        question: researchQuestion,
+        context: vars.context || "",
+    },
+    config
+)
+output.fence(results, "json")