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Resume Search (doc-reader)

A resume search app that lets you upload PDF/text resumes, index them with embeddings, and ask natural-language questions to find the best candidates. Built with FastAPI, Streamlit, ChromaDB, and OpenAI.

Features

  • Upload resumes — PDF or plain text; files are chunked, embedded, and stored in ChromaDB
  • Ask questions — e.g. "Who has the most Python experience?" or "Find candidates who know DevOps"; answers are grounded in your documents with ranked candidates
  • Document library — List and download uploaded resumes (paginated)
  • Storage — Local disk by default, or S3-compatible storage when configured
  • Reset — Wipe the vector DB from the Settings tab when you need a fresh start
  • Google SSO — When configured, sign in with Google; JWT is used for API auth

Architecture

  • Backend (main.py) — FastAPI app: ingest PDF/text, embed with OpenAI text-embedding-3-small, store in ChromaDB; /ask does retrieval + GPT-4.1-mini for answers and ranked candidates
  • Frontend (ui.py) — Streamlit app: Ask tab, Resumes tab (upload + list + download), Settings (danger zone)
  • Storage (storage.py) — S3 when bucket + credentials are set; otherwise uses a local directory

Requirements

  • Python 3.10+
  • OpenAI API key

Setup

  1. Clone and create a virtual environment

    git clone <repo-url>cd doc-reader
    python -m venv .venv
    source .venv/bin/activate # Windows: .venv\Scripts\activate
    pip install -r requirements.txt
  2. Environment variables

    Create a .env in the project root:

    VariableDescription
    OPEN_API_KEYOpenAI API key (embeddings + chat)
    API_URLBackend URL for the Streamlit app (e.g. http://localhost:8000)
    FRONTEND_URLStreamlit app URL for CORS (e.g. http://localhost:8501)
    DOCUMENT_LOCAL_DIRLocal folder for uploaded files when not using S3 (e.g. document_storage)

    Optional (Google SSO):

    When set, the backend requires Google sign-in for all API routes except / and /auth/*. The frontend shows a "Sign in with Google" page when unauthenticated.

    VariableDescription
    GOOGLE_CLIENT_IDOAuth 2.0 Client ID from Google Cloud Console
    GOOGLE_CLIENT_SECRETOAuth 2.0 Client secret
    JWT_SECRETSecret used to sign session JWTs (e.g. 32+ character random string)

    Configure in Google Cloud Console: create an OAuth 2.0 Client ID (Web application), add authorized redirect URI https://<your-backend-host>/auth/google/callback (and http://localhost:8000/auth/google/callback for local dev).

    Optional (S3-compatible storage):

    VariableDescription
    DOCUMENT_BUCKETBucket name
    DOCUMENT_BUCKET_ACCESS_KEY_IDAccess key
    DOCUMENT_BUCKET_SECRET_ACCESS_KEYSecret key
    DOCUMENT_BUCKET_REGIONRegion (e.g. us-east-1)
    DOCUMENT_BUCKET_ENDPOINTCustom endpoint URL (optional)
  3. Run locally

    Terminal 1 — Backend:

    uvicorn main:app --reload

    Terminal 2 — Frontend:

    streamlit run ui.py

    Set API_URL=http://localhost:8000 and FRONTEND_URL=http://localhost:8501 in .env so the UI can call the API and CORS allows the origin.

API overview

MethodPathDescription
GET/Health / status
GET/auth/googleRedirect to Google sign-in (when SSO configured)
GET/auth/google/callbackOAuth callback; redirects to frontend with ?token=...
GET/auth/meCurrent user email/name (Bearer token required when SSO on)
POST/ingestIngest raw text ({"text": "..."})
POST/ingest_pdfIngest PDF (multipart file)
POST/askRAG Q&A ({"question": "..."}) → answer + ranked candidates + excerpts
GET/documents/listList stored document names
GET/documents/download?filename=...Download file (redirect to S3 presigned URL or file response)
POST/wipeDelete all documents from the ChromaDB collection

When Google SSO is configured, /ask, /ingest, /ingest_pdf, /documents/list, /documents/download, and /wipe require an Authorization: Bearer <token> header (or token query param for download).

Deployment (Railway)

The repo includes railway.toml defining two services:

  • backenduvicorn main:app --host 0.0.0.0 --port $PORT
  • frontendstreamlit run ui.py --server.port $PORT --server.address 0.0.0.0

Set in Railway:

  • OPEN_API_KEY
  • API_URL → public URL of the backend service
  • FRONTEND_URL → public URL of the Streamlit service (no trailing slash)
  • DOCUMENT_LOCAL_DIR or the S3 variables if you use object storage
  • For Google SSO: GOOGLE_CLIENT_ID, GOOGLE_CLIENT_SECRET, JWT_SECRET (backend only)

For production, add a /health route if your platform expects it (e.g. healthcheckPath = "/health" in railway.toml).

License

See repository license.

About

RAG-based resume reader

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all
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(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
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btn.textContent = 'Copy';
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btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
GitHub - tones31/doc-reader: RAG-based resume reader · GitHub
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Repository files navigation

Resume Search (doc-reader)

A resume search app that lets you upload PDF/text resumes, index them with embeddings, and ask natural-language questions to find the best candidates. Built with FastAPI, Streamlit, ChromaDB, and OpenAI.

