refactor: Rename all file/folders to camelCase

agentTools/specRetriever.ts

@@ -0,0 +1,88 @@
+/**
+ * Specification retriever tool.
+ * Queries the language specification for text content about specific sections or topics.
+ */
+
+import type { Table } from "@lancedb/lancedb";
+import { DynamicStructuredTool } from "@langchain/core/tools";
+import type { OllamaEmbeddings } from "@langchain/ollama";
+import { z } from "zod";
+import { rerankDocuments } from "./reranker";
+
+const specRetrieverSchema = z.object({
+  query: z
+    .string()
+    .describe("The search query to find relevant specification sections"),
+});
+
+/**
+ * Creates the spec retriever tool.
+ * @param table - LanceDB table containing spec vectors
+ * @param embeddings - Ollama embeddings instance
+ */
+export function createSpecRetrieverTool(
+  table: Table,
+  embeddings: OllamaEmbeddings,
+) {
+  return new DynamicStructuredTool({
+    name: "spec_retriever",
+    description:
+      "Queries the language specification for text content about specific sections or topics. Fetches up to 10 initial matches and uses a reranker to dynamically select the most relevant 3-5 documents based on query relevance.",
+    schema: specRetrieverSchema,
+    func: async ({ query }) => {
+      // Generate embedding for the query
+      const queryVector = await embeddings.embedQuery(query);
+
+      // Search using LanceDB directly
+      const results = await table.search(queryVector).limit(10).toArray();
+
+      // Create document objects with metadata
+      const documents = results.map((r: Record<string, unknown>) => ({
+        pageContent: String(r.text || ""),
+        metadata: {
+          source: r.source,
+          sectionid: r.sectionid,
+          sectiontitle: r.sectiontitle,
+          type: r.type,
+          parentsectionid: r.parentsectionid,
+          childrensectionids: r.childrensectionids,
+          partindex: r.partindex,
+          totalparts: r.totalparts,
+        },
+      }));
+
+      // Rerank documents
+      const reranked = await rerankDocuments(query, documents);
+
+      // Sort by score and filter to most relevant
+      reranked.sort((a, b) => b.score - a.score);
+
+      // Dynamic selection: take top documents with score > 0.5, or at least top 3
+      const threshold = 0.5;
+      const minDocs = 3;
+      const maxDocs = 5;
+
+      const selected = reranked.filter(
+        (r, i) => i < minDocs || (i < maxDocs && r.score > threshold),
+      );
+
+      console.log(
+        `[spec_retriever] Query: "${query.slice(0, 50)}..." - Fetched ${documents.length}, reranked to ${selected.length} (scores: ${selected.map((s) => s.score.toFixed(2)).join(", ")})`,
+      );
+
+      // Return documents with metadata
+      return selected
+        .map((r) => {
+          const meta = r.document.metadata;
+          const sectionId = meta?.sectionid || "unknown";
+          const sectionTitle = meta?.sectiontitle || "unknown";
+          const partInfo =
+            meta?.partindex !== null && meta?.partindex !== undefined
+              ? ` [part ${(meta.partindex as number) + 1}/${meta.totalparts}]`
+              : "";
+          return `--- Section: ${sectionId} | "${sectionTitle}"${partInfo} (score: ${r.score.toFixed(2)}) ---\n${r.document.pageContent}`;
+        })
+        .join("\n\n");
+    },
+  });
+}