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import { Hono } from 'hono'; | |
import { streamSSE } from 'hono/streaming'; | |
import { v4 as uuidv4 } from 'uuid'; | |
import { z } from 'zod'; | |
import { config } from '../config'; | |
import { cache } from '../services/cache'; | |
import { vespaRequest } from '../services/vespa-https'; | |
const backendApi = new Hono(); | |
// Search request schema | |
const searchQuerySchema = z.object({ | |
query: z.string().min(1).max(500), | |
ranking: z.enum(['hybrid', 'colpali', 'bm25']).optional().default('hybrid'), | |
}); | |
// Main search endpoint - /fetch_results | |
backendApi.get('/fetch_results', async (c) => { | |
try { | |
const query = c.req.query('query'); | |
const ranking = c.req.query('ranking') || 'hybrid'; | |
const validation = searchQuerySchema.safeParse({ query, ranking }); | |
if (!validation.success) { | |
return c.json({ error: 'Invalid request', details: validation.error.issues }, 400); | |
} | |
const validatedData = validation.data; | |
// Check cache | |
const cacheKey = `search:${validatedData.query}:${validatedData.ranking}`; | |
const cachedResult = cache.get(cacheKey); | |
if (cachedResult) { | |
c.header('X-Cache', 'HIT'); | |
return c.json(cachedResult); | |
} | |
// Build YQL query based on ranking | |
let yql = ''; | |
let searchParams: any = { | |
query: validatedData.query, | |
hits: '20' | |
}; | |
switch (validatedData.ranking) { | |
case 'colpali': | |
// Use retrieval-and-rerank profile for ColPali | |
yql = `select * from linqto where userQuery() limit 20`; | |
searchParams.ranking = 'retrieval-and-rerank'; | |
break; | |
case 'bm25': | |
yql = `select * from linqto where userQuery() limit 20`; | |
searchParams.ranking = 'default'; | |
break; | |
case 'hybrid': | |
default: | |
yql = `select * from linqto where userQuery() limit 20`; | |
searchParams.ranking = 'default'; | |
break; | |
} | |
// For ColPali ranking, we need embeddings | |
let body: any = {}; | |
let useNearestNeighbor = false; | |
if (validatedData.ranking === 'colpali') { | |
try { | |
// Call embedding API to get query embeddings | |
const embeddingResponse = await fetch( | |
`http://localhost:7861/embed_query?query=${encodeURIComponent(validatedData.query)}` | |
); | |
if (embeddingResponse.ok) { | |
const embeddingData = await embeddingResponse.json(); | |
// Create nearestNeighbor query string | |
const numTokens = Object.keys(embeddingData.embeddings.binary).length; | |
const maxTokens = Math.min(numTokens, 20); // Limit to 20 tokens to avoid timeouts | |
const nnClauses = []; | |
// Add individual rq tensors for nearestNeighbor | |
for (let i = 0; i < maxTokens; i++) { | |
body[`input.query(rq${i})`] = embeddingData.embeddings.binary[i.toString()]; | |
nnClauses.push(`({targetHits:10}nearestNeighbor(embedding,rq${i}))`); | |
} | |
// Update YQL for nearestNeighbor search | |
if (nnClauses.length > 0) { | |
yql = `select * from linqto where ${nnClauses.join(' OR ')} limit 20`; | |
useNearestNeighbor = true; | |
} | |
// Add qt and qtb for ranking | |
body["input.query(qt)"] = embeddingData.embeddings.float; | |
body["input.query(qtb)"] = embeddingData.embeddings.binary; | |
body["presentation.timing"] = true; | |
} else { | |
// Fall back to text-only search | |
searchParams.ranking = 'default'; | |
} | |
} catch (error) { | |
console.log('Embedding API not available, falling back to text search'); | |
searchParams.ranking = 'default'; | |
} | |
} | |
// Query Vespa directly | |
const searchUrl = `${config.vespaAppUrl}/search/`; | |
const urlSearchParams = new URLSearchParams({ | |
yql, | |
...searchParams | |
}); | |
// Use ranking.profile for Vespa instead of ranking | |
if (searchParams.ranking) { | |
urlSearchParams.delete('ranking'); | |
urlSearchParams.set('ranking.profile', searchParams.ranking); | |
