Summary
Website Quality Score
Performance 10.0
SEO 6.0
Security 6.5
Accessibility 5.0
Best Practices 6.1
- ⛔ Skipped URLs - 397 skipped URLs found.
- ⛔ 17 page(s) with multiple <h1> headings.
- ⛔ 1 page(s) without <h1> heading.
- ⛔ Security - 476 pages(s) with critical finding(s).
- ⚠️ Redirects - 8 redirects found.
- ⚠️ 188 page(s) do not support Brotli compression.
- ⚠️ No WebP image found on the website.
- ⚠️ No AVIF image found on the website.
- ⚠️ 2 page(s) with duplicated inline SVGs (> 5 duplicates).
- ⚠️ 167 page(s) with skipped heading levels.
- ⚠️ 9 page(s) with non-clickable (non-interactive) phone numbers.
- ⚠️ 35 page(s) without image alt attributes.
- ⚠️ 188 page(s) without aria labels.
- ⚠️ 188 page(s) without role attributes.
- ⏩ Loaded robots.txt for domain 'docs.cohere.com': status code 200, size 95 B and took 283 ms.
- ⏩ External URLs - 397 external URL(s) found.
- ⏩ DNS IPv6: domain docs.cohere.com does not support IPv6 (DNS server: 127.0.0.53).
- ✅ 404 OK - all pages exists, no non-existent pages found.
- ✅ SSL/TLS certificate is valid until May 26 21:55:33 2026 GMT. Issued by C = US, O = Let's Encrypt, CN = R12. Subject is CN = docs.cohere.com.
- ✅ SSL/TLS certificate issued by 'C = US, O = Let's Encrypt, CN = R12'.
- ✅ Performance OK - all non-media URLs are faster than 3 seconds.
- ✅ HTTP headers - found 22 unique headers.
- ✅ All 160 unique title(s) are within the allowed 10% duplicity. Highest duplicity title has 1%.
- ✅ All 162 description(s) are within the allowed 10% duplicity. Highest duplicity description has 1%.
- ✅ All pages have quoted attributes.
- ✅ All pages have inline SVGs smaller than 5120 bytes.
- ✅ All pages have valid or none inline SVGs.
- ✅ All pages have DOM depth less than 30.
- ✅ All pages have valid HTML.
- ✅ All pages have form labels.
- ✅ All pages have lang attribute.
- ✅ DNS IPv4 OK: domain docs.cohere.com resolved to cname.vercel-dns.com., 76.76.21.241, 66.33.60.193 (DNS server: 127.0.0.53).
- 📌 DNS Aliases: IP(s) for domain docs.cohere.com were resolved by CNAME chain docs.cohere.com > cname.vercel-dns.com.
Visited URLs
Found 434 row(s).
Best practices
Found 11 row(s).
| Analysis name | OK | Notice | Warning | Critical |
|---|---|---|---|---|
| Duplicate inline SVGs (> 5 and > 1024 B) | 53 | 0 | 1 | 0 |
| DOM depth (> 30) | 426 | 0 | 0 | 0 |
| Heading structure | 208 | 238 | 197 | 17 |
| Non-clickable phone numbers | 3 | 0 | 18 | 0 |
| Large inline SVGs (> 5120 B) | 54 | 0 | 0 | 0 |
| Invalid inline SVGs | 54 | 0 | 0 | 0 |
| Title uniqueness (> 10%) | 160 | 0 | 0 | 0 |
| Description uniqueness (> 10%) | 162 | 0 | 0 | 0 |
| Brotli support | 0 | 0 | 188 | 0 |
| WebP support | 0 | 0 | 1 | 0 |
| AVIF support | 0 | 0 | 1 | 0 |
| No rows found, please edit your search term. | ||||
Large inline SVGs
No problems found.
Duplicate inline SVGs
Invalid inline SVGs
No problems found.
Missing quotes on attributes
No problems found.
DOM depth
No problems found.
Heading structure
Found 14 row(s).
| Severity | Occurs | Detail | Affected URLs (max 5) |
|---|---|---|---|
| critical | 58 | Multiple <h1> headings found. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| critical | 1 | No <h1> tag found in the HTML content. | / |
| warning | 72 | Heading structure is skipping levels: found an <h4> after an <h2>. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 38 | Heading structure is skipping levels: found an <h3> after an <h1>. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 24 | Heading structure is skipping levels: found an <h5> after an <h1>. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 21 | Heading structure is skipping levels: found an <h4> after an <h1>. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 21 | Heading structure is skipping levels: found an <h6> after an <h2>. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 17 | Heading structure is skipping levels: found an <h5> after an <h2>. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 11 | Heading structure is skipping levels: found an <h6> after an <h3>. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 10 | Heading structure is skipping levels: found an <h5> after an <h3>. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 4 | Heading structure is skipping levels: found an <h6> after an <h4>. | URL 1, URL 2, URL 3 |
| warning | 1 | Heading structure is skipping levels: found an <h6> after an <h1>. | /docs/parameter-types-in-json |
| warning | 1 | Heading structure is skipping levels: found an <h2> without a previous higher heading. | / |
| notice | 238 | No headings found in the HTML content. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| No rows found, please edit your search term. | |||
Non-clickable phone numbers
Found 18 row(s).
| Severity | Occurs | Detail | Affected URLs (max 5) |
|---|---|---|---|
| warning | 4 | + 08-2024 | URL 1, URL 2, URL 3, URL 4 |
| warning | 2 | + 08 2024 | URL 1, URL 2 |
| warning | 1 | +11 104956000000 | /page/csv-agent-native-api |
| warning | 1 | +66865092 | /page/rag-with-chat-embed |
| warning | 1 | (403) 262-3443 | /page/sql-agent |
| warning | 1 | (514) 721-4711 | /page/sql-agent |
| warning | 1 | +11 101839000000 | /page/csv-agent-native-api |
| warning | 1 | +09 57411000000 | /page/csv-agent-native-api |
| warning | 1 | (403) 262-6712 | /page/sql-agent |
| warning | 1 | (780) 428-3457 | /page/sql-agent |
| warning | 1 | (403) 262-3322 | /page/sql-agent |
| warning | 1 | (12) 3923-5566 | /page/sql-agent |
| warning | 1 | (12) 3923-5555 | /page/sql-agent |
| warning | 1 | +10 59531000000 | /page/csv-agent-native-api |
| warning | 1 | +11 98392000000 | /page/csv-agent-native-api |
| warning | 1 | +49 0711 2842222 | /page/sql-agent |
| warning | 1 | +10 55256000000 | /page/csv-agent-native-api |
| warning | 1 | (780) 428-9482 | /page/sql-agent |
| No rows found, please edit your search term. | |||
Title uniqueness
No problems found.
Description uniqueness
No problems found.
Accessibility
| Analysis name | OK | Notice | Warning | Critical |
|---|---|---|---|---|
| Missing html lang attribute | 1 | 0 | 0 | 0 |
| Missing roles | 0 | 0 | 9 | 0 |
| Missing aria labels | 5 | 0 | 218 | 1 |
| Missing image alt attributes | 59 | 0 | 92 | 0 |
Valid HTML
No problems found.
Missing image alt attributes
Missing form labels
No problems found.
Missing aria labels
Found 142 row(s).
| Severity | Occurs | Detail | Affected URLs (max 5) |
|---|---|---|---|
| critical | 10 | <select ***> | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 8183 | <a class="fern-* fern-*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 1713 | <a class="fern-*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 1504 | <a class="group cursor-* fern-* minimal normal" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 1144 | <a class="block break-* text-* transition-* hover:transition-* text-* hover:text-*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 1138 | <button class="focus-* rounded-* inline-* items-* justify-* gap-* whitespace-* text-* font-* transition-* hover:transition-* focus-* focus-* disabled:pointer-* disabled:opacity-* [&_* [&_* [&_* text-* hover:bg-* hover:text-* pointer-* size-* fern-* group mr-*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 1101 | <button class="fern-* fern-*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 376 | <a class="group cursor-* fern-* outlined normal primary" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 376 | <button class="text-* h-* w-* flex-* font-* cursor-* fern-* outlined normal" id="fern-ask-ai-button" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 374 | <button class="focus-* rounded-* inline-* items-* justify-* gap-* whitespace-* text-* font-* transition-* hover:transition-* focus-* focus-* disabled:pointer-* disabled:opacity-* [&_* [&_* [&_* text-* hover:bg-* hover:text-* pointer-* size-* fern-* group fern-*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 370 | <button class="focus-* rounded-* inline-* items-* justify-* gap-* whitespace-* font-* transition-* hover:transition-* focus-* focus-* disabled:pointer-* disabled:opacity-* [&_* [&_* [&_* border-* text-* hover:bg-* hover:text-* data-* data-* border pointer-* h-* px-* text-*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 188 | <button class="focus-* rounded-* items-* justify-* gap-* whitespace-* text-* font-* transition-* hover:transition-* focus-* focus-* disabled:pointer-* disabled:opacity-* [&_* [&_* [&_* border-* text-* hover:bg-* hover:text-* data-* data-* border h-* px-* py-* mx-* mt-* flex lg:hidden" id="radix-_R_28ramriv5ubs5akknpfivb_" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 188 | <button class="focus-* rounded-* inline-* items-* justify-* gap-* whitespace-* text-* font-* transition-* hover:transition-* focus-* focus-* disabled:pointer-* disabled:opacity-* [&_* [&_* [&_* text-* hover:bg-* hover:text-* size-* ml-*" id="radix-_R_13d4riv5ubs5akknpfivb_" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 188 | <button class="fern-* group w-* lg:w-*" id="radix-_R_lubsnpamriv5ubs5akknpfivb_" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 188 | <a class="w-* shrink-* flex items-*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 188 | <button class="focus-* rounded-* inline-* items-* justify-* gap-* whitespace-* text-* font-* transition-* hover:transition-* focus-* focus-* disabled:pointer-* disabled:opacity-* [&_* [&_* [&_* text-* hover:bg-* hover:text-* size-* shrink-*"> | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 188 | <button class="fern-* group w-* lg:w-*" id="radix-_R_5fiv5uhd4riv5ubs5akknpfivb_" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 185 | <a class="flex items-* gap-* mx-* mt-* w-*" id="builtwithfern" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 185 | <button class="w-* px-* rounded-* fern-* minimal normal" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 175 | <button class="group rounded-* px-* fern-* minimal normal" id="radix-_R_kkqklubr6riv5ubs5akknpfivb_" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 175 | <a class="focus-* rounded-* inline-* items-* justify-* gap-* whitespace-* font-* transition-* hover:transition-* focus-* focus-* disabled:pointer-* disabled:opacity-* [&_* [&_* [&_* border-* text-* hover:bg-* hover:text-* data-* data-* border pointer-* h-* px-* text-*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 170 | <a class="min-* lg:min-* hover:text-* rounded-* group flex min-* flex-* select-* items-* justify-* py-* text-* lg:px-* lg:text-* data-* data-* [&_*" id="radix-_R_lfiv5t8ramriv5ubs5akknpfivb_-trigger-14257cfecd3842580642a719d96bf48aa465288c080e54f46c1ee86833378e4f" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 170 | <a class="min-* lg:min-* hover:text-* rounded-* group flex min-* flex-* select-* items-* justify-* py-* text-* lg:px-* lg:text-* data-* data-* [&_*" id="radix-_R_lfiv5t8ramriv5ubs5akknpfivb_-trigger-4b3f5f8a3687239d03c5a3051622f88dc408dd124eef4223dfc0b93be4be580d" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 170 | <a id="f8cde0058c7b3c7db1f53291b281709f5f3dbe3d17dc405ad7ddec24a5dbf42b" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 170 | <a id="14257cfecd3842580642a719d96bf48aa465288c080e54f46c1ee86833378e4f" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 170 | <a class="min-* lg:min-* hover:text-* rounded-* group flex min-* flex-* select-* items-* justify-* py-* text-* lg:px-* lg:text-* data-* data-* [&_*" id="radix-_R_lfiv5t8ramriv5ubs5akknpfivb_-trigger-f9e773b0cea65744bfa3a0ec6c30132d72b0336e798b3917b26f7007426f68e***" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 170 | <a id="f9e773b0cea65744bfa3a0ec6c30132d72b0336e798b3917b26f7007426f68e***" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 170 | <a class="min-* lg:min-* hover:text-* rounded-* group flex min-* flex-* select-* items-* justify-* py-* text-* lg:px-* lg:text-* data-* data-* [&_*" id="radix-_R_lfiv5t8ramriv5ubs5akknpfivb_-trigger-f8cde0058c7b3c7db1f53291b281709f5f3dbe3d17dc405ad7ddec24a5dbf42b" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 170 | <a class="min-* lg:min-* hover:text-* rounded-* group flex min-* flex-* select-* items-* justify-* py-* text-* lg:px-* lg:text-* data-* data-* [&_*" id="radix-_R_lfiv5t8ramriv5ubs5akknpfivb_-trigger-95535634587d5c7a76020c212bbfcb69e1734b9b1b33baeb49a98ed4b5a9404d" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 170 | <a id="95535634587d5c7a76020c212bbfcb69e1734b9b1b33baeb49a98ed4b5a9404d" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 170 | <a id="4b3f5f8a3687239d03c5a3051622f88dc408dd124eef4223dfc0b93be4be580d" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 123 | <a ***> | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 59 | <button class="focus-* rounded-* inline-* items-* justify-* gap-* whitespace-* text-* font-* transition-* hover:transition-* focus-* focus-* disabled:pointer-* disabled:opacity-* [&_* [&_* [&_* text-* hover:bg-* hover:text-* pointer-* size-* fern-* group" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 47 | <a class="back-*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 