Features

  • Upload resumes — PDF or plain text; files are chunked, embedded, and stored in ChromaDB
  • Ask questions — e.g. "Who has the most Python experience?" or "Find candidates who know DevOps"; answers are grounded in your documents with ranked candidates
  • Document library — List and download uploaded resumes (paginated)
  • Storage — Local disk by default, or S3-compatible storage when configured
  • Reset — Wipe the vector DB from the Settings tab when you need a fresh start
  • Google SSO — When configured, sign in with Google; JWT is used for API auth

Architecture

  • Backend (main.py) — FastAPI app: ingest PDF/text, embed with OpenAI text-embedding-3-small, store in ChromaDB; /ask does retrieval + GPT-4.1-mini for answers and ranked candidates
  • Frontend (ui.py) — Streamlit app: Ask tab, Resumes tab (upload + list + download), Settings (danger zone)
  • Storage (storage.py) — S3 when bucket + credentials are set; otherwise uses a local directory

Requirements

  • Python 3.10+
  • OpenAI API key

Setup

  1. Clone and create a virtual environment

    git clone <repo-url>cd doc-reader
    python -m venv .venv
    source .venv/bin/activate # Windows: .venv\Scripts\activate
    pip install -r requirements.txt
  2. Environment variables

    Create a .env in the project root:

    VariableDescription
    OPEN_API_KEYOpenAI API key (embeddings + chat)
    API_URLBackend URL for the Streamlit app (e.g. http://localhost:8000)
    FRONTEND_URLStreamlit app URL for CORS (e.g. http://localhost:8501)
    DOCUMENT_LOCAL_DIRLocal folder for uploaded files when not using S3 (e.g. document_storage)

    Optional (Google SSO):

    When set, the backend requires Google sign-in for all API routes except / and /auth/*. The frontend shows a "Sign in with Google" page when unauthenticated.

    VariableDescription
    GOOGLE_CLIENT_IDOAuth 2.0 Client ID from Google Cloud Console
    GOOGLE_CLIENT_SECRETOAuth 2.0 Client secret
    JWT_SECRETSecret used to sign session JWTs (e.g. 32+ character random string)

    Configure in Google Cloud Console: create an OAuth 2.0 Client ID (Web application), add authorized redirect URI https://<your-backend-host>/auth/google/callback (and http://localhost:8000/auth/google/callback for local dev).

    Optional (S3-compatible storage):

    VariableDescription
    DOCUMENT_BUCKETBucket name
    DOCUMENT_BUCKET_ACCESS_KEY_IDAccess key
    DOCUMENT_BUCKET_SECRET_ACCESS_KEYSecret key
    DOCUMENT_BUCKET_REGIONRegion (e.g. us-east-1)
    DOCUMENT_BUCKET_ENDPOINTCustom endpoint URL (optional)
  3. Run locally

    Terminal 1 — Backend:

    uvicorn main:app --reload

    Terminal 2 — Frontend:

    streamlit run ui.py

    Set API_URL=http://localhost:8000 and FRONTEND_URL=http://localhost:8501 in .env so the UI can call the API and CORS allows the origin.

API overview

MethodPathDescription
GET/Health / status
GET/auth/googleRedirect to Google sign-in (when SSO configured)
GET/auth/google/callbackOAuth callback; redirects to frontend with ?token=...
GET/auth/meCurrent user email/name (Bearer token required when SSO on)
POST/ingestIngest raw text ({"text": "..."})
POST/ingest_pdfIngest PDF (multipart file)
POST/askRAG Q&A ({"question": "..."}) → answer + ranked candidates + excerpts
GET/documents/listList stored document names
GET/documents/download?filename=...Download file (redirect to S3 presigned URL or file response)
POST/wipeDelete all documents from the ChromaDB collection

When Google SSO is configured, /ask, /ingest, /ingest_pdf, /documents/list, /documents/download, and /wipe require an Authorization: Bearer <token> header (or token query param for download).

Deployment (Railway)

The repo includes railway.toml defining two services:

  • backenduvicorn main:app --host 0.0.0.0 --port $PORT
  • frontendstreamlit run ui.py --server.port $PORT --server.address 0.0.0.0

Set in Railway:

  • OPEN_API_KEY
  • API_URL → public URL of the backend service
  • FRONTEND_URL → public URL of the Streamlit service (no trailing slash)
  • DOCUMENT_LOCAL_DIR or the S3 variables if you use object storage
  • For Google SSO: GOOGLE_CLIENT_ID, GOOGLE_CLIENT_SECRET, JWT_SECRET (backend only)

For production, add a /health route if your platform expects it (e.g. healthcheckPath = "/health" in railway.toml).

License

See repository license.

About

RAG-based resume reader

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Force GitHub README to respect dark mode (function() { var style = document.createElement('style'); style.textContent = ' .markdown-body { color-scheme: dark light; } .markdown-body pre { background: #161b22 !important; } .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; } .markdown-body table th, .markdown-body table td { border-color: #30363d !important; } .markdown-body img { background: #0d1117; } .markdown-body blockquote { border-left-color: #8b949e; } .markdown-body hr { border-color: #30363d; } '; document.head.appendChild(style); })(); } } catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - tones31/doc-reader: RAG-based resume reader · GitHub
Skip to content

Repository files navigation

Resume Search (doc-reader)

A resume search app that lets you upload PDF/text resumes, index them with embeddings, and ask natural-language questions to find the best candidates. Built with FastAPI, Streamlit, ChromaDB, and OpenAI.