} | |
const startTime = Date.now(); | |
let requestOptions: any = {}; | |
// Only use POST with body if we have embeddings | |
if (Object.keys(body).length > 0) { | |
requestOptions = { | |
method: 'POST', | |
headers: { | |
'Content-Type': 'application/json', | |
}, | |
body: JSON.stringify(body) | |
}; | |
} else { | |
requestOptions = { | |
method: 'GET' | |
}; | |
} | |
console.log('Vespa query URL:', `${searchUrl}?${urlSearchParams}`); | |
console.log('Request options:', requestOptions); | |
const response = await vespaRequest(`${searchUrl}?${urlSearchParams}`, requestOptions); | |
if (!response.ok && response.status !== 504) { | |
const errorText = await response.text(); | |
console.error('Vespa error:', errorText); | |
throw new Error(`Vespa returned ${response.status}: ${errorText}`); | |
} | |
const data = await response.json(); | |
const searchTime = (Date.now() - startTime) / 1000; // Convert to seconds | |
// Generate query_id for sim_map compatibility | |
const queryId = uuidv4(); | |
// Transform to match expected format | |
if (data.root && data.root.children) { | |
data.root.children.forEach((hit: any, idx: number) => { | |
if (!hit.fields) hit.fields = {}; | |
// Add sim_map identifier for compatibility | |
hit.fields.sim_map = `${queryId}_${idx}`; | |
}); | |
} | |
// Add timing information | |
data.timing = { | |
searchtime: searchTime | |
}; | |
// Cache the result | |
cache.set(cacheKey, data); | |
c.header('X-Cache', 'MISS'); | |
return c.json(data); | |
} catch (error) { | |
console.error('Search error:', error); | |
return c.json({ | |
error: 'Search failed', | |
message: error instanceof Error ? error.message : 'Unknown error' | |
}, 500); | |
} | |
}); | |
// Full image endpoint - /full_image | |
backendApi.get('/full_image', async (c) => { | |
try { | |
const docId = c.req.query('doc_id'); // Note: backend expects doc_id, not docId | |
if (!docId) { | |
return c.json({ error: 'doc_id is required' }, 400); | |
} | |
// Check cache | |
const cacheKey = `fullimage:${docId}`; | |
const cachedImage = cache.get<{ base64_image: string }>(cacheKey); | |
if (cachedImage) { | |
c.header('X-Cache', 'HIT'); | |
return c.json(cachedImage); | |
} | |
// Query Vespa for the document | |
const searchUrl = `${config.vespaAppUrl}/search/`; | |
const searchParams = new URLSearchParams({ | |
yql: `select * from linqto where id contains "${docId}"`, | |
hits: '1' | |
}); | |
const response = await vespaRequest(`${searchUrl}?${searchParams}`); | |
if (!response.ok) { | |
throw new Error(`Vespa returned ${response.status}`); | |
} | |
const data = await response.json(); | |
if (data.root?.children?.[0]?.fields) { | |
const fields = data.root.children[0].fields; | |
const base64Image = fields.full_image || fields.image; | |
if (base64Image) { | |
const result = { base64_image: base64Image }; | |
cache.set(cacheKey, result, 86400); // 24 hours | |
c.header('X-Cache', 'MISS'); | |
return c.json(result); | |
} | |
} | |
return c.json({ error: 'Image not found' }, 404); | |
} catch (error) { | |
console.error('Full image error:', error); | |
return c.json({ | |
error: 'Failed to fetch image', | |
message: error instanceof Error ? error.message : 'Unknown error' | |
}, 500); | |
} | |
}); | |
// Query suggestions endpoint - /suggestions | |
backendApi.get('/suggestions', async (c) => { | |
try { | |
const query = c.req.query('query') || ''; | |
// Static suggestions for now | |
const staticSuggestions = [ | |
'linqto bankruptcy', | |
'linqto filing date', | |
'linqto creditors', | |
'linqto assets', | |
'linqto liabilities', | |
'linqto chapter 11', | |
'linqto docket', | |
'linqto plan', | |
'linqto disclosure statement', | |
'linqto claims', | |
]; | |
if (!query) { | |
return c.json({ suggestions: staticSuggestions.slice(0, 5) }); | |
} | |