47 | <a class="github-*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 45 | <button class="fern-* text-* fern-* minimal normal" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 28 | <button class="fern-* small grayscale subtle interactive"> | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 26 | <a class="fern-* fern-* opacity-*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 19 | <button class="focus-* rounded-* inline-* items-* justify-* gap-* whitespace-* text-* font-* transition-* hover:transition-* focus-* focus-* disabled:pointer-* disabled:opacity-* [&_* [&_* [&_* text-* hover:bg-* hover:text-* pointer-* size-* fern-* group -*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 18 | <a class="min-* lg:min-* hover:text-* rounded-* group flex min-* flex-* select-* items-* justify-* py-* text-* lg:px-* lg:text-* data-* data-* [&_*" id="radix-_R_lfiv5t8ramriv5ubs5akknpfivb_-trigger-2f1ad46432092feb78d2b18a3c2340a52aa9d5ed0cc6ec83f3660e2cb2e2a2b***" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 18 | <a id="2f1ad46432092feb78d2b18a3c2340a52aa9d5ed0cc6ec83f3660e2cb2e2a2b***" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 18 | <a id="38177b52a53fc0efa2b6c52b8ab4404d22bfb697f9a724492673ca3496c286dd" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 18 | <a class="min-* lg:min-* hover:text-* rounded-* group flex min-* flex-* select-* items-* justify-* py-* text-* lg:px-* lg:text-* data-* data-* [&_*" id="radix-_R_lfiv5t8ramriv5ubs5akknpfivb_-trigger-c550ee6085f083ecbe98f8f8f90c3aa2e9b2b4a43e5ef88e5f7ce3dbf6d5b5fa" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 18 | <a id="c550ee6085f083ecbe98f8f8f90c3aa2e9b2b4a43e5ef88e5f7ce3dbf6d5b5fa" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 18 | <a id="650ac6ac095057110b93efe5ec719debd9cded6cf8a708d49e9540b6b73bf6bb" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 18 | <a id="90c85b4f4af1fd5d7308f95bb110b7ce495f9065387077624cc6f79aa7c58c***" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 18 | <a class="min-* lg:min-* hover:text-* rounded-* group flex min-* flex-* select-* items-* justify-* py-* text-* lg:px-* lg:text-* data-* data-* [&_*" id="radix-_R_lfiv5t8ramriv5ubs5akknpfivb_-trigger-38177b52a53fc0efa2b6c52b8ab4404d22bfb697f9a724492673ca3496c286dd" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 18 | <a class="min-* lg:min-* hover:text-* rounded-* group flex min-* flex-* select-* items-* justify-* py-* text-* lg:px-* lg:text-* data-* data-* [&_*" id="radix-_R_lfiv5t8ramriv5ubs5akknpfivb_-trigger-90c85b4f4af1fd5d7308f95bb110b7ce495f9065387077624cc6f79aa7c58c***" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 18 | <a class="min-* lg:min-* hover:text-* rounded-* group flex min-* flex-* select-* items-* justify-* py-* text-* lg:px-* lg:text-* data-* data-* [&_*" id="radix-_R_lfiv5t8ramriv5ubs5akknpfivb_-trigger-650ac6ac095057110b93efe5ec719debd9cded6cf8a708d49e9540b6b73bf6bb" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 12 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_pmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 12 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_11mqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2, URL 3, URL 4 |
| warning | 10 | <button class="fern-* outlined small" id="radix-_R_6acklubr6riv5ubs5akknpfivb_" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 10 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_15mqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2, URL 3 |
| warning | 10 | <button class="group rounded-* px-* fern-* minimal normal" id="radix-_R_kiklubr6riv5ubs5akknpfivb_" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 10 | <button class="-* pl-* fern-* minimal normal success" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 10 | <button class="focus-* rounded-* inline-* items-* justify-* gap-* whitespace-* text-* font-* transition-* hover:transition-* focus-* focus-* disabled:pointer-* disabled:opacity-* [&_* [&_* [&_* text-* hover:bg-* hover:text-* pointer-* size-* fern-*"> | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 10 | <button class="focus-* rounded-* inline-* items-* justify-* gap-* whitespace-* text-* font-* transition-* hover:transition-* focus-* focus-* disabled:pointer-* disabled:opacity-* [&_* [&_* [&_* text-* hover:bg-* hover:text-* pointer-* size-* fern-* group invisible" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 8 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_5mqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2 |
| warning | 8 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_vmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2, URL 3, URL 4 |
| warning | 7 | <button class="min-* flex-* truncate ring-* fern-* outlined small rounded" *** > | URL 1, URL 2, URL 3 |
| warning | 6 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_lmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2 |
| warning | 6 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_1hmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2, URL 3 |
| warning | 6 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_2jmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2, URL 3 |
| warning | 6 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_rmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2 |
| warning | 6 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_19mqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2 |
| warning | 4 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_fmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /v1/docs/overview-rag-connectors |
| warning | 4 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_nmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/cohere-works-everywhere |
| warning | 4 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_9mqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2 |
| warning | 4 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_1tmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2 |
| warning | 4 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_hmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2 |
| warning | 4 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_tmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /v1/docs/cohere-works-everywhere |
| warning | 4 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_jmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2 |
| warning | 4 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_1rmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | URL 1, URL 2 |
| warning | 3 | <button class="min-* flex-* truncate ring-* fern-* outlined small primary rounded" *** > | URL 1, URL 2, URL 3 |
| warning | 3 | <a class="fern-* endpoint-*" *** > | / |
| warning | 3 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_dmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/reasoning |
| warning | 3 | <a class="flex items-* gap-* mx-* my-* w-*" id="builtwithfern" *** > | URL 1, URL 2, URL 3 |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_2tmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/rag-citations |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_1pmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/embed-jobs-api |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_13mqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/image-inputs |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_23mqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/image-inputs |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_bmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/streaming |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_31mqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/tool-use-citations |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_3tmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/tool-use-overview |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_1jmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/rag-citations |
| warning | 2 | <button class="fern-* fern-* opacity-*" *** > | URL 1, URL 2 |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_2fmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/rag-citations |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_3jmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/tool-use-streaming |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_1nmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/tool-use-citations |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_43mqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/tool-use-usage-patterns |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_ep5mqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/tool-use-parameter-types |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_39mqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/tool-use-streaming |
| warning | 2 | <button class="fern-* data-* group flex min-* items-* px-* py-* data-*" id="radix-_R_2pmqklubr6riv5ubs5akknpfivb_-trigger-***" *** > | /docs/tool-use-overview |
| warning | 1 | <button class="fern-* data-*" id="radix-_R_ahkcklubr6riv5ubs5akknpfivb_" *** > | /reference/chat-stream |
| warning | 1 | <button class="fern-* data-*" id="radix-_R_16hkcklubr6riv5ubs5akknpfivb_" *** > | /reference/chat-stream |
| warning | 1 | <button class="fern-* data-*" id="radix-_R_3ahkcklubr6riv5ubs5akknpfivb_" *** > | /reference/chat-stream |
| warning | 1 | <button class="focus-* rounded-* inline-* items-* justify-* gap-* whitespace-* font-* transition-* hover:transition-* focus-* focus-* disabled:pointer-* disabled:opacity-* [&_* [&_* bg-* hover:bg-* text-* h-* px-* text-* font-* [&_*" id="playground-button:reference/listfinetunedmodels" *** > | /reference/listfinetunedmodels |
| warning | 1 | <button class="fern-* data-*" id="radix-_R_1mhkcklubr6riv5ubs5akknpfivb_" *** > | /reference/chat-stream |
| warning | 1 | <button class="fern-* data-*" id="radix-_R_1ahkcklubr6riv5ubs5akknpfivb_" *** > | /reference/chat-stream |
| warning | 1 | <a class="fern-*" name="merge" *** > | /page/deploy-finetuned-model-aws-marketplace |
| You have reached the limit of 100 rows as a protection against very large output or exhausted memory. | |||
| No rows found, please edit your search term. | |||
Missing roles
Found 10 row(s).
| Severity | Occurs | Detail | Affected URLs (max 5) |
|---|---|---|---|
| warning | 188 | <main class="relative z-* flex transition-* duration-* ease-* mt-*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 188 | <aside class="fern-*" id="fern-sidebar" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 188 | <footer class="width-*" id="fern-footer"> | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 188 | <nav class="fern-* fern-* hidden lg:flex" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 188 | <aside id="fern-sidebar-spacer"> | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 185 | <nav class="fern-*" *** > | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 185 | <header class="my-* space-*"> | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 185 | <footer class="fern-* not-*"> | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 175 | <aside id="fern-toc"> | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 10 | <aside class="fern-*"> | URL 1, URL 2, URL 3, URL 4, URL 5 |
| No rows found, please edit your search term. | |||
Missing html lang attribute
No problems found.
Security
Found 10 row(s).
| Header | OK | Notice | Warning | Critical | Recommendation |
|---|---|---|---|---|---|
| Strict-Transport-Security | 188 | 0 | 0 | 238 | Strict-Transport-Security header is not set. It enforces secure connections and protects against MITM attacks. |
| Content-Security-Policy | 188 | 0 | 0 | 238 | Content-Security-Policy header is not set. It restricts resources the page can load and prevents XSS attacks. |
| X-Frame-Options | 0 | 0 | 426 | 0 | X-Frame-Options header is not set. It prevents clickjacking attacks when set to 'deny' or 'sameorigin. |
| X-Content-Type-Options | 188 | 0 | 238 | 0 | X-Content-Type-Options header is not set. It stops MIME type sniffing and mitigates content type attacks. |
| Referrer-Policy | 188 | 0 | 238 | 0 | Referrer-Policy header is not set. It controls referrer header sharing and enhances privacy and security. |
| Feature-Policy | 0 | 188 | 238 | 0 | Feature-Policy header is not set but Permissions-Policy is set. That's enough.. Feature-Policy header is not set. It allows enabling/disabling browser APIs and features for security. Not important if Permissions-Policy is set. |
| Permissions-Policy | 188 | 0 | 238 | 0 | Permissions-Policy header is not set. It allows enabling/disabling browser APIs and features for security. |
| X-Powered-By | 0 | 0 | 188 | 0 | X-Powered-By header is set to 'Next.js'. It is better not to reveal used technologies. |
| Server | 0 | 426 | 0 | 0 | Server header is set to 'Vercel'. It is better not to reveal used technologies. |
| X-XSS-Protection | 426 | 0 | 0 | 0 | |
| No rows found, please edit your search term. | |||||
Security headers
Found 10 row(s).