Features

  • Upload resumes — PDF or plain text; files are chunked, embedded, and stored in ChromaDB
  • Ask questions — e.g. "Who has the most Python experience?" or "Find candidates who know DevOps"; answers are grounded in your documents with ranked candidates
  • Document library — List and download uploaded resumes (paginated)
  • Storage — Local disk by default, or S3-compatible storage when configured
  • Reset — Wipe the vector DB from the Settings tab when you need a fresh start
  • Google SSO — When configured, sign in with Google; JWT is used for API auth

Architecture

  • Backend (main.py) — FastAPI app: ingest PDF/text, embed with OpenAI text-embedding-3-small, store in ChromaDB; /ask does retrieval + GPT-4.1-mini for answers and ranked candidates
  • Frontend (ui.py) — Streamlit app: Ask tab, Resumes tab (upload + list + download), Settings (danger zone)
  • Storage (storage.py) — S3 when bucket + credentials are set; otherwise uses a local directory

Requirements

  • Python 3.10+
  • OpenAI API key

Setup

  1. Clone and create a virtual environment

    git clone <repo-url>cd doc-reader
    python -m venv .venv
    source .venv/bin/activate # Windows: .venv\Scripts\activate
    pip install -r requirements.txt
  2. Environment variables

    Create a .env in the project root:

    VariableDescription
    OPEN_API_KEYOpenAI API key (embeddings + chat)
    API_URLBackend URL for the Streamlit app (e.g. http://localhost:8000)
    FRONTEND_URLStreamlit app URL for CORS (e.g. http://localhost:8501)
    DOCUMENT_LOCAL_DIRLocal folder for uploaded files when not using S3 (e.g. document_storage)

    Optional (Google SSO):

    When set, the backend requires Google sign-in for all API routes except / and /auth/*. The frontend shows a "Sign in with Google" page when unauthenticated.

    VariableDescription
    GOOGLE_CLIENT_IDOAuth 2.0 Client ID from Google Cloud Console
    GOOGLE_CLIENT_SECRETOAuth 2.0 Client secret
    JWT_SECRETSecret used to sign session JWTs (e.g. 32+ character random string)

    Configure in Google Cloud Console: create an OAuth 2.0 Client ID (Web application), add authorized redirect URI https://<your-backend-host>/auth/google/callback (and http://localhost:8000/auth/google/callback for local dev).

    Optional (S3-compatible storage):

    VariableDescription
    DOCUMENT_BUCKETBucket name
    DOCUMENT_BUCKET_ACCESS_KEY_IDAccess key
    DOCUMENT_BUCKET_SECRET_ACCESS_KEYSecret key
    DOCUMENT_BUCKET_REGIONRegion (e.g. us-east-1)
    DOCUMENT_BUCKET_ENDPOINTCustom endpoint URL (optional)
  3. Run locally

    Terminal 1 — Backend:

    uvicorn main:app --reload

    Terminal 2 — Frontend:

    streamlit run ui.py

    Set API_URL=http://localhost:8000 and FRONTEND_URL=http://localhost:8501 in .env so the UI can call the API and CORS allows the origin.

API overview

MethodPathDescription
GET/Health / status
GET/auth/googleRedirect to Google sign-in (when SSO configured)
GET/auth/google/callbackOAuth callback; redirects to frontend with ?token=...
GET/auth/meCurrent user email/name (Bearer token required when SSO on)
POST/ingestIngest raw text ({"text": "..."})
POST/ingest_pdfIngest PDF (multipart file)
POST/askRAG Q&A ({"question": "..."}) → answer + ranked candidates + excerpts
GET/documents/listList stored document names
GET/documents/download?filename=...Download file (redirect to S3 presigned URL or file response)
POST/wipeDelete all documents from the ChromaDB collection

When Google SSO is configured, /ask, /ingest, /ingest_pdf, /documents/list, /documents/download, and /wipe require an Authorization: Bearer <token> header (or token query param for download).

Deployment (Railway)

The repo includes railway.toml defining two services:

  • backenduvicorn main:app --host 0.0.0.0 --port $PORT
  • frontendstreamlit run ui.py --server.port $PORT --server.address 0.0.0.0

Set in Railway:

  • OPEN_API_KEY
  • API_URL → public URL of the backend service
  • FRONTEND_URL → public URL of the Streamlit service (no trailing slash)
  • DOCUMENT_LOCAL_DIR or the S3 variables if you use object storage
  • For Google SSO: GOOGLE_CLIENT_ID, GOOGLE_CLIENT_SECRET, JWT_SECRET (backend only)

For production, add a /health route if your platform expects it (e.g. healthcheckPath = "/health" in railway.toml).

License

See repository license.

About

RAG-based resume reader

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Highlight search terms from Google/DuckDuckGo/Bing referrer (function() { var ref = document.referrer; var terms = []; if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) { var url = new URL(ref); var q = url.searchParams.get('q') || url.searchParams.get('p'); if (q) { terms = q.split(/\s+/).filter(function(t) { return t.length > 2; }); } } if (terms.length === 0) return; var style = document.createElement('style'); style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }'; document.head.appendChild(style); function highlight(node) { if (node.nodeType === 3) { // text node var text = node.textContent; var found = false; terms.forEach(function(term) { var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi'); if (regex.test(text)) { found = true; var frag = document.createDocumentFragment(); var parts = text.split(regex); parts.forEach(function(part, i) { if (i % 2 === 0) { frag.appendChild(document.createTextNode(part)); } else { var span = document.createElement('span'); span.className = 'userscript-highlight'; span.textContent = part; frag.appendChild(span); } }); node.parentNode.replaceChild(frag, node); } }); } else if (node.nodeType === 1 && node.childNodes) { // element var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT']; if (!skipTags.includes(node.tagName)) { Array.from(node.childNodes).forEach(highlight); } } } highlight(document.body); // Re-highlight on dynamic content var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1 || node.nodeType === 3) highlight(node); }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - tones31/doc-reader: RAG-based resume reader · GitHub
Skip to content

Repository files navigation

Resume Search (doc-reader)

A resume search app that lets you upload PDF/text resumes, index them with embeddings, and ask natural-language questions to find the best candidates. Built with FastAPI, Streamlit, ChromaDB, and OpenAI.

