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\n \n \n \n \n \n \n \n \n \n {results.map((result) => (\n \n \n \n \n \n ))}\n \n

Name	Content	embeddingsContent
{result.name}	{result.content}	{result.embeddingsContent}

\n);\n\nconst Home = () => (\n

Build Club Project Search

\n \n \n

\n);\n\nconst airtableApiKey = Deno.env.get(\"AIRTABLE_API_KEY\");\nconst defaultBaseId = \"app1wLB4irV14mxMU\";\nconst defaultTableName = \"ExampleItems\";\nconst defaultNameColumn = \"Name\";\nconst defaultContentColumn = \"Content\";\nconst defaultEmbeddingColumn = \"Embeddings\";\n\nasync function fetchAirtableData(baseId, tableName, nameColumn, contentColumn, embeddingColumn) {\n const base = new Airtable({ apiKey: airtableApiKey }).base(baseId);\n const records = await base(tableName).select().all();\n const documents = records.map(record => ({\n id: record.id,\n name: record.get(nameColumn),\n content: record.get(contentColumn),\n embedding: record.get(embeddingColumn).split(\",\").map(parseFloat),\n embeddingsContent: record.get('EmbeddingsContent'),\n }));\n return documents;\n}\n\nasync function searchDocuments(c, query, similarityThreshold) {\n const baseId = c.req.query(\"base_id\") || defaultBaseId;\n const tableName = c.req.query(\"table_name\") || defaultTableName;\n const nameColumn = c.req.query(\"name_column\") || defaultNameColumn;\n const contentColumn = c.req.query(\"content_column\") || defaultContentColumn;\n const embeddingColumn = c.req.query(\"embedding_column\") || defaultEmbeddingColumn;\n\n try {\n const documents = await fetchAirtableData(baseId, tableName, nameColumn, contentColumn, embeddingColumn);\n console.log('documents:', documents)\n await semanticSearch.addDocuments({documents, fields: 'embeddingsContent'});\n const results = await semanticSearch.search({query, similarityThreshold});\n\n if (c.req.headers.get(\"HX-Request\")) {\n return c.html();\n } else {\n return c.json(results);\n }\n } catch (error) {\n console.error(\"Search error:\", error);\n throw error;\n }\n}\n\napp.get(\"/\", (c) => {\n return c.html(\n \n \n ,\n );\n});\n\napp.get(\"/search\", async (c) => {\n const query = c.req.query(\"query\");\n const similarityThreshold = parseFloat(c.req.query(\"similarity_threshold\") || \"0.28\");\n\n try {\n return await searchDocuments(c, query, similarityThreshold);\n } catch (error) {\n return c.text(\"Error occurred during search\", 500);\n }\n});\n\napp.post(\"/search\", async (c) => {\n const { query, similarity_threshold } = await c.req.json();\n const similarityThreshold = similarity_threshold || \"0.28\";\n\n try {\n return await searchDocuments(c, query, parseFloat(similarityThreshold));\n } catch (error) {\n return c.text(\"Error occurred during search\", 500);\n }\n});\n\nexport default app.fetch;\n","deleted","label",[109],"07ec7ae4-2793-11f0-9d52-569c3dd06744","README.md","07ec7b5c-2793-11f0-9d52-569c3dd06744",1,["D",1717627587518],"Use embeddings / Lunr search on Airtable. 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