| Severity | Occurs | Detail | Affected URLs (max 5) |
|---|---|---|---|
| critical | 238 | Strict-Transport-Security header is not set. It enforces secure connections and protects against MITM attacks. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| critical | 238 | Content-Security-Policy header is not set. It restricts resources the page can load and prevents XSS attacks. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 426 | X-Frame-Options header is not set. It prevents clickjacking attacks when set to 'deny' or 'sameorigin. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 238 | Feature-Policy header is not set. It allows enabling/disabling browser APIs and features for security. Not important if Permissions-Policy is set. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 238 | Referrer-Policy header is not set. It controls referrer header sharing and enhances privacy and security. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 238 | Permissions-Policy header is not set. It allows enabling/disabling browser APIs and features for security. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 238 | X-Content-Type-Options header is not set. It stops MIME type sniffing and mitigates content type attacks. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| warning | 188 | X-Powered-By header is set to 'Next.js'. It is better not to reveal used technologies. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| notice | 426 | Server header is set to 'Vercel'. It is better not to reveal used technologies. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| notice | 188 | Feature-Policy header is not set but Permissions-Policy is set. That's enough. | URL 1, URL 2, URL 3, URL 4, URL 5 |
| No rows found, please edit your search term. | |||
TOP non-unique titles
Found 10 row(s).
| Count 🔽 | Title |
|---|---|
| 3 | Different Types of API Keys and Rate Limits | Cohere |
| 3 | An Overview of Cohere's Models | Cohere |
| 3 | Retrieval Augmented Generation (RAG) | Cohere |
| 3 | Cohere's Command R7B Model | Cohere |
| 2 | Train and deploy a fine-tuned model. | Cohere |
| 2 | Cohere SDK Cloud Platform Compatibility | Cohere |
| 2 | Cohere's Embed Models (Details and Application) | Cohere |
| 2 | Using the Cohere Chat API for Text Generation | Cohere |
| 2 | Cohere's Rerank Model (Details and Application) | Cohere |
| 2 | How Does Cohere's Pricing Work? | Cohere |
| No rows found, please edit your search term. | |
TOP non-unique descriptions
Found 10 row(s).
| Count 🔽 | Description |
|---|---|
| 3 | This page describes Cohere API rate limits for production and evaluation keys. |
| 3 | Cohere has a variety of models that cover many different use cases. If you need more customization, you can train a model to tune it to your specific use case. |
| 3 | Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused large language models. It excels at RAG, tool use, and agents. |
| 2 | This page details Cohere's pricing model. Our models can be accessed directly through our API, allowing for the creation of scalable production workloads. |
| 2 | Command R is a conversational model that excels in language tasks and supports multiple languages, making it ideal for coding use cases. |
| 2 | How to use the Chat API endpoint with Cohere LLMs to generate text responses in a conversational interface |
| 2 | This page describes how to get Cohere models to create outputs in a certain format, such as JSON, TOOLS, using parameters such as response_format. |
| 2 | Explore a range of AI guides and get started with Cohere's generative platform, ready-made and best-practice optimized. |
| 2 | The safety modes documentation describes how to use default and strict modes in order to exercise additional control over model output. |
| 2 | Cohere offers world-class Large Language Models (LLMs) like Command, Rerank, and Embed. These help developers and enterprises build LLM-powered applications. |
| No rows found, please edit your search term. | |
SEO metadata
Found 188 row(s).
| URL 🔼 | Indexing | Title | H1 | Description | Keywords |
|---|---|---|---|---|---|
| / | Allowed | Cohere Documentation | Cohere | Missing H1 | Cohere's API documentation helps developers easily integrate natural language processing and generation into their products. | |
| /docs/advanced-generation-hyperparameters | Allowed | Advanced Generation Parameters | Cohere | Advanced Generation Parameters | This page describes advanced parameters for controlling generation. | LLMs, Cohere |
| /docs/agentic-rag | Allowed | Building Agentic RAG with Cohere | Cohere | Building Agentic RAG with Cohere | Hands-on tutorials on building agentic RAG applications with Cohere | Cohere, RAG, agents, function calling,tool use |
| /docs/aya | Allowed | Aya Family of Models | Cohere | Aya Family of Models | Understand Cohere Labs groundbreaking multilingual Aya models, which aim to bring many more languages into generative AI. | Cohere AI, multilingual large language models, generative AI |
| /docs/build-things-with-cohere | Allowed | Build an Onboarding Assistant with Cohere! | Cohere | Build an Onboarding Assistant with Cohere! | This page describes how to build an onboarding assistant with Cohere's large language models. | working with LLMs, Cohere |
| /docs/chat-api | Allowed | Using the Cohere Chat API for Text Generation | Cohere | Using the Cohere Chat API for Text Generation | How to use the Chat API endpoint with Cohere LLMs to generate text responses in a conversational interface | Cohere, text generation, LLMs, generative AI |
| /docs/chat-fine-tuning | DENY (meta) | Fine-tuning for Cohere's Chat Model | Cohere | Fine-tuning for Cohere’s Chat Model | This document provides guidance on fine-tuning, evaluating, and improving chat models. | chat models, fine-tuning language models, fine-tuning, fine-tuning chat models |
| /docs/chat-improving-the-results | DENY (meta) | Improving the Chat Fine-tuning Results | Cohere | Improving the Chat Fine-tuning Results | Learn how to refine data, iterate on hyperparameters, and troubleshoot to fine-tune your Chat model effectively. | fine-tuning, fine-tuning language models, chat models |
| /docs/chat-on-langchain | Allowed | Cohere Chat on LangChain (Integration Guide) | Cohere | Cohere Chat on LangChain (Integration Guide) | Integrate Cohere with LangChain to build applications using Cohere's models and LangChain tools. | LangChain, generative AI |
| /docs/chat-preparing-the-data | DENY (meta) | Preparing the Chat Fine-tuning Data | Cohere | Preparing the Chat Fine-tuning Data | Prepare your data for fine-tuning a Command model for Chat with this step-by-step guide, including data formatting, requirements, and best practices. | fine-tuning, fine-tuning language models |
| /docs/chat-starting-the-training | DENY (meta) | Starting the Chat Fine-Tuning Run | Cohere | Starting the Chat Fine-Tuning Run | Learn how to fine-tune a Command model for chat with the Cohere Web UI or Python SDK, including data requirements, pricing, and calling your model. | fine-tuning, fine-tuning language models |
| /docs/chat-understanding-the-results | DENY (meta) | Understanding the Chat Fine-tuning Results | Cohere | Understanding the Chat Fine-tuning Results | Learn how to evaluate and troubleshoot a fine-tuned chat model with accuracy and loss metrics. | chat models, fine-tuning, fine-tuning language models |
| /docs/chroma-and-cohere | Allowed | Chroma and Cohere (Integration Guide) | Cohere | Chroma and Cohere (Integration Guide) | This page describes how to integrate Cohere and Chroma. | Cohere and Chroma |
| /docs/classify-fine-tuning | DENY (meta) | Fine-tuning for Cohere's Classify Model | Cohere | Fine-tuning for Cohere’s Classify Model | This document provides guidance on fine-tuning, evaluating, and improving classification models. | classification, classification models, fine-tuning large language models |
| /docs/classify-improving-the-results | DENY (meta) | Improving the Classify Fine-tuning Results | Cohere | Improving the Classify Fine-tuning Results | Troubleshoot your fine-tuned classification model with these tips for refining data quality and improving results. | classification models, fine-tuning, fine-tuning classification models |
| /docs/classify-preparing-the-data | DENY (meta) | Preparing the Classify Fine-tuning data | Cohere | Preparing the Classify Fine-tuning data | Learn how to prepare your data for fine-tuning classification models, including single-label and multi-label data formats and dataset cleaning tips. | classification models, fine-tuning, fine-tuning language models |
| /docs/classify-starting-the-training | DENY (meta) | Train and deploy a fine-tuned model. | Cohere | Train and deploy a fine-tuned model. | Fine-tune classification models with Cohere's Web UI or Python SDK using custom datasets. (V2) | classification models, fine-tuning language models, fine-tuning |
| /docs/classify-understanding-the-results | DENY (meta) | Understanding the Classify Fine-tuning Results | Cohere | Understanding the Classify Fine-tuning Results | Understand the performance metrics for a fine-tuned classification model and learn how to interpret its accuracy, precision, recall, and F1 scores. | fine-tuning, classification, fine-tuning language models |
| /docs/cohere-and-langchain | Allowed | Cohere and LangChain (Integration Guide) | Cohere | Cohere and LangChain (Integration Guide) | Integrate Cohere with LangChain for advanced chat features, RAG, embeddings, and reranking; this guide includes code examples for each feature. | LangChain, Cohere integrations, Retrieval Augmented Generation |
| /docs/cohere-embed | Allowed | Cohere's Embed Models (Details and Application) | Cohere | Cohere’s Embed Models (Details and Application) | Explore Embed models for text classification and embedding generation in English and multiple languages, with details on dimensions and endpoints. | Cohere, large language models, generative AI, embeddings |
| /docs/cohere-faqs | Allowed | Frequently Asked Questions About Cohere | Cohere | Frequently Asked Questions About Cohere | Cohere is a powerful platform for using Large Language Models (LLMs). This page covers FAQs related to functionality, pricing, troubleshooting, and more. | natural language processing, generative AI, fine-tuning models |
| /docs/cohere-labs-acceptable-use-policy | Allowed | Cohere Labs Acceptable Use Policy | Cohere | Cohere Labs Acceptable Use Policy | "Promoting safe and ethical use of generative AI with guidelines to prevent misuse and abuse." | c4ai, api reference, open source, LLM, Command-R |
| /docs/cohere-on-azure/cohere-on-azure-ai-foundry | Allowed | Introduction to Cohere on Azure AI Foundry | Cohere | Introduction to Cohere on Azure AI Foundry | An introduction to Cohere on Azure AI Foundry, a fully managed service by Azure (API v2). | Cohere, Command models, Embed models, Rerank models, Azure AI Foundry |
| /docs/cohere-toolkit | Allowed | How to Start with the Cohere Toolkit | Cohere | How to Start with the Cohere Toolkit | Build and deploy RAG applications quickly with the Cohere Toolkit, which offers pre-built front-end and back-end components. | toolkit, agents, LLMs, generative AI |
| /docs/cohere-works-everywhere | Allowed | Cohere SDK Cloud Platform Compatibility | Cohere | Cohere SDK Cloud Platform Compatibility | This page describes various places you can use Cohere's SDK. | Cohere, Cohere SDK, large language model SDK |
| /docs/command-a | Allowed | Command A | Cohere | Command A | Command A is a performant mode good at tool use, RAG, agents, and multilingual use cases. It has 111 billion parameters and a 256k context length. | generative AI, Cohere, large language models |
| /docs/command-a-reasoning | Allowed | Cohere's Command A Reasoning Model | Cohere | Cohere's Command A Reasoning Model | Command A Reasoning excels in tool use, agentic workflows, and complex problem-solving. It has 111 billion parameters and a 256k context length. | generative AI, Cohere, reasoning, large language models |
| /docs/command-r | Allowed | Cohere's Command R Model | Cohere | Cohere's Command R Model | Command R is a conversational model that excels in language tasks and supports multiple languages, making it ideal for coding use cases. | Cohere, large language models, generative AI, command model, chat models, conversational AI |
| /docs/command-r-plus | Allowed | Cohere's Command R+ Model | Cohere | Cohere’s Command R+ Model | Command R+ is Cohere's optimized for conversational interaction and long-context tasks, best suited for complex RAG workflows and multi-step tool use. | generative AI, Cohere, large language models |
| /docs/command-r7b | Allowed | Cohere's Command R7B Model | Cohere | Cohere's Command R7B Model | Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused large language models. It excels at RAG, tool use, and agents. | generative AI, Cohere, large language models |
| /docs/compatibility-api | Allowed | Using Cohere models via the OpenAI SDK | Cohere | Using Cohere models via the OpenAI SDK | The document serves as a guide for Cohere's Compatibility API, which allows developers to seamlessly use Cohere's models using OpenAI's SDK. | Cohere, text generation, LLMs, generative AI |