Features

  • Upload resumes — PDF or plain text; files are chunked, embedded, and stored in ChromaDB
  • Ask questions — e.g. "Who has the most Python experience?" or "Find candidates who know DevOps"; answers are grounded in your documents with ranked candidates
  • Document library — List and download uploaded resumes (paginated)
  • Storage — Local disk by default, or S3-compatible storage when configured
  • Reset — Wipe the vector DB from the Settings tab when you need a fresh start
  • Google SSO — When configured, sign in with Google; JWT is used for API auth

Architecture

  • Backend (main.py) — FastAPI app: ingest PDF/text, embed with OpenAI text-embedding-3-small, store in ChromaDB; /ask does retrieval + GPT-4.1-mini for answers and ranked candidates
  • Frontend (ui.py) — Streamlit app: Ask tab, Resumes tab (upload + list + download), Settings (danger zone)
  • Storage (storage.py) — S3 when bucket + credentials are set; otherwise uses a local directory

Requirements

  • Python 3.10+
  • OpenAI API key

Setup

  1. Clone and create a virtual environment

    git clone <repo-url>cd doc-reader
    python -m venv .venv
    source .venv/bin/activate # Windows: .venv\Scripts\activate
    pip install -r requirements.txt
  2. Environment variables

    Create a .env in the project root:

    VariableDescription
    OPEN_API_KEYOpenAI API key (embeddings + chat)
    API_URLBackend URL for the Streamlit app (e.g. http://localhost:8000)
    FRONTEND_URLStreamlit app URL for CORS (e.g. http://localhost:8501)
    DOCUMENT_LOCAL_DIRLocal folder for uploaded files when not using S3 (e.g. document_storage)

    Optional (Google SSO):

    When set, the backend requires Google sign-in for all API routes except / and /auth/*. The frontend shows a "Sign in with Google" page when unauthenticated.

    VariableDescription
    GOOGLE_CLIENT_IDOAuth 2.0 Client ID from Google Cloud Console
    GOOGLE_CLIENT_SECRETOAuth 2.0 Client secret
    JWT_SECRETSecret used to sign session JWTs (e.g. 32+ character random string)

    Configure in Google Cloud Console: create an OAuth 2.0 Client ID (Web application), add authorized redirect URI https://<your-backend-host>/auth/google/callback (and http://localhost:8000/auth/google/callback for local dev).

    Optional (S3-compatible storage):

    VariableDescription
    DOCUMENT_BUCKETBucket name
    DOCUMENT_BUCKET_ACCESS_KEY_IDAccess key
    DOCUMENT_BUCKET_SECRET_ACCESS_KEYSecret key
    DOCUMENT_BUCKET_REGIONRegion (e.g. us-east-1)
    DOCUMENT_BUCKET_ENDPOINTCustom endpoint URL (optional)
  3. Run locally

    Terminal 1 — Backend:

    uvicorn main:app --reload

    Terminal 2 — Frontend:

    streamlit run ui.py

    Set API_URL=http://localhost:8000 and FRONTEND_URL=http://localhost:8501 in .env so the UI can call the API and CORS allows the origin.

API overview

MethodPathDescription
GET/Health / status
GET/auth/googleRedirect to Google sign-in (when SSO configured)
GET/auth/google/callbackOAuth callback; redirects to frontend with ?token=...
GET/auth/meCurrent user email/name (Bearer token required when SSO on)
POST/ingestIngest raw text ({"text": "..."})
POST/ingest_pdfIngest PDF (multipart file)
POST/askRAG Q&A ({"question": "..."}) → answer + ranked candidates + excerpts
GET/documents/listList stored document names
GET/documents/download?filename=...Download file (redirect to S3 presigned URL or file response)
POST/wipeDelete all documents from the ChromaDB collection

When Google SSO is configured, /ask, /ingest, /ingest_pdf, /documents/list, /documents/download, and /wipe require an Authorization: Bearer <token> header (or token query param for download).

Deployment (Railway)

The repo includes railway.toml defining two services:

  • backenduvicorn main:app --host 0.0.0.0 --port $PORT
  • frontendstreamlit run ui.py --server.port $PORT --server.address 0.0.0.0

Set in Railway:

  • OPEN_API_KEY
  • API_URL → public URL of the backend service
  • FRONTEND_URL → public URL of the Streamlit service (no trailing slash)
  • DOCUMENT_LOCAL_DIR or the S3 variables if you use object storage
  • For Google SSO: GOOGLE_CLIENT_ID, GOOGLE_CLIENT_SECRET, JWT_SECRET (backend only)

For production, add a /health route if your platform expects it (e.g. healthcheckPath = "/health" in railway.toml).

License

See repository license.

About

RAG-based resume reader

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Strip utm_, fbclid, gclid, etc. from all links on page (function() { var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content', 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid', 'ref', 'ref_src', 'source', 'medium', 'campaign']; function cleanUrl(url) { try { var u = new URL(url, window.location.origin); var changed = false; trackingParams.forEach(function(p) { if (u.searchParams.has(p)) { u.searchParams.delete(p); changed = true; } }); return changed ? u.toString() : url; } catch (e) { return url; } } function cleanLinks() { document.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } cleanLinks(); var observer = new MutationObserver(function(mutations) { mutations.forEach(function(m) { m.addedNodes.forEach(function(node) { if (node.nodeType === 1) { if (node.tagName === 'A') cleanLinks(); node.querySelectorAll('a[href]').forEach(function(a) { var clean = cleanUrl(a.href); if (clean !== a.href) a.href = clean; }); } }); }); }); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); } })(); (function(){ try { var __m = "youtube.com"; var __re = new RegExp('^' + "youtube\\.com" + ' GitHub - tones31/doc-reader: RAG-based resume reader · GitHub
Skip to content

Repository files navigation

Resume Search (doc-reader)

A resume search app that lets you upload PDF/text resumes, index them with embeddings, and ask natural-language questions to find the best candidates. Built with FastAPI, Streamlit, ChromaDB, and OpenAI.