const lowerQuery = query.toLowerCase(); | |
const filtered = staticSuggestions | |
.filter(s => s.startsWith(lowerQuery)) | |
.slice(0, 5); | |
return c.json({ suggestions: filtered }); | |
} catch (error) { | |
console.error('Suggestions error:', error); | |
return c.json({ | |
error: 'Failed to fetch suggestions', | |
suggestions: [] | |
}, 500); | |
} | |
}); | |
// Similarity maps endpoint - /get_sim_map | |
backendApi.get('/get_sim_map', async (c) => { | |
try { | |
const queryId = c.req.query('query_id'); // Note: backend expects query_id | |
const idx = c.req.query('idx'); | |
const token = c.req.query('token'); | |
const tokenIdx = c.req.query('token_idx'); // Note: backend expects token_idx | |
if (!queryId || !idx || !token || !tokenIdx) { | |
return c.json({ error: 'Missing required parameters' }, 400); | |
} | |
// Return placeholder HTML | |
const html = ` | |
<div style="padding: 20px; text-align: center;"> | |
<h3>Similarity Map</h3> | |
<p>Query: ${token}</p> | |
<p>Document: ${idx}</p> | |
<p style="color: #666;"> | |
Similarity map generation requires the ColPali model. | |
This is a placeholder for the demo. | |
</p> | |
</div> | |
`; | |
return c.html(html); | |
} catch (error) { | |
console.error('Similarity map error:', error); | |
return c.json({ | |
error: 'Failed to generate similarity map', | |
message: error instanceof Error ? error.message : 'Unknown error' | |
}, 500); | |
} | |
}); | |
// Visual RAG Chat SSE endpoint - /get-message | |
backendApi.get('/get-message', async (c) => { | |
const queryId = c.req.query('query_id'); // Note: backend expects query_id | |
const query = c.req.query('query'); | |
const docIds = c.req.query('doc_ids'); // Note: backend expects doc_ids | |
if (!queryId || !query || !docIds) { | |
return c.json({ error: 'Missing required parameters: query_id, query, doc_ids' }, 400); | |
} | |
return streamSSE(c, async (stream) => { | |
try { | |
// Mock response for now - in production this would use an LLM | |
// Extract key information from the query | |
const messages = []; | |
if (query.toLowerCase().includes('when') && query.toLowerCase().includes('file')) { | |
messages.push( | |
`I'll analyze the search results for your query: "${query}"`, | |
"", | |
"Based on the documents provided:", | |
"", | |
"**LINQTO filed for Chapter 11 bankruptcy on July 7, 2025**", | |
"", | |
"The filing was made in the United States Bankruptcy Court for the Southern District of Texas under case number 25-90186.", | |
"", | |
"Key details:", | |
"• Filing Date: July 7, 2025 (Petition Date)", | |
"• Court: Southern District of Texas", | |
"• Case Number: 25-90186", | |
"• Chapter: 11 (Reorganization)", | |
"", | |
"This is a demo response. In production, an LLM would analyze the actual document contents for more details." | |
); | |
} else { | |
messages.push( | |
`I'll analyze the search results for your query: "${query}"`, | |
"Based on the documents provided, here are the key findings:", | |
"1. LINQTO filed for Chapter 11 bankruptcy protection on July 7, 2025", | |
"2. The filing includes detailed financial statements and creditor information", | |
"3. Various claims and assets are documented in the court filings", | |
"", | |
"This is a demo response. In production, this would analyze the actual document contents using an LLM." | |
); | |
} | |
for (const msg of messages) { | |
await stream.writeSSE({ data: msg }); | |
await new Promise(resolve => setTimeout(resolve, 500)); // Simulate typing delay | |
} | |
} catch (error) { | |
console.error('Chat streaming error:', error); | |
await stream.writeSSE({ | |
event: 'error', | |
data: JSON.stringify({ | |
error: 'Streaming failed', | |
message: error instanceof Error ? error.message : 'Unknown error' | |
}), | |
}); | |
} | |
}); | |
}); | |
export { backendApi }; |