| /docs/contribute | Allowed | Help Us Improve The Cohere Docs | Cohere | Help Us Improve The Cohere Docs | Contribute to our docs content, stored in the cohere-developer-experience repo; we welcome your pull requests! | cohere, documentation, contribute, open-source |
| /docs/cookbooks | Allowed | Cohere Cookbooks: Build AI Agents and Solutions | Cohere | Cohere Cookbooks: Build AI Agents and Solutions | Get started with Cohere's cookbooks to build agents, QA bots, perform searches, and more, all organized by category. | Cohere, large language models, generative AI, LLM tutorial |
| /docs/create-client | Allowed | Creating a client | Cohere | Creating a client | A guide for creating Cohere API client using Cohere SDK, supported in 4 different languages – Python, TypeScript, Java, and Go. | Cohere, Cohere SDK, API v2, API v1 |
| /docs/datasets | Allowed | The Cohere Datasets API (and How to Use It) | Cohere | The Cohere Datasets API (and How to Use It) | Learn about the Dataset API, including its file size limits, data retention, creation, validation, metadata, and more, with provided code snippets. | datasets, processing datasets with language models, generative AI |
| /docs/deployment-options-overview | Allowed | Deployment Options - Overview | Cohere | Overview | This page provides an overview of the available options for deploying Cohere's models. | generative AI, large language models, private deployment |
| /docs/deprecations | Allowed | Deprecations | Cohere | Deprecations | Learn about Cohere's deprecation policies and recommended replacements | Cohere API, large language models, generative AI |
| /docs/documents-and-citations | DENY (meta) | Documents and Citations | Cohere | Documents and Citations | The document introduces RAG as a method to improve language model responses by providing source material for context. | retrieval augmented generation, LLM hallucination reduction |
| /docs/embed-jobs-api | Allowed | Batch Embedding Jobs with the Embed API | Cohere | Batch Embedding Jobs with the Embed API | Learn how to use the Embed Jobs API to handle large text data efficiently with a focus on creating datasets and running embed jobs. | datasets embedding, embedding models, vector embeddings |
| /docs/embed-on-langchain | Allowed | Cohere Embed on LangChain (Integration Guide) | Cohere | Cohere Embed on LangChain (Integration Guide) | This page describes how to work with Cohere's embeddings models and LangChain. | Cohere, vector embedding model |
| /docs/embeddings | Allowed | Introduction to Embeddings at Cohere | Cohere | Introduction to Embeddings at Cohere | Embeddings transform text into numerical data, enabling language-agnostic similarity searches and efficient storage with compression. | vector embeddings, embeddings, natural language processing |
| /docs/fine-tuning | DENY (meta) | Introduction to Fine-Tuning with Cohere Models | Cohere | Introduction to Fine-Tuning with Cohere Models | Fine-tune Cohere's large language models for specific tasks, styles, and formats with custom data. | fine-tuning language models, fine-tuning |
| /docs/fine-tuning-with-the-python-sdk | DENY (meta) | Programmatic Fine-tuning with Cohere's Python SDK | Cohere | Programmatic Fine-tuning with Cohere’s Python SDK | Fine-tune models using the Cohere Python SDK programmatically and monitor the results through the Dashboard Web UI. | python, fine-tuning, fine-tuning large language models |
| /docs/foundation-models | DENY (meta) | Foundational Models | Cohere | Foundational Models | In this chapter, you'll get an overview of Cohere's foundation models. | |
| /docs/generate-fine-tuning | DENY (meta) | Fine-tuning for Generate | Cohere | Fine-tuning for Generate | This document provides guidance on fine-tuning, evaluating, and improving generative models. | fine-tuning language models, fine-tuning |
| /docs/get-started-installation | Allowed | Installation | Cohere | Installation | A guide for installing the Cohere SDK, supported in 4 different languages – Python, TypeScript, Java, and Go. | Cohere, Cohere SDK, API v1 |
| /docs/going-live | Allowed | Going Live with a Cohere Model | Cohere | Going Live with a Cohere Model | Learn to upgrade from a Trial to a Production key; understand the limitations and benefits of each and go live with Cohere. | Cohere API, large language models, generative AI |
| /docs/how-does-cohere-pricing-work | Allowed | How Does Cohere's Pricing Work? | Cohere | How Does Cohere's Pricing Work? | This page details Cohere's pricing model. Our models can be accessed directly through our API, allowing for the creation of scalable production workloads. | Cohere, large language model pricing |
| /docs/image-inputs | Allowed | Using Cohere's Models to Work with Image Inputs | Cohere | Using Cohere's Models to Work with Image Inputs | This page describes how a Cohere large language model works with image inputs. It covers passing images with the API, limitations, and best practices. | Cohere, large language models |
| /docs/integrations | Allowed | Integrating Embedding Models with Other Tools | Cohere | Integrating Embedding Models with Other Tools | Learn how to integrate Cohere embeddings with open-source vector search engines for enhanced applications. | Cohere integrations |
| /docs/introduction-to-text-generation-at-cohere | Allowed | Introduction to Text Generation at Cohere | Cohere | Introduction to Text Generation at Cohere | This page describes how a large language model generates textual output. | Cohere, large language models |
| /docs/llamaindex | Allowed | LlamaIndex and Cohere's Models | Cohere | LlamaIndex and Cohere's Models | Learn how to use Cohere and LlamaIndex together to generate responses based on data. | embeddings, LlamaIndex |
| /docs/llmu-2 | Allowed | Welcome to LLM University! | Cohere | Welcome to LLM University! | LLM University (LLMU) offers in-depth, practical NLP and LLM training. Ideal for all skill levels. Learn, build, and deploy Language AI with Cohere. | |
| /docs/migrating-v1-to-v2 | Allowed | Migrating From API v1 to API v2 | Cohere | Migrating From API v1 to API v2 | The document serves as a reference for developers looking to update their existing Cohere API v1 implementations to the new v2 standard. | Cohere, text generation, LLMs, generative AI |
| /docs/models | Allowed | An Overview of Cohere's Models | Cohere | An Overview of Cohere's Models | Cohere has a variety of models that cover many different use cases. If you need more customization, you can train a model to tune it to your specific use case. | large language models, generative AI models |
| /docs/multimodal-embeddings | Allowed | Unlocking the Power of Multimodal Embeddings | Cohere | Unlocking the Power of Multimodal Embeddings | Multimodal embeddings convert text and images into embeddings for search and classification (API v2). | vector embeddings, image embeddings, images, multimodal, multimodal embeddings, embeddings, natural language processing |
| /docs/parameter-types-in-json | Allowed | Parameter Types in Structured Outputs (JSON) | Cohere | Parameter Types in Structured Outputs (JSON) | This page shows usage examples of the JSON Schema parameter types supported in Structured Outputs (JSON). | Cohere, language models, structured outputs |
| /docs/playground-overview | Allowed | An Overview of the Developer Playground | Cohere | An Overview of the Developer Playground | The Cohere Playground is a powerful visual interface for testing Cohere's generation and embedding language models without coding. | Large language model playground, generative AI |
| /docs/predictable-outputs | Allowed | How to Get Predictable Outputs with Cohere Models | Cohere | How to Get Predictable Outputs with Cohere Models | Strategies for decoding text, and the parameters that impact the randomness and predictability of a language model's output. | generative AI output |
| /docs/rag-citations | Allowed | RAG Citations | Cohere | RAG Citations | Guide on accessing and utilizing citations generated by the Cohere Chat endpoint for RAG. It covers both non-streaming and streaming modes (API v2). | retrieval augmented generation, RAG, grounded replies, text generation |
| /docs/rate-limits | Allowed | Different Types of API Keys and Rate Limits | Cohere | Different Types of API Keys and Rate Limits | This page describes Cohere API rate limits for production and evaluation keys. | Cohere, large language model API |
| /docs/reasoning | Allowed | Reasoning Capabilities | Cohere | Reasoning Capabilities | Reasoning models excel at tool use, agentic workflows, and complex problem-solving. This page provides a general overview of Cohere's reasoning capalities. | generative AI, Cohere, reasoning models, large language models |
| /docs/rerank | Allowed | Cohere's Rerank Model (Details and Application) | Cohere | Cohere’s Rerank Model (Details and Application) | This page describes how Cohere's Rerank models work and how to use them. | Cohere, language models, rerank models |
| /docs/rerank-fine-tuning | DENY (meta) | Fine-tuning for Cohere's Rerank Model | Cohere | Fine-tuning for Cohere’s Rerank Model | This document provides guidance on fine-tuning, evaluating, and improving rerank models. | rerank models, generative AI, fine-tuning, fine-tuning language models |
| /docs/rerank-improving-the-results | DENY (meta) | Improving the Rerank Fine-tuning Results | Cohere | Improving the Rerank Fine-tuning Results | Tips for achieving the best fine-tuned rerank model and troubleshooting guide for fine-tuned models. | fine-tuning, fine-tuning language models, rerank |
| /docs/rerank-on-langchain | Allowed | Cohere Rerank on LangChain (Integration Guide) | Cohere | Cohere Rerank on LangChain (Integration Guide) | This page describes how to integrate Cohere's ReRank models with LangChain. | Cohere, language models, LangChain, Rerank models |
| /docs/rerank-preparing-the-data | DENY (meta) | Preparing the Rerank Fine-tuning Data | Cohere | Preparing the Rerank Fine-tuning Data | Learn how to prepare and format your data for fine-tuning Cohere's Rerank model. | fine-tuning, fine-tuning language models |
| /docs/rerank-starting-the-training | DENY (meta) | Starting the Rerank Fine-Tuning | Cohere | Starting the Rerank Fine-Tuning | How to start training a fine-tuning model for Rerank using both the Web UI and the Python SDK. | fine-tuning, fine-tuning language models |
| /docs/rerank-understanding-the-results | DENY (meta) | Understanding the Rerank Fine-tuning Results | Cohere | Understanding the Rerank Fine-tuning Results | Understand how fine-tuned models for Rerank are evaluated, and learn about the specific metrics used, including Accuracy, MRR, and nDCG. | fine-tuning, data, fine-tuning language models |
| /docs/reranking-best-practices | Allowed | Best Practices for using Rerank | Cohere | Best Practices for using Rerank | Tips for optimal endpoint performance, including constraints on the number of documents, tokens per document, and tokens per query. | rerank, natural language processing |
| /docs/responsible-use | Allowed | Command R and Command R+ Model Card | Cohere | Command R and Command R+ Model Card | This doc provides guidelines for using Cohere generation models ethically and constructively. | AI safety, AI risk, responsible AI |
| /docs/retrieval-augmented-generation-rag | Allowed | Retrieval Augmented Generation (RAG) | Cohere | Retrieval Augmented Generation (RAG) | Guide on using Cohere's Retrieval Augmented Generation (RAG) capabilities such as document grounding and citations. | retrieval augmented generation, RAG, grounded replies, text generation |
| /docs/safety-modes | Allowed | Safety Modes | Cohere | Safety Modes | The safety modes documentation describes how to use default and strict modes in order to exercise additional control over model output. | AI safety, AI risk, responsible AI, Cohere |
| /docs/semantic-search | DENY (meta) | Semantic Search | Cohere | Semantic Search | This document provides a guide on building a simple semantic search engine using language models to search by meaning. It includes steps to embed text, build an index, conduct nearest neighbor search, and visualize the results. | semantic search, generative AI, large language models |
| /docs/semantic-search-embed | Allowed | Semantic Search with Embeddings | Cohere | Semantic Search with Embeddings | Examples on how to use the Embed endpoint to perform semantic search (API v2). | vector embeddings, embeddings, natural language processing |
| /docs/serving-platform | DENY (meta) | Serving Platform | Cohere | Serving Platform | In this chapter, you'll get an overview of Cohere's serving platform. | |
| /docs/streaming | Allowed | A Guide to Streaming Responses | Cohere | A Guide to Streaming Responses | The document explains how the Chat API can stream events like text generation in real-time. | streaming, generative AI, text generation |