Features

  • Upload resumes — PDF or plain text; files are chunked, embedded, and stored in ChromaDB
  • Ask questions — e.g. "Who has the most Python experience?" or "Find candidates who know DevOps"; answers are grounded in your documents with ranked candidates
  • Document library — List and download uploaded resumes (paginated)
  • Storage — Local disk by default, or S3-compatible storage when configured
  • Reset — Wipe the vector DB from the Settings tab when you need a fresh start
  • Google SSO — When configured, sign in with Google; JWT is used for API auth

Architecture

  • Backend (main.py) — FastAPI app: ingest PDF/text, embed with OpenAI text-embedding-3-small, store in ChromaDB; /ask does retrieval + GPT-4.1-mini for answers and ranked candidates
  • Frontend (ui.py) — Streamlit app: Ask tab, Resumes tab (upload + list + download), Settings (danger zone)
  • Storage (storage.py) — S3 when bucket + credentials are set; otherwise uses a local directory

Requirements

  • Python 3.10+
  • OpenAI API key

Setup

  1. Clone and create a virtual environment

    git clone <repo-url>cd doc-reader
    python -m venv .venv
    source .venv/bin/activate # Windows: .venv\Scripts\activate
    pip install -r requirements.txt
  2. Environment variables

    Create a .env in the project root:

    VariableDescription
    OPEN_API_KEYOpenAI API key (embeddings + chat)
    API_URLBackend URL for the Streamlit app (e.g. http://localhost:8000)
    FRONTEND_URLStreamlit app URL for CORS (e.g. http://localhost:8501)
    DOCUMENT_LOCAL_DIRLocal folder for uploaded files when not using S3 (e.g. document_storage)

    Optional (Google SSO):

    When set, the backend requires Google sign-in for all API routes except / and /auth/*. The frontend shows a "Sign in with Google" page when unauthenticated.

    VariableDescription
    GOOGLE_CLIENT_IDOAuth 2.0 Client ID from Google Cloud Console
    GOOGLE_CLIENT_SECRETOAuth 2.0 Client secret
    JWT_SECRETSecret used to sign session JWTs (e.g. 32+ character random string)

    Configure in Google Cloud Console: create an OAuth 2.0 Client ID (Web application), add authorized redirect URI https://<your-backend-host>/auth/google/callback (and http://localhost:8000/auth/google/callback for local dev).

    Optional (S3-compatible storage):

    VariableDescription
    DOCUMENT_BUCKETBucket name
    DOCUMENT_BUCKET_ACCESS_KEY_IDAccess key
    DOCUMENT_BUCKET_SECRET_ACCESS_KEYSecret key
    DOCUMENT_BUCKET_REGIONRegion (e.g. us-east-1)
    DOCUMENT_BUCKET_ENDPOINTCustom endpoint URL (optional)
  3. Run locally

    Terminal 1 — Backend:

    uvicorn main:app --reload

    Terminal 2 — Frontend:

    streamlit run ui.py

    Set API_URL=http://localhost:8000 and FRONTEND_URL=http://localhost:8501 in .env so the UI can call the API and CORS allows the origin.

API overview

MethodPathDescription
GET/Health / status
GET/auth/googleRedirect to Google sign-in (when SSO configured)
GET/auth/google/callbackOAuth callback; redirects to frontend with ?token=...
GET/auth/meCurrent user email/name (Bearer token required when SSO on)
POST/ingestIngest raw text ({"text": "..."})
POST/ingest_pdfIngest PDF (multipart file)
POST/askRAG Q&A ({"question": "..."}) → answer + ranked candidates + excerpts
GET/documents/listList stored document names
GET/documents/download?filename=...Download file (redirect to S3 presigned URL or file response)
POST/wipeDelete all documents from the ChromaDB collection

When Google SSO is configured, /ask, /ingest, /ingest_pdf, /documents/list, /documents/download, and /wipe require an Authorization: Bearer <token> header (or token query param for download).

Deployment (Railway)

The repo includes railway.toml defining two services:

  • backenduvicorn main:app --host 0.0.0.0 --port $PORT
  • frontendstreamlit run ui.py --server.port $PORT --server.address 0.0.0.0

Set in Railway:

  • OPEN_API_KEY
  • API_URL → public URL of the backend service
  • FRONTEND_URL → public URL of the Streamlit service (no trailing slash)
  • DOCUMENT_LOCAL_DIR or the S3 variables if you use object storage
  • For Google SSO: GOOGLE_CLIENT_ID, GOOGLE_CLIENT_SECRET, JWT_SECRET (backend only)

For production, add a /health route if your platform expects it (e.g. healthcheckPath = "/health" in railway.toml).

License

See repository license.

About

RAG-based resume reader

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, 'i'); if (__m === '*' || __re.test(location.href)) { // Auto-enable theater mode on YouTube (function() { function tryTheater() { var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]'); if (btn && !btn.classList.contains('activated')) { btn.click(); } } // Try immediately tryTheater(); // Try after navigation (SPA) var lastUrl = location.href; setInterval(function() { if (location.href !== lastUrl) { lastUrl = location.href; setTimeout(tryTheater, 500); } }, 1000); // Also try on player load var observer = new MutationObserver(tryTheater); observer.observe(document.body, { childList: true, subtree: true }); })(); } } catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - tones31/doc-reader: RAG-based resume reader · GitHub
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Resume Search (doc-reader)

A resume search app that lets you upload PDF/text resumes, index them with embeddings, and ask natural-language questions to find the best candidates. Built with FastAPI, Streamlit, ChromaDB, and OpenAI.

Features

  • Upload resumes — PDF or plain text; files are chunked, embedded, and stored in ChromaDB
  • Ask questions — e.g. "Who has the most Python experience?" or "Find candidates who know DevOps"; answers are grounded in your documents with ranked candidates
  • Document library — List and download uploaded resumes (paginated)
  • Storage — Local disk by default, or S3-compatible storage when configured
  • Reset — Wipe the vector DB from the Settings tab when you need a fresh start
  • Google SSO — When configured, sign in with Google; JWT is used for API auth

Architecture

  • Backend (main.py) — FastAPI app: ingest PDF/text, embed with OpenAI text-embedding-3-small, store in ChromaDB; /ask does retrieval + GPT-4.1-mini for answers and ranked candidates
  • Frontend (ui.py) — Streamlit app: Ask tab, Resumes tab (upload + list + download), Settings (danger zone)
  • Storage (storage.py) — S3 when bucket + credentials are set; otherwise uses a local directory

Requirements

  • Python 3.10+
  • OpenAI API key

Setup

  1. Clone and create a virtual environment

    git clone <repo-url>cd doc-reader
    python -m venv .venv
    source .venv/bin/activate # Windows: .venv\Scripts\activate
    pip install -r requirements.txt
  2. Environment variables

    Create a .env in the project root:

    VariableDescription
    OPEN_API_KEYOpenAI API key (embeddings + chat)
    API_URLBackend URL for the Streamlit app (e.g. http://localhost:8000)
    FRONTEND_URLStreamlit app URL for CORS (e.g. http://localhost:8501)
    DOCUMENT_LOCAL_DIRLocal folder for uploaded files when not using S3 (e.g. document_storage)

    Optional (Google SSO):

    When set, the backend requires Google sign-in for all API routes except / and /auth/*. The frontend shows a "Sign in with Google" page when unauthenticated.

    VariableDescription
    GOOGLE_CLIENT_IDOAuth 2.0 Client ID from Google Cloud Console
    GOOGLE_CLIENT_SECRETOAuth 2.0 Client secret
    JWT_SECRETSecret used to sign session JWTs (e.g. 32+ character random string)

    Configure in Google Cloud Console: create an OAuth 2.0 Client ID (Web application), add authorized redirect URI https://<your-backend-host>/auth/google/callback (and http://localhost:8000/auth/google/callback for local dev).

    Optional (S3-compatible storage):

    VariableDescription
    DOCUMENT_BUCKETBucket name
    DOCUMENT_BUCKET_ACCESS_KEY_IDAccess key
    DOCUMENT_BUCKET_SECRET_ACCESS_KEYSecret key
    DOCUMENT_BUCKET_REGIONRegion (e.g. us-east-1)
    DOCUMENT_BUCKET_ENDPOINTCustom endpoint URL (optional)
  3. Run locally

    Terminal 1 — Backend:

    uvicorn main:app --reload

    Terminal 2 — Frontend:

    streamlit run ui.py

    Set API_URL=http://localhost:8000 and FRONTEND_URL=http://localhost:8501 in .env so the UI can call the API and CORS allows the origin.

API overview

MethodPathDescription
GET/Health / status
GET/auth/googleRedirect to Google sign-in (when SSO configured)
GET/auth/google/callbackOAuth callback; redirects to frontend with ?token=...
GET/auth/meCurrent user email/name (Bearer token required when SSO on)
POST/ingestIngest raw text ({"text": "..."})
POST/ingest_pdfIngest PDF (multipart file)
POST/askRAG Q&A ({"question": "..."}) → answer + ranked candidates + excerpts
GET/documents/listList stored document names
GET/documents/download?filename=...Download file (redirect to S3 presigned URL or file response)
POST/wipeDelete all documents from the ChromaDB collection

When Google SSO is configured, /ask, /ingest, /ingest_pdf, /documents/list, /documents/download, and /wipe require an Authorization: Bearer <token> header (or token query param for download).

Deployment (Railway)

The repo includes railway.toml defining two services:

  • backenduvicorn main:app --host 0.0.0.0 --port $PORT
  • frontendstreamlit run ui.py --server.port $PORT --server.address 0.0.0.0

Set in Railway:

  • OPEN_API_KEY
  • API_URL → public URL of the backend service
  • FRONTEND_URL → public URL of the Streamlit service (no trailing slash)
  • DOCUMENT_LOCAL_DIR or the S3 variables if you use object storage
  • For Google SSO: GOOGLE_CLIENT_ID, GOOGLE_CLIENT_SECRET, JWT_SECRET (backend only)

For production, add a /health route if your platform expects it (e.g. healthcheckPath = "/health" in railway.toml).

License

See repository license.

About

RAG-based resume reader

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Used by

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Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Remove or un-stick sticky/fixed headers that block content (function() { function unstick() { document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) { if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') { el.style.position = 'static'; el.style.top = 'auto'; el.style.zIndex = 'auto'; } }); } unstick(); var observer = new MutationObserver(unstick); observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] }); })(); } } catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); } })(); (function(){ try { var __m = "*"; var __re = new RegExp('^' + ".*" + ' GitHub - tones31/doc-reader: RAG-based resume reader · GitHub
Skip to content

Repository files navigation

Resume Search (doc-reader)

A resume search app that lets you upload PDF/text resumes, index them with embeddings, and ask natural-language questions to find the best candidates. Built with FastAPI, Streamlit, ChromaDB, and OpenAI.

Features

  • Upload resumes — PDF or plain text; files are chunked, embedded, and stored in ChromaDB
  • Ask questions — e.g. "Who has the most Python experience?" or "Find candidates who know DevOps"; answers are grounded in your documents with ranked candidates
  • Document library — List and download uploaded resumes (paginated)
  • Storage — Local disk by default, or S3-compatible storage when configured
  • Reset — Wipe the vector DB from the Settings tab when you need a fresh start
  • Google SSO — When configured, sign in with Google; JWT is used for API auth

Architecture

  • Backend (main.py) — FastAPI app: ingest PDF/text, embed with OpenAI text-embedding-3-small, store in ChromaDB; /ask does retrieval + GPT-4.1-mini for answers and ranked candidates
  • Frontend (ui.py) — Streamlit app: Ask tab, Resumes tab (upload + list + download), Settings (danger zone)
  • Storage (storage.py) — S3 when bucket + credentials are set; otherwise uses a local directory

Requirements

  • Python 3.10+
  • OpenAI API key

Setup

  1. Clone and create a virtual environment

    git clone <repo-url>cd doc-reader
    python -m venv .venv
    source .venv/bin/activate # Windows: .venv\Scripts\activate
    pip install -r requirements.txt
  2. Environment variables

    Create a .env in the project root:

    VariableDescription
    OPEN_API_KEYOpenAI API key (embeddings + chat)
    API_URLBackend URL for the Streamlit app (e.g. http://localhost:8000)
    FRONTEND_URLStreamlit app URL for CORS (e.g. http://localhost:8501)
    DOCUMENT_LOCAL_DIRLocal folder for uploaded files when not using S3 (e.g. document_storage)

    Optional (Google SSO):

    When set, the backend requires Google sign-in for all API routes except / and /auth/*. The frontend shows a "Sign in with Google" page when unauthenticated.

    VariableDescription
    GOOGLE_CLIENT_IDOAuth 2.0 Client ID from Google Cloud Console
    GOOGLE_CLIENT_SECRETOAuth 2.0 Client secret
    JWT_SECRETSecret used to sign session JWTs (e.g. 32+ character random string)

    Configure in Google Cloud Console: create an OAuth 2.0 Client ID (Web application), add authorized redirect URI https://<your-backend-host>/auth/google/callback (and http://localhost:8000/auth/google/callback for local dev).

    Optional (S3-compatible storage):

    VariableDescription
    DOCUMENT_BUCKETBucket name
    DOCUMENT_BUCKET_ACCESS_KEY_IDAccess key
    DOCUMENT_BUCKET_SECRET_ACCESS_KEYSecret key
    DOCUMENT_BUCKET_REGIONRegion (e.g. us-east-1)
    DOCUMENT_BUCKET_ENDPOINTCustom endpoint URL (optional)
  3. Run locally

    Terminal 1 — Backend:

    uvicorn main:app --reload

    Terminal 2 — Frontend:

    streamlit run ui.py

    Set API_URL=http://localhost:8000 and FRONTEND_URL=http://localhost:8501 in .env so the UI can call the API and CORS allows the origin.

API overview

MethodPathDescription
GET/Health / status
GET/auth/googleRedirect to Google sign-in (when SSO configured)
GET/auth/google/callbackOAuth callback; redirects to frontend with ?token=...
GET/auth/meCurrent user email/name (Bearer token required when SSO on)
POST/ingestIngest raw text ({"text": "..."})
POST/ingest_pdfIngest PDF (multipart file)
POST/askRAG Q&A ({"question": "..."}) → answer + ranked candidates + excerpts
GET/documents/listList stored document names
GET/documents/download?filename=...Download file (redirect to S3 presigned URL or file response)
POST/wipeDelete all documents from the ChromaDB collection

When Google SSO is configured, /ask, /ingest, /ingest_pdf, /documents/list, /documents/download, and /wipe require an Authorization: Bearer <token> header (or token query param for download).

Deployment (Railway)

The repo includes railway.toml defining two services:

  • backenduvicorn main:app --host 0.0.0.0 --port $PORT
  • frontendstreamlit run ui.py --server.port $PORT --server.address 0.0.0.0

Set in Railway:

  • OPEN_API_KEY
  • API_URL → public URL of the backend service
  • FRONTEND_URL → public URL of the Streamlit service (no trailing slash)
  • DOCUMENT_LOCAL_DIR or the S3 variables if you use object storage
  • For Google SSO: GOOGLE_CLIENT_ID, GOOGLE_CLIENT_SECRET, JWT_SECRET (backend only)

For production, add a /health route if your platform expects it (e.g. healthcheckPath = "/health" in railway.toml).

License

See repository license.

About

RAG-based resume reader

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { // Universal Dark Mode - works on any site (function() { var enabled = true; function applyDarkMode() { if (!enabled) return; // Create style element if it doesn't exist var style = document.getElementById('universal-dark-mode-style'); if (!style) { style = document.createElement('style'); style.id = 'universal-dark-mode-style'; document.head.appendChild(style); } // Dark mode CSS - inverts colors but preserves images/video style.textContent = ' /* Invert everything except media */ html { filter: invert(1) hue-rotate(180deg) !important; background: #1a1a2e !important; } /* Restore images, videos, iframes, canvas */ img, video, iframe, canvas, svg, picture, [style*="background-image"] { filter: invert(1) hue-rotate(180deg) !important; } /* Preserve specific elements that should not be inverted */ .no-dark-mode, .no-dark-mode *, [data-theme="light"], [data-theme="light"], .ace_editor, .ace_editor *, .CodeMirror, .CodeMirror *, .monaco-editor, .monaco-editor *, .markdown-body pre, .markdown-body pre *, .highlight, .highlight *, pre code, pre code * { filter: none !important; } /* Fix common UI elements */ .modal, .popup, .dropdown-menu, .tooltip, .popover { filter: invert(1) hue-rotate(180deg) !important; background: #2d2d44 !important; border-color: #444 !important; } /* Scrollbars */ ::-webkit-scrollbar { background: #1a1a2e !important; } ::-webkit-scrollbar-thumb { background: #444 !important; } ::-webkit-scrollbar-thumb:hover { background: #555 !important; } /* Selection */ ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; } ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; } '; } function removeDarkMode() { var style = document.getElementById('universal-dark-mode-style'); if (style) style.remove(); } // Toggle with Alt+Shift+D document.addEventListener('keydown', function(e) { if (e.altKey && e.shiftKey && e.key === 'D') { e.preventDefault(); enabled = !enabled; if (enabled) { applyDarkMode(); console.log('[Universal Dark Mode] Enabled'); } else { removeDarkMode(); console.log('[Universal Dark Mode] Disabled'); } } }); // Apply on load applyDarkMode(); // Re-apply on dynamic content var observer = new MutationObserver(function(mutations) { if (enabled && !document.getElementById('universal-dark-mode-style')) { applyDarkMode(); } }); observer.observe(document.head, { childList: true }); console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle'); })(); } } catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); } })(); })(); GitHub - tones31/doc-reader: RAG-based resume reader · GitHub
Skip to content

Repository files navigation

Resume Search (doc-reader)

A resume search app that lets you upload PDF/text resumes, index them with embeddings, and ask natural-language questions to find the best candidates. Built with FastAPI, Streamlit, ChromaDB, and OpenAI.

Features

  • Upload resumes — PDF or plain text; files are chunked, embedded, and stored in ChromaDB
  • Ask questions — e.g. "Who has the most Python experience?" or "Find candidates who know DevOps"; answers are grounded in your documents with ranked candidates
  • Document library — List and download uploaded resumes (paginated)
  • Storage — Local disk by default, or S3-compatible storage when configured
  • Reset — Wipe the vector DB from the Settings tab when you need a fresh start
  • Google SSO — When configured, sign in with Google; JWT is used for API auth

Architecture

  • Backend (main.py) — FastAPI app: ingest PDF/text, embed with OpenAI text-embedding-3-small, store in ChromaDB; /ask does retrieval + GPT-4.1-mini for answers and ranked candidates
  • Frontend (ui.py) — Streamlit app: Ask tab, Resumes tab (upload + list + download), Settings (danger zone)
  • Storage (storage.py) — S3 when bucket + credentials are set; otherwise uses a local directory

Requirements

  • Python 3.10+
  • OpenAI API key

Setup

  1. Clone and create a virtual environment

    git clone <repo-url>cd doc-reader
    python -m venv .venv
    source .venv/bin/activate # Windows: .venv\Scripts\activate
    pip install -r requirements.txt
  2. Environment variables

    Create a .env in the project root:

    VariableDescription
    OPEN_API_KEYOpenAI API key (embeddings + chat)
    API_URLBackend URL for the Streamlit app (e.g. http://localhost:8000)
    FRONTEND_URLStreamlit app URL for CORS (e.g. http://localhost:8501)
    DOCUMENT_LOCAL_DIRLocal folder for uploaded files when not using S3 (e.g. document_storage)

    Optional (Google SSO):

    When set, the backend requires Google sign-in for all API routes except / and /auth/*. The frontend shows a "Sign in with Google" page when unauthenticated.

    VariableDescription
    GOOGLE_CLIENT_IDOAuth 2.0 Client ID from Google Cloud Console
    GOOGLE_CLIENT_SECRETOAuth 2.0 Client secret
    JWT_SECRETSecret used to sign session JWTs (e.g. 32+ character random string)

    Configure in Google Cloud Console: create an OAuth 2.0 Client ID (Web application), add authorized redirect URI https://<your-backend-host>/auth/google/callback (and http://localhost:8000/auth/google/callback for local dev).

    Optional (S3-compatible storage):

    VariableDescription
    DOCUMENT_BUCKETBucket name
    DOCUMENT_BUCKET_ACCESS_KEY_IDAccess key
    DOCUMENT_BUCKET_SECRET_ACCESS_KEYSecret key
    DOCUMENT_BUCKET_REGIONRegion (e.g. us-east-1)
    DOCUMENT_BUCKET_ENDPOINTCustom endpoint URL (optional)
  3. Run locally

    Terminal 1 — Backend:

    uvicorn main:app --reload

    Terminal 2 — Frontend:

    streamlit run ui.py

    Set API_URL=http://localhost:8000 and FRONTEND_URL=http://localhost:8501 in .env so the UI can call the API and CORS allows the origin.

API overview

MethodPathDescription
GET/Health / status
GET/auth/googleRedirect to Google sign-in (when SSO configured)
GET/auth/google/callbackOAuth callback; redirects to frontend with ?token=...
GET/auth/meCurrent user email/name (Bearer token required when SSO on)
POST/ingestIngest raw text ({"text": "..."})
POST/ingest_pdfIngest PDF (multipart file)
POST/askRAG Q&A ({"question": "..."}) → answer + ranked candidates + excerpts
GET/documents/listList stored document names
GET/documents/download?filename=...Download file (redirect to S3 presigned URL or file response)
POST/wipeDelete all documents from the ChromaDB collection

When Google SSO is configured, /ask, /ingest, /ingest_pdf, /documents/list, /documents/download, and /wipe require an Authorization: Bearer <token> header (or token query param for download).

Deployment (Railway)

The repo includes railway.toml defining two services:

  • backenduvicorn main:app --host 0.0.0.0 --port $PORT
  • frontendstreamlit run ui.py --server.port $PORT --server.address 0.0.0.0

Set in Railway:

  • OPEN_API_KEY
  • API_URL → public URL of the backend service
  • FRONTEND_URL → public URL of the Streamlit service (no trailing slash)
  • DOCUMENT_LOCAL_DIR or the S3 variables if you use object storage
  • For Google SSO: GOOGLE_CLIENT_ID, GOOGLE_CLIENT_SECRET, JWT_SECRET (backend only)

For production, add a /health route if your platform expects it (e.g. healthcheckPath = "/health" in railway.toml).

License

See repository license.

About

RAG-based resume reader

Resources

Stars

0 stars

Watchers

0 watching

Forks

Releases

Packages

Used by

Contributors

Languages