| /docs/structured-outputs | Allowed | How do Structured Outputs Work? | Cohere | How do Structured Outputs Work? | This page describes how to get Cohere models to create outputs in a certain format, such as JSON, TOOLS, using parameters such as response_format. | Cohere, language models, structured outputs, the response format parameter |
| /docs/summarizing-text | Allowed | Summarizing Text with the Chat Endpoint | Cohere | Summarizing Text with the Chat Endpoint | Learn how to perform text summarization using Cohere's Chat endpoint with features like length control and RAG. | Cohere, large language models, generative AI |
| /docs/supported-languages | DENY (meta) | Supported Languages | Cohere | Supported Languages | A list of languages that Cohere's multilingual embedding model provides. | multilingual embedding models, vector embeddings |
| /docs/text-generation-tutorial | Allowed | Cohere Text Generation Tutorial | Cohere | Cohere Text Generation Tutorial | This page walks through how Cohere's generation models work and how to use them. | Cohere, how do LLMs generate text |
| /docs/the-cohere-platform | Allowed | An Overview of The Cohere Platform | Cohere | An Overview of The Cohere Platform | Cohere offers world-class Large Language Models (LLMs) like Command, Rerank, and Embed. These help developers and enterprises build LLM-powered applications. | natural language processing, generative AI, fine-tuning models |
| /docs/tokens-and-tokenizers | Allowed | A Guide to Tokens and Tokenizers | Cohere | A Guide to Tokens and Tokenizers | This document describes how to use the tokenize and detokenize API endpoints. | language model tokens, natural language processing |
| /docs/tool-use-citations | Allowed | Citations for tool use (function calling) | Cohere | Citations for tool use (function calling) | Guide on accessing and utilizing citations generated by the Cohere Chat endpoint for tool use. It covers both non-streaming and streaming modes (API v2). | Cohere, text generation, LLMs, generative AI |
| /docs/tool-use-overview | Allowed | Basic usage of tool use (function calling) | Cohere | Basic usage of tool use (function calling) | An overview of using Cohere's tool use capabilities, enabling developers to build agentic workflows (API v2). | Cohere, text generation, LLMs, generative AI |
| /docs/tool-use-parameter-types | Allowed | Parameter types for tool use (function calling) | Cohere | Parameter types for tool use (function calling) | Guide on using structured outputs with tool parameters in the Cohere Chat API. Includes guide on supported parameter types and usage examples (API v2). | Cohere, text generation, LLMs, generative AI |
| /docs/tool-use-streaming | Allowed | Streaming for tool use (function calling) | Cohere | Streaming for tool use (function calling) | Guide on implementing streaming for tool use in Cohere's platform and details on the events stream (API v2). | Cohere, text generation, LLMs, generative AI |
| /docs/tool-use-usage-patterns | Allowed | Usage patterns for tool use (function calling) | Cohere | Usage patterns for tool use (function calling) | Guide on implementing various tool use patterns with the Cohere Chat endpoint such as parallel tool calling, multi-step tool use, and more (API v2). | Cohere, text generation, LLMs, generative AI |
| /docs/tools | Allowed | An Overview of Tool Use with Cohere | Cohere | An Overview of Tool Use with Cohere | Learn when to use leverage multi-step tool use in your workflows. | Cohere, large language models, generative AI |
| /docs/tools-on-langchain | Allowed | Cohere Tools on LangChain (Integration Guide) | Cohere | Cohere Tools on LangChain (Integration Guide) | Explore code examples for multi-step and single-step tool usage in chatbots, harnessing internet search and vector storage. | tool use, generative AI, langchain |
| /docs/usage-policy | Allowed | Usage Policy | Cohere | Usage Policy | Developers must outline and get approval for their use case to access the Cohere API, understanding the models and limitations. They should refer to model cards for detailed information and document potential harms of their application. Certain use cases, such as violence, hate speech, fraud, and privacy violations, are strictly prohibited. | Cohere API |
| /page/agent-api-calls | Allowed | Building an LLM Agent with the Cohere API | Cohere | Building an LLM Agent with the Cohere API | This page how to use Cohere's API to build an LLM-based agent. | Cohere, agents, LLMs |
| /page/agent-short-term-memory | Allowed | Short-Term Memory Handling for Agents | Cohere | Short-Term Memory Handling for Agents | This page describes how to manage short-term memory in an agent built with Cohere models. | Cohere, agents, short-term memory |
| /page/agentic-multi-stage-rag | Allowed | Agentic Multi-Stage RAG with Cohere Tools API | Cohere | Agentic Multi-Stage RAG with Cohere Tools API | This page describes how to build a powerful, multi-stage agent with the Cohere platform. | Cohere, agents, LLMs |
| /page/agentic-rag-mixed-data | Allowed | Agentic RAG for PDFs with mixed data | Cohere | Agentic RAG for PDFs with mixed data | This page describes building a powerful, multi-step chatbot with Cohere's models. | Cohere, chatbot |
| /page/analysis-of-financial-forms | Allowed | Analysis of Form 10-K/10-Q Using Cohere and RAG | Cohere | Analysis of Form 10-K/10-Q Using Cohere and RAG | This page describes how to use Cohere's large language models to build an agent able to analyze financial forms like a 10-K or a 10-Q. | Cohere, AI assistant for finance |
| /page/analyzing-hacker-news | Allowed | Analyzing Hacker News with Cohere | Cohere | Analyzing Hacker News with Cohere | This page describes building a generative-AI powered tool to analyze headlines with Cohere. | Cohere, analyzing text with a large language model. |
| /page/article-recommender-with-text-embeddings | Allowed | Article Recommender via Embedding & Classification | Cohere | Article Recommender via Embedding & Classification | This page describes how to build a generative-AI tool to recommend articles with Cohere. | Cohere, AI assistant, recommendation engines |
| /page/aya-vision-intro | Allowed | Introduction to Aya Vision | Cohere | Introduction to Aya Vision | In this notebook, we will explore the capabilities of Aya Vision, which can take text and image inputs to generates text responses. | Aya, Cohere Labs, multimodal model, multilingual model |
| /page/basic-multi-step | Allowed | Multi-Step Tool Use with Cohere | Cohere | Multi-Step Tool Use with Cohere | This page describes how to create a multi-step, tool-using AI agent with Cohere's tool use functionality. | Cohere, AI assistant, agent, LLMs, agent tool use |
| /page/basic-rag | Allowed | Basic RAG: Retrieval-Augmented Generation with Cohere | Cohere | Basic RAG: Retrieval-Augmented Generation with Cohere | This page describes how to work with Cohere's basic retrieval-augmented generation functionality. | Cohere, retrieval-augmented generation, RAG, AI agents |
| /page/basic-semantic-search | Allowed | Basic Semantic Search with Cohere Models | Cohere | Basic Semantic Search with Cohere Models | This page describes how to do basic semantic search with Cohere's models. | Cohere, semantic search |
| /page/basic-tool-use | Allowed | Getting Started with Basic Tool Use | Cohere | Getting Started with Basic Tool Use | This page describes how to work with Cohere's basic tool use functionality. | Cohere, tool use, AI agents |
| /page/calendar-agent | Allowed | Calendar Agent with Native Multi Step Tool | Cohere | Calendar Agent with Native Multi Step Tool | This page describes how to use cohere Chat API with list_calendar_events and create_calendar_event tools to book appointments. | Cohere, AI agents |
| /page/chunking-strategies | Allowed | Effective Chunking Strategies for RAG | Cohere | Effective Chunking Strategies for RAG | This page describes various chunking strategies you can use to get better RAG performance. | Cohere, retrieval-augmented generation, RAG |
| /page/command-a-translate | Allowed | Document Translation with Command A Translate | Cohere | Document Translation with Command A Translate | This page describes how to use Command A Translate for automated translation across 23 languages with industry-leading performance. | Cohere, AI agents |
| /page/convfinqa-finetuning-wandb | Allowed | Finetuning on Cohere's Platform | Cohere | Finetuning on Cohere's Platform | An example of finetuning using Cohere's platform and a financial dataset. | Cohere, LLMs, finetuning |
| /page/cookbooks | Allowed | Cookbooks | Cohere | Cookbooks | Explore a range of AI guides and get started with Cohere's generative platform, ready-made and best-practice optimized. | |
| /page/creating-a-qa-bot | Allowed | Creating a QA Bot From Technical Documentation | Cohere | Creating a QA Bot From Technical Documentation | This page describes how to use Cohere to build a simple question-answering system. | Cohere, AI agents, question answering systems |
| /page/csv-agent | Allowed | Financial CSV Agent with Langchain | Cohere | Financial CSV Agent with Langchain | This page describes how to use Cohere's models to build an agent able to work with CSV data. | Cohere, retrieval-augmented generation, RAG, AI agents, CSV |
| /page/csv-agent-native-api | Allowed | Financial CSV Agent with Native Multi-Step Cohere API | Cohere | Financial CSV Agent with Native Multi-Step Cohere API | This page describes how to use Cohere's models and its native API to build an agent able to work with CSV data. | Cohere, retrieval-augmented generation, RAG, AI agents, CSV |
| /page/data-analyst-agent | Allowed | A Data Analyst Agent Built with Cohere and Langchain | Cohere | A Data Analyst Agent Built with Cohere and Langchain | This page describes how to build a data-analysis system out of Cohere's models. | Cohere, AI agents, automated data analysis |
| /page/deploy-finetuned-model-aws-marketplace | Allowed | Deploy your finetuned model on AWS Marketplace | Cohere | Deploy your finetuned model on AWS Marketplace | Learn how to deploy your finetuned model on AWS Marketplace. | Cohere, LLMs, finetuning |
| /page/document-parsing-for-enterprises | Allowed | Advanced Document Parsing For Enterprises | Cohere | Advanced Document Parsing For Enterprises | This page describes how to use Cohere's models to build a document-parsing agent. | Cohere, AI agents, document parsing |
| /page/elasticsearch-and-cohere | Allowed | End-to-end RAG using Elasticsearch and Cohere | Cohere | End-to-end RAG using Elasticsearch and Cohere | This page contains a basic tutorial on how to get Cohere and ElasticSearch to work well together. | Cohere, ElasticSearch |
| /page/embed-jobs | Allowed | Semantic Search with Cohere Embed Jobs | Cohere | Semantic Search with Cohere Embed Jobs | This page contains a basic tutorial on how to use Cohere's Embed Jobs functionality. | Cohere, embed jobs |
| /page/embed-jobs-serverless-pinecone | Allowed | Serverless Semantic Search with Cohere and Pinecone | Cohere | Serverless Semantic Search with Cohere and Pinecone | This page contains a basic tutorial on how to get Cohere and the Pinecone vector database to work well together. | Cohere, Pinecone |
| /page/finetune-on-sagemaker | Allowed | Finetuning Cohere Models on AWS Sagemaker | Cohere | Finetuning Cohere Models on AWS Sagemaker | Learn how to finetune one of Cohere's models on AWS Sagemaker. | Cohere, LLMs, finetuning |
| /page/fueling-generative-content | Allowed | Fueling Generative Content with Keyword Research | Cohere | Fueling Generative Content with Keyword Research | This page contains a basic workflow for using Cohere's models to come up with keyword content ideas. | Cohere, LLMs, text generation, AI for marketing |
| /page/grounded-summarization | Allowed | Grounded Summarization Using Command R | Cohere | Grounded Summarization Using Command R | This page contains a basic tutorial on how to do grounded summarization with Cohere's models. | Cohere, summarization, grounded generations, RAG, retrieval-augmented generation |
| /page/hello-world-meet-ai | Allowed | Hello World! Explore Language AI with Cohere | Cohere | Hello World! Explore Language AI with Cohere | This page contains a breakdown of some of what can be achieved with Cohere's LLM platform. | Cohere, large language models, LLMs, generative AI |
| /page/long-form-general-strategies | Allowed | Long-Form Text Strategies with Cohere | Cohere | Long-Form Text Strategies with Cohere | This discusses ways of getting Cohere's LLM platform to perform well in generating long-form text. | Cohere, text comprehension, reading comprehension, AI, context windows |
| /page/migrate-csv-agent | Allowed | Migrating away from create_csv_agent in langchain-cohere | Cohere | Migrating away from create_csv_agent in langchain-cohere | This page contains a tutorial on how to build a CSV agent without the deprecated create_csv_agent abstraction in langchain-cohere v0.3.5 and beyond. | Cohere, CSV, AI agents |
| /page/migrating-prompts | Allowed | Migrating Monolithic Prompts to Command A with RAG | Cohere | Migrating Monolithic Prompts to Command A with RAG | This page contains a discussion of how to automatically migrating monolothic prompts. | Cohere, prompt engineering |
| /page/multilingual-search | Allowed | Multilingual Search with Cohere and Langchain | Cohere | Multilingual Search with Cohere and Langchain | This page contains a basic tutorial on how to do search across different languages with Cohere's LLM platform. | Cohere, ElasticSearch |
| /page/pdf-extractor | Allowed | PDF Extractor with Native Multi Step Tool Use | Cohere | PDF Extractor with Native Multi Step Tool Use | This page describes how to create an AI agent able to extract information from PDFs. | Cohere, PDF extraction, LLMs, AI agents |
| /page/pondr | Allowed | Pondr, Fostering Connection through Good Conversation | Cohere | Pondr, Fostering Connection through Good Conversation | This page contains a basic tutorial on how tplay an AI-powered version of the icebreaking game 'Pondr'. | Cohere, Pondr, AI games |
| /page/rag-cohere-mongodb | Allowed | Build Chatbots with MongoDB and Cohere | Cohere | Build Chatbots with MongoDB and Cohere | This page describes how to build a chatbot that provides actionable insights on technology company market reports. | Cohere, retrieval-augmented generation, RAG, chatbot |
| /page/rag-evaluation-deep-dive | Allowed | Deep Dive Into Evaluating RAG Outputs | Cohere | Deep Dive Into Evaluating RAG Outputs | This page contains information on evaluating the output of RAG systems. | Cohere, retrieval-augmented generation, RAG |
| /page/rag-with-chat-embed | Allowed | RAG With Chat Embed and Rerank via Pinecone | Cohere | RAG With Chat Embed and Rerank via Pinecone | This page contains a basic tutorial on how to build a RAG-powered chatbot. | Cohere, retrieval-augmented generation, RAG, chatbot |
| /page/rerank-demo | Allowed | Learn How Cohere's Rerank Models Work | Cohere | Learn How Cohere's Rerank Models Work | This page contains a basic tutorial on how Cohere's ReRank models work and how to use them. | Cohere, ReRank |
| /page/retrieval-eval-pydantic-ai | Allowed | Retrieval evaluation using LLM-as-a-judge via Pydantic AI | Cohere | Retrieval evaluation using LLM-as-a-judge via Pydantic AI | This page contains a tutorial on how to evaluate retrieval systems using LLMs as judges via Pydantic AI. | Cohere, retrieval evaluation, LLM-as-a-judge, Pydantic AI |
| /page/sql-agent | Allowed | Build a SQL Agent with Cohere's LLM Platform | Cohere | Build a SQL Agent with Cohere's LLM Platform | This page contains a tutorial on how to build a SQL agent with Cohere's LLM platform. | Cohere, automatic SQL generation, code generation, AI agents |
| /page/sql-agent-cohere-langchain | Allowed | SQL Agent with Cohere and LangChain (i-5O Case Study) | Cohere | SQL Agent with Cohere and LangChain (i-5O Case Study) | This page contains a tutorial on how to build a SQL agent with Cohere and LangChain in the manufacturing industry. | Cohere, automatic SQL generation, code generation, AI agents |
| /page/summarization-evals | Allowed | Evaluating Text Summarization Models | Cohere | Evaluating Text Summarization Models | This page discusses how to evaluate a model's text summarization. | Cohere, text summarization |
| /page/text-classification-using-embeddings | Allowed | Text Classification Using Embeddings | Cohere | Text Classification Using Embeddings | This page discusses the creation of a text classification model using word vector embeddings. | Cohere, text classification, classification models, word vector embeddings |
| /page/topic-modeling-ai-papers | Allowed | Topic Modeling System for AI Papers | Cohere | Topic Modeling System for AI Papers | This page discusses how to create a topic-modeling system for papers focused on AI papers. | Cohere, topic modeling, automated science |
| /page/wikipedia-search-with-weaviate | Allowed | Wikipedia Semantic Search with Cohere + Weaviate | Cohere | Wikipedia Semantic Search with Cohere + Weaviate | This page contains a description of building a Wikipedia-focused search engine with Cohere's LLM platform and the Weaviate vector database. | Cohere, Weaviate, vector databases |
| /page/wikipedia-semantic-search | Allowed | Wikipedia Semantic Search with Cohere Embedding Archives | Cohere | Wikipedia Semantic Search with Cohere Embedding Archives | This page contains a description of building a Wikipedia-focused semantic search engine with Cohere's LLM platform and the Weaviate vector database. | Cohere, Weaviate, vector databases, semantic search |
| /reference/about | Allowed | Working with Cohere's API and SDK | Cohere | Working with Cohere's API and SDK | Cohere's NLP platform provides customizable large language models and tools for developers to build AI applications. | RAG, retrieval, augmented, generation, LLM, connectors, connector, langchain |
| /reference/chat | Allowed | Chat | Cohere | Chat | Generates a text response to a user message and streams it down, token by token. | |
| /reference/chat-stream | Allowed | Chat with Streaming | Cohere | Chat with Streaming | Generates a text response to a user message. To learn how to use the Chat API and RAG follow our Text Generation guides(https://docs.cohere. | |
| /reference/classify | Allowed | Classify | Cohere | Classify | This endpoint makes a prediction about which label fits the specified text inputs best. | |
| /reference/create-embed-job | Allowed | Create an Embed Job | Cohere | Create an Embed Job | This API launches an async Embed job for a Dataset(https://docs.cohere.com/docs/datasets) of type embed-input. | |
| /reference/embed | Allowed | Embed API (v2) | Cohere | Embed API (v2) | This endpoint returns text embeddings. An embedding is a list of floating point numbers that captures semantic information about the text that it represents. | |
| /reference/errors | Allowed | Errors (status codes and description) | Cohere | Errors (status codes and description) | Understand Cohere's HTTP response codes and how to handle errors in various programming languages. | RAG, retrieval, augmented, generation, LLM, connectors, connector, langchain |
| /reference/list-connectors | Allowed | List Connectors | Cohere | List Connectors | Returns a list of connectors ordered by descending creation date (newer first). See 'Managing your Connector'(https://docs.cohere. | |
| /reference/list-models | Allowed | List Models | Cohere | List Models | Returns a list of models available for use. | |
| /reference/listfinetunedmodels | Allowed | Lists fine-tuned models. | Cohere | Lists fine-tuned models. | Returns a list of fine-tuned models that the user has access to. | |
| /reference/rerank | Allowed | Rerank API (v2) | Cohere | Rerank API (v2) | This endpoint takes in a query and a list of texts and produces an ordered array with each text assigned a relevance score. | |
| /reference/teams-and-roles | Allowed | Teams and Roles on the Cohere Platform | Cohere | Teams and Roles on the Cohere Platform | The document outlines how to work in teams on the Cohere platform, including inviting others, managing roles, and access permissions for Owners and Users. | RAG, retrieval, augmented, generation, LLM, connectors, connector, langchain |
| /reference/tokenize | Allowed | Tokenize | Cohere | Tokenize | This endpoint splits input text into smaller units called tokens using byte-pair encoding (BPE). | |
| /v1/docs/advanced-prompt-engineering-techniques | DENY (meta) | Advanced Prompt Engineering Techniques | Cohere | Advanced Prompt Engineering Techniques | This page describes advanced ways of controlling prompt engineering. | prompt engineering |
| /v1/docs/aya | Allowed | Aya Family of Models | Cohere | Aya Family of Models | Understand Cohere Labs groundbreaking multilingual Aya models, which aim to bring many more languages into generative AI. | Cohere AI, multilingual large language models, generative AI |
| /v1/docs/chat-improving-the-results | DENY (meta) | Improving the Chat Fine-tuning Results | Cohere | Improving the Chat Fine-tuning Results | Learn how to refine data, iterate on hyperparameters, and troubleshoot to fine-tune your Chat model effectively. | fine-tuning, fine-tuning language models, chat models |
| /v1/docs/cohere-embed | Allowed | Cohere's Embed Models (Details and Application) | Cohere | Cohere’s Embed Models (Details and Application) | Explore Embed models for text classification and embedding generation in English and multiple languages, with details on dimensions and endpoints. | Cohere, large language models, generative AI, embeddings |
| /v1/docs/cohere-works-everywhere | Allowed | Cohere SDK Cloud Platform Compatibility | Cohere | Cohere SDK Cloud Platform Compatibility | This page describes various places you can use Cohere's SDK. | Cohere, Cohere SDK, large language model SDK |
| /v1/docs/command-a | Allowed | Command A | Cohere | Command A | Command A is a performant mode good at tool use, RAG, agents, and multilingual use cases. It has 111 billion parameters and a 256k context length. | generative AI, Cohere, large language models |
| /v1/docs/command-r7b | Allowed | Cohere's Command R7B Model | Cohere | Cohere's Command R7B Model | Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused large language models. It excels at RAG, tool use, and agents. | generative AI, Cohere, large language models |
| /v1/docs/fine-tuning | DENY (meta) | Introduction to Fine-Tuning with Cohere Models | Cohere | Introduction to Fine-Tuning with Cohere Models | Fine-tune Cohere's large language models for specific tasks, styles, and formats with custom data. | fine-tuning language models, fine-tuning |
| /v1/docs/introduction-to-text-generation-at-cohere | Allowed | Introduction to Text Generation at Cohere | Cohere | Introduction to Text Generation at Cohere | This page describes how a large language model generates textual output. | Cohere, large language models |
| /v1/docs/models | Allowed | An Overview of Cohere's Models | Cohere | An Overview of Cohere's Models | Cohere has a variety of models that cover many different use cases. If you need more customization, you can train a model to tune it to your specific use case. | large language models, generative AI models |
| /v1/docs/multi-step-tool-use | Allowed | Multi-step Tool Use (Agents) | Cohere | Multi-step Tool Use (Agents) | "Cohere's tool use feature enhances AI capabilities by connecting external tools for dynamic, adaptable, and sequential actions." | |
| /v1/docs/overview-rag-connectors | DENY (meta) | An Overview of Cohere's RAG Connectors | Cohere | An Overview of Cohere's RAG Connectors | This page describes how to work with Cohere's retrieval-augmented generation connectors. | Cohere, retrieval augmented generation |
| /v1/docs/rate-limits | Allowed | Different Types of API Keys and Rate Limits | Cohere | Different Types of API Keys and Rate Limits | This page describes Cohere API rate limits for production and evaluation keys. | Cohere, large language model API |
| /v1/docs/rerank | Allowed | Cohere's Rerank Model (Details and Application) | Cohere | Cohere’s Rerank Model (Details and Application) | This page describes how Cohere's Rerank models work and how to use them. | Cohere, language models, rerank models |
| /v1/docs/retrieval-augmented-generation-rag | Allowed | Retrieval Augmented Generation (RAG) | Cohere | Retrieval Augmented Generation (RAG) | Generate text with external data and inline citations using Retrieval Augmented Generation and Cohere's Chat API. | retrieval augmented generation, RAG, grounded replies, text generation |
| /v1/docs/semantic-search-embed | Allowed | Semantic Search with Embeddings | Cohere | Semantic Search with Embeddings | Examples on how to use the Embed endpoint to perform semantic search (API v1). | vector embeddings, embeddings, natural language processing |
| /v1/docs/the-cohere-platform | Allowed | An Overview of The Cohere Platform | Cohere | An Overview of The Cohere Platform | Cohere offers world-class Large Language Models (LLMs) like Command, Rerank, and Embed. These help developers and enterprises build LLM-powered applications. | natural language processing, generative AI, fine-tuning models |
| /v1/page/cookbooks | Allowed | Cookbooks | Cohere | Cookbooks | Explore a range of AI guides and get started with Cohere's generative platform, ready-made and best-practice optimized. | |
| /v2/docs/building-a-chatbot-with-cohere | Allowed | Building a Chatbot with Cohere | Cohere | Building a Chatbot with Cohere | This page describes building a generative-AI powered chatbot with Cohere. | Cohere, chatbot |
| /v2/docs/building-an-agent-with-cohere | Allowed | Building a Generative AI Agent with Cohere | Cohere | Building a Generative AI Agent with Cohere | This page describes building a generative-AI powered agent with Cohere. | Cohere, agents |
| /v2/docs/chat-api | Allowed | Using the Cohere Chat API for Text Generation | Cohere | Using the Cohere Chat API for Text Generation | How to use the Chat API endpoint with Cohere LLMs to generate text responses in a conversational interface | Cohere, text generation, LLMs, generative AI |
| /v2/docs/classify-starting-the-training | DENY (meta) | Train and deploy a fine-tuned model. | Cohere | Train and deploy a fine-tuned model. | Fine-tune classification models with Cohere's Web UI or Python SDK using custom datasets. (V2) | classification models, fine-tuning language models, fine-tuning |
| /v2/docs/command-r | Allowed | Cohere's Command R Model | Cohere | Cohere's Command R Model | Command R is a conversational model that excels in language tasks and supports multiple languages, making it ideal for coding use cases. | Cohere, large language models, generative AI, command model, chat models, conversational AI |
| /v2/docs/command-r-plus | Allowed | Cohere's Command R+ Model | Cohere | Cohere’s Command R+ Model | Command R+ is Cohere's optimized for conversational interaction and long-context tasks, best suited for complex RAG workflows and multi-step tool use. | generative AI, Cohere, large language models |
| /v2/docs/command-r7b | Allowed | Cohere's Command R7B Model | Cohere | Cohere's Command R7B Model | Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused large language models. It excels at RAG, tool use, and agents. | generative AI, Cohere, large language models |
| /v2/docs/how-does-cohere-pricing-work | Allowed | How Does Cohere's Pricing Work? | Cohere | How Does Cohere's Pricing Work? | This page details Cohere's pricing model. Our models can be accessed directly through our API, allowing for the creation of scalable production workloads. | Cohere, large language model pricing |
| /v2/docs/migrating-v1-to-v2 | Allowed | Migrating From API v1 to API v2 | Cohere | Migrating From API v1 to API v2 | The document serves as a reference for developers looking to update their existing Cohere API v1 implementations to the new v2 standard. | Cohere, text generation, LLMs, generative AI |
| /v2/docs/models | Allowed | An Overview of Cohere's Models | Cohere | An Overview of Cohere's Models | Cohere has a variety of models that cover many different use cases. If you need more customization, you can train a model to tune it to your specific use case. | large language models, generative AI models |
| /v2/docs/rag-with-cohere | Allowed | Building RAG models with Cohere | Cohere | Building RAG models with Cohere | This page walks through building a retrieval-augmented generation model with Cohere. | Cohere, retrieval-augmented generation, RAG |
| /v2/docs/rate-limits | Allowed | Different Types of API Keys and Rate Limits | Cohere | Different Types of API Keys and Rate Limits | This page describes Cohere API rate limits for production and evaluation keys. | Cohere, large language model API |
| /v2/docs/reranking-with-cohere | Allowed | Master Reranking with Cohere Models | Cohere | Master Reranking with Cohere Models | This page contains a tutorial on using Cohere's ReRank models. | Cohere, language models, ReRank models |
| /v2/docs/retrieval-augmented-generation-rag | Allowed | Retrieval Augmented Generation (RAG) | Cohere | Retrieval Augmented Generation (RAG) | Guide on using Cohere's Retrieval Augmented Generation (RAG) capabilities such as document grounding and citations. | retrieval augmented generation, RAG, grounded replies, text generation |
| /v2/docs/safety-modes | Allowed | Safety Modes | Cohere | Safety Modes | The safety modes documentation describes how to use default and strict modes in order to exercise additional control over model output. | AI safety, AI risk, responsible AI, Cohere |
| /v2/docs/semantic-search-with-cohere | Allowed | Semantic Search with Cohere Models | Cohere | Semantic Search with Cohere Models | This is a tutorial describing how to leverage Cohere's models for semantic search. | Cohere, language models, |
| /v2/docs/structured-outputs | Allowed | How do Structured Outputs Work? | Cohere | How do Structured Outputs Work? | This page describes how to get Cohere models to create outputs in a certain format, such as JSON, TOOLS, using parameters such as response_format. | Cohere, language models, structured outputs, the response format parameter |
| /v2/docs/text-generation-tutorial | Allowed | Cohere Text Generation Tutorial | Cohere | Cohere Text Generation Tutorial | This page walks through how Cohere's generation models work and how to use them. | Cohere, how do LLMs generate text |
| No rows found, please edit your search term. | |||||
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| URL 🔼 | OG Title | OG Description | OG Image | Twitter Title | Twitter Description | Twitter Image |
|---|---|---|---|---|---|---|
| / | Cohere Documentation | Cohere | Cohere's API documentation helps developers easily integrate natural language processing and generation into their products. | Cohere Documentation | Cohere | Cohere's API documentation helps developers easily integrate natural language processing and generation into their products. | ||
| /docs/advanced-generation-hyperparameters | Advanced Generation Parameters | Cohere | This page describes advanced parameters for controlling generation. | Advanced Generation Parameters | Cohere | This page describes advanced parameters for controlling generation. | ||
| /docs/agentic-rag | Building Agentic RAG with Cohere | Cohere | Hands-on tutorials on building agentic RAG applications with Cohere | Building Agentic RAG with Cohere | Cohere | Hands-on tutorials on building agentic RAG applications with Cohere | ||
| /docs/aya | Aya Family of Models | Cohere | Understand Cohere Labs groundbreaking multilingual Aya models, which aim to bring many more languages into generative AI. | Aya Family of Models | Cohere | Understand Cohere Labs groundbreaking multilingual Aya models, which aim to bring many more languages into generative AI. | ||
| /docs/build-things-with-cohere | Build an Onboarding Assistant with Cohere! | Cohere | This page describes how to build an onboarding assistant with Cohere's large language models. | Build an Onboarding Assistant with Cohere! | Cohere | This page describes how to build an onboarding assistant with Cohere's large language models. | ||
| /docs/chat-api | Using the Cohere Chat API for Text Generation | Cohere | How to use the Chat API endpoint with Cohere LLMs to generate text responses in a conversational interface | Using the Cohere Chat API for Text Generation | Cohere | How to use the Chat API endpoint with Cohere LLMs to generate text responses in a conversational interface | ||
| /docs/chat-fine-tuning | Fine-tuning for Cohere's Chat Model | Cohere | This document provides guidance on fine-tuning, evaluating, and improving chat models. | Fine-tuning for Cohere's Chat Model | Cohere | This document provides guidance on fine-tuning, evaluating, and improving chat models. | ||
| /docs/chat-improving-the-results | Improving the Chat Fine-tuning Results | Cohere | Learn how to refine data, iterate on hyperparameters, and troubleshoot to fine-tune your Chat model effectively. | Improving the Chat Fine-tuning Results | Cohere | Learn how to refine data, iterate on hyperparameters, and troubleshoot to fine-tune your Chat model effectively. | ||
| /docs/chat-on-langchain | Cohere Chat on LangChain (Integration Guide) | Cohere | Integrate Cohere with LangChain to build applications using Cohere's models and LangChain tools. | Cohere Chat on LangChain (Integration Guide) | Cohere | Integrate Cohere with LangChain to build applications using Cohere's models and LangChain tools. | ||
| /docs/chat-preparing-the-data | Preparing the Chat Fine-tuning Data | Cohere | Prepare your data for fine-tuning a Command model for Chat with this step-by-step guide, including data formatting, requirements, and best practices. | Preparing the Chat Fine-tuning Data | Cohere | Prepare your data for fine-tuning a Command model for Chat with this step-by-step guide, including data formatting, requirements, and best practices. | ||
| /docs/chat-starting-the-training | Starting the Chat Fine-Tuning Run | Cohere | Learn how to fine-tune a Command model for chat with the Cohere Web UI or Python SDK, including data requirements, pricing, and calling your model. | Starting the Chat Fine-Tuning Run | Cohere | Learn how to fine-tune a Command model for chat with the Cohere Web UI or Python SDK, including data requirements, pricing, and calling your model. | ||
| /docs/chat-understanding-the-results | Understanding the Chat Fine-tuning Results | Cohere | Learn how to evaluate and troubleshoot a fine-tuned chat model with accuracy and loss metrics. | Understanding the Chat Fine-tuning Results | Cohere | Learn how to evaluate and troubleshoot a fine-tuned chat model with accuracy and loss metrics. | ||
| /docs/chroma-and-cohere | Chroma and Cohere (Integration Guide) | Cohere | This page describes how to integrate Cohere and Chroma. | Chroma and Cohere (Integration Guide) | Cohere | This page describes how to integrate Cohere and Chroma. | ||
| /docs/classify-fine-tuning | Fine-tuning for Cohere's Classify Model | Cohere | This document provides guidance on fine-tuning, evaluating, and improving classification models. | Fine-tuning for Cohere's Classify Model | Cohere | This document provides guidance on fine-tuning, evaluating, and improving classification models. | ||
| /docs/classify-improving-the-results | Improving the Classify Fine-tuning Results | Cohere | Troubleshoot your fine-tuned classification model with these tips for refining data quality and improving results. | Improving the Classify Fine-tuning Results | Cohere | Troubleshoot your fine-tuned classification model with these tips for refining data quality and improving results. | ||
| /docs/classify-preparing-the-data | Preparing the Classify Fine-tuning data | Cohere | Learn how to prepare your data for fine-tuning classification models, including single-label and multi-label data formats and dataset cleaning tips. | Preparing the Classify Fine-tuning data | Cohere | Learn how to prepare your data for fine-tuning classification models, including single-label and multi-label data formats and dataset cleaning tips. | ||
| /docs/classify-starting-the-training | Train and deploy a fine-tuned model. | Cohere | Fine-tune classification models with Cohere's Web UI or Python SDK using custom datasets. (V2) | Train and deploy a fine-tuned model. | Cohere | Fine-tune classification models with Cohere's Web UI or Python SDK using custom datasets. (V2) | ||
| /docs/classify-understanding-the-results | Understanding the Classify Fine-tuning Results | Cohere | Understand the performance metrics for a fine-tuned classification model and learn how to interpret its accuracy, precision, recall, and F1 scores. | Understanding the Classify Fine-tuning Results | Cohere | Understand the performance metrics for a fine-tuned classification model and learn how to interpret its accuracy, precision, recall, and F1 scores. | ||
| /docs/cohere-and-langchain | Cohere and LangChain (Integration Guide) | Cohere | Integrate Cohere with LangChain for advanced chat features, RAG, embeddings, and reranking; this guide includes code examples for each feature. | Cohere and LangChain (Integration Guide) | Cohere | Integrate Cohere with LangChain for advanced chat features, RAG, embeddings, and reranking; this guide includes code examples for each feature. | ||
| /docs/cohere-embed | Cohere's Embed Models (Details and Application) | Cohere | Explore Embed models for text classification and embedding generation in English and multiple languages, with details on dimensions and endpoints. | Cohere's Embed Models (Details and Application) | Cohere | Explore Embed models for text classification and embedding generation in English and multiple languages, with details on dimensions and endpoints. | ||
| /docs/cohere-faqs | Frequently Asked Questions About Cohere | Cohere | Cohere is a powerful platform for using Large Language Models (LLMs). This page covers FAQs related to functionality, pricing, troubleshooting, and more. | Frequently Asked Questions About Cohere | Cohere | Cohere is a powerful platform for using Large Language Models (LLMs). This page covers FAQs related to functionality, pricing, troubleshooting, and more. | ||
| /docs/cohere-labs-acceptable-use-policy | Cohere Labs Acceptable Use Policy | Cohere | "Promoting safe and ethical use of generative AI with guidelines to prevent misuse and abuse." | Cohere Labs Acceptable Use Policy | Cohere | "Promoting safe and ethical use of generative AI with guidelines to prevent misuse and abuse." | ||
| /docs/cohere-on-azure/cohere-on-azure-ai-foundry | Introduction to Cohere on Azure AI Foundry | Cohere | An introduction to Cohere on Azure AI Foundry, a fully managed service by Azure (API v2). | Introduction to Cohere on Azure AI Foundry | Cohere | An introduction to Cohere on Azure AI Foundry, a fully managed service by Azure (API v2). | ||
| /docs/cohere-toolkit | How to Start with the Cohere Toolkit | Cohere | Build and deploy RAG applications quickly with the Cohere Toolkit, which offers pre-built front-end and back-end components. | How to Start with the Cohere Toolkit | Cohere | Build and deploy RAG applications quickly with the Cohere Toolkit, which offers pre-built front-end and back-end components. | ||
| /docs/cohere-works-everywhere | Cohere SDK Cloud Platform Compatibility | Cohere | This page describes various places you can use Cohere's SDK. | Cohere SDK Cloud Platform Compatibility | Cohere | This page describes various places you can use Cohere's SDK. | ||
| /docs/command-a | Command A | Cohere | Command A is a performant mode good at tool use, RAG, agents, and multilingual use cases. It has 111 billion parameters and a 256k context length. | Command A | Cohere | Command A is a performant mode good at tool use, RAG, agents, and multilingual use cases. It has 111 billion parameters and a 256k context length. | ||
| /docs/command-a-reasoning | Cohere's Command A Reasoning Model | Cohere | Command A Reasoning excels in tool use, agentic workflows, and complex problem-solving. It has 111 billion parameters and a 256k context length. | Cohere's Command A Reasoning Model | Cohere | Command A Reasoning excels in tool use, agentic workflows, and complex problem-solving. It has 111 billion parameters and a 256k context length. | ||
| /docs/command-r | Cohere's Command R Model | Cohere | Command R is a conversational model that excels in language tasks and supports multiple languages, making it ideal for coding use cases. | Cohere's Command R Model | Cohere | Command R is a conversational model that excels in language tasks and supports multiple languages, making it ideal for coding use cases. | ||
| /docs/command-r-plus | Cohere's Command R+ Model | Cohere | Command R+ is Cohere's optimized for conversational interaction and long-context tasks, best suited for complex RAG workflows and multi-step tool use. | Cohere's Command R+ Model | Cohere | Command R+ is Cohere's optimized for conversational interaction and long-context tasks, best suited for complex RAG workflows and multi-step tool use. | ||
| /docs/command-r7b | Cohere's Command R7B Model | Cohere | Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused large language models. It excels at RAG, tool use, and agents. | Cohere's Command R7B Model | Cohere | Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused large language models. It excels at RAG, tool use, and agents. | ||
| /docs/compatibility-api | Using Cohere models via the OpenAI SDK | Cohere | The document serves as a guide for Cohere's Compatibility API, which allows developers to seamlessly use Cohere's models using OpenAI's SDK. | Using Cohere models via the OpenAI SDK | Cohere | The document serves as a guide for Cohere's Compatibility API, which allows developers to seamlessly use Cohere's models using OpenAI's SDK. | ||
| /docs/contribute | Help Us Improve The Cohere Docs | Cohere | Contribute to our docs content, stored in the cohere-developer-experience repo; we welcome your pull requests! | Help Us Improve The Cohere Docs | Cohere | Contribute to our docs content, stored in the cohere-developer-experience repo; we welcome your pull requests! | ||
| /docs/cookbooks | Cohere Cookbooks: Build AI Agents and Solutions | Cohere | Get started with Cohere's cookbooks to build agents, QA bots, perform searches, and more, all organized by category. | Cohere Cookbooks: Build AI Agents and Solutions | Cohere | Get started with Cohere's cookbooks to build agents, QA bots, perform searches, and more, all organized by category. | ||
| /docs/create-client | Creating a client | Cohere | A guide for creating Cohere API client using Cohere SDK, supported in 4 different languages – Python, TypeScript, Java, and Go. | Creating a client | Cohere | A guide for creating Cohere API client using Cohere SDK, supported in 4 different languages – Python, TypeScript, Java, and Go. | ||
| /docs/datasets | The Cohere Datasets API (and How to Use It) | Cohere | Learn about the Dataset API, including its file size limits, data retention, creation, validation, metadata, and more, with provided code snippets. | The Cohere Datasets API (and How to Use It) | Cohere | Learn about the Dataset API, including its file size limits, data retention, creation, validation, metadata, and more, with provided code snippets. | ||
| /docs/deployment-options-overview | Deployment Options - Overview | Cohere | This page provides an overview of the available options for deploying Cohere's models. | Deployment Options - Overview | Cohere | This page provides an overview of the available options for deploying Cohere's models. | ||
| /docs/deprecations | Deprecations | Cohere | Learn about Cohere's deprecation policies and recommended replacements | Deprecations | Cohere | Learn about Cohere's deprecation policies and recommended replacements | ||
| /docs/documents-and-citations | Documents and Citations | Cohere | The document introduces RAG as a method to improve language model responses by providing source material for context. | Documents and Citations | Cohere | The document introduces RAG as a method to improve language model responses by providing source material for context. | ||
| /docs/embed-jobs-api | Batch Embedding Jobs with the Embed API | Cohere | Learn how to use the Embed Jobs API to handle large text data efficiently with a focus on creating datasets and running embed jobs. | Batch Embedding Jobs with the Embed API | Cohere | Learn how to use the Embed Jobs API to handle large text data efficiently with a focus on creating datasets and running embed jobs. | ||
| /docs/embed-on-langchain | Cohere Embed on LangChain (Integration Guide) | Cohere | This page describes how to work with Cohere's embeddings models and LangChain. | Cohere Embed on LangChain (Integration Guide) | Cohere | This page describes how to work with Cohere's embeddings models and LangChain. | ||
| /docs/embeddings | Introduction to Embeddings at Cohere | Cohere | Embeddings transform text into numerical data, enabling language-agnostic similarity searches and efficient storage with compression. | Introduction to Embeddings at Cohere | Cohere | Embeddings transform text into numerical data, enabling language-agnostic similarity searches and efficient storage with compression. | ||
| /docs/fine-tuning | Introduction to Fine-Tuning with Cohere Models | Cohere | Fine-tune Cohere's large language models for specific tasks, styles, and formats with custom data. | Introduction to Fine-Tuning with Cohere Models | Cohere | Fine-tune Cohere's large language models for specific tasks, styles, and formats with custom data. | ||
| /docs/fine-tuning-with-the-python-sdk | Programmatic Fine-tuning with Cohere's Python SDK | Cohere | Fine-tune models using the Cohere Python SDK programmatically and monitor the results through the Dashboard Web UI. | Programmatic Fine-tuning with Cohere's Python SDK | Cohere | Fine-tune models using the Cohere Python SDK programmatically and monitor the results through the Dashboard Web UI. | ||
| /docs/foundation-models | Foundational Models | Cohere | In this chapter, you'll get an overview of Cohere's foundation models. | Foundational Models | Cohere | In this chapter, you'll get an overview of Cohere's foundation models. | ||
| /docs/generate-fine-tuning | Fine-tuning for Generate | Cohere | This document provides guidance on fine-tuning, evaluating, and improving generative models. | Fine-tuning for Generate | Cohere | This document provides guidance on fine-tuning, evaluating, and improving generative models. | ||
| /docs/get-started-installation | Installation | Cohere | A guide for installing the Cohere SDK, supported in 4 different languages – Python, TypeScript, Java, and Go. | Installation | Cohere | A guide for installing the Cohere SDK, supported in 4 different languages – Python, TypeScript, Java, and Go. | ||
| /docs/going-live | Going Live with a Cohere Model | Cohere | Learn to upgrade from a Trial to a Production key; understand the limitations and benefits of each and go live with Cohere. | Going Live with a Cohere Model | Cohere | Learn to upgrade from a Trial to a Production key; understand the limitations and benefits of each and go live with Cohere. | ||
| /docs/how-does-cohere-pricing-work | How Does Cohere's Pricing Work? | Cohere | This page details Cohere's pricing model. Our models can be accessed directly through our API, allowing for the creation of scalable production workloads. | How Does Cohere's Pricing Work? | Cohere | This page details Cohere's pricing model. Our models can be accessed directly through our API, allowing for the creation of scalable production workloads. | ||
| /docs/image-inputs | Using Cohere's Models to Work with Image Inputs | Cohere | This page describes how a Cohere large language model works with image inputs. It covers passing images with the API, limitations, and best practices. | Using Cohere's Models to Work with Image Inputs | Cohere | This page describes how a Cohere large language model works with image inputs. It covers passing images with the API, limitations, and best practices. | ||
| /docs/integrations | Integrating Embedding Models with Other Tools | Cohere | Learn how to integrate Cohere embeddings with open-source vector search engines for enhanced applications. | Integrating Embedding Models with Other Tools | Cohere | Learn how to integrate Cohere embeddings with open-source vector search engines for enhanced applications. | ||
| /docs/introduction-to-text-generation-at-cohere | Introduction to Text Generation at Cohere | Cohere | This page describes how a large language model generates textual output. | Introduction to Text Generation at Cohere | Cohere | This page describes how a large language model generates textual output. | ||
| /docs/llamaindex | LlamaIndex and Cohere's Models | Cohere | Learn how to use Cohere and LlamaIndex together to generate responses based on data. | LlamaIndex and Cohere's Models | Cohere | Learn how to use Cohere and LlamaIndex together to generate responses based on data. | ||
| /docs/llmu-2 | Welcome to LLM University! | Cohere | LLM University (LLMU) offers in-depth, practical NLP and LLM training. Ideal for all skill levels. Learn, build, and deploy Language AI with Cohere. | Welcome to LLM University! | Cohere | LLM University (LLMU) offers in-depth, practical NLP and LLM training. Ideal for all skill levels. Learn, build, and deploy Language AI with Cohere. | ||
| /docs/migrating-v1-to-v2 | Migrating From API v1 to API v2 | Cohere | The document serves as a reference for developers looking to update their existing Cohere API v1 implementations to the new v2 standard. | Migrating From API v1 to API v2 | Cohere | The document serves as a reference for developers looking to update their existing Cohere API v1 implementations to the new v2 standard. | ||
| /docs/models | An Overview of Cohere's Models | Cohere | Cohere has a variety of models that cover many different use cases. If you need more customization, you can train a model to tune it to your specific use case. | An Overview of Cohere's Models | Cohere | Cohere has a variety of models that cover many different use cases. If you need more customization, you can train a model to tune it to your specific use case. | ||
| /docs/multimodal-embeddings | Unlocking the Power of Multimodal Embeddings | Cohere | Multimodal embeddings convert text and images into embeddings for search and classification (API v2). | Unlocking the Power of Multimodal Embeddings | Cohere | Multimodal embeddings convert text and images into embeddings for search and classification (API v2). | ||
| /docs/parameter-types-in-json | Parameter Types in Structured Outputs (JSON) | Cohere | This page shows usage examples of the JSON Schema parameter types supported in Structured Outputs (JSON). | Parameter Types in Structured Outputs (JSON) | Cohere | This page shows usage examples of the JSON Schema parameter types supported in Structured Outputs (JSON). | ||
| /docs/playground-overview | An Overview of the Developer Playground | Cohere | The Cohere Playground is a powerful visual interface for testing Cohere's generation and embedding language models without coding. | An Overview of the Developer Playground | Cohere | The Cohere Playground is a powerful visual interface for testing Cohere's generation and embedding language models without coding. | ||
| /docs/predictable-outputs | How to Get Predictable Outputs with Cohere Models | Cohere | Strategies for decoding text, and the parameters that impact the randomness and predictability of a language model's output. | How to Get Predictable Outputs with Cohere Models | Cohere | Strategies for decoding text, and the parameters that impact the randomness and predictability of a language model's output. | ||
| /docs/rag-citations | RAG Citations | Cohere | Guide on accessing and utilizing citations generated by the Cohere Chat endpoint for RAG. It covers both non-streaming and streaming modes (API v2). | RAG Citations | Cohere | Guide on accessing and utilizing citations generated by the Cohere Chat endpoint for RAG. It covers both non-streaming and streaming modes (API v2). | ||
| /docs/rate-limits | Different Types of API Keys and Rate Limits | Cohere | This page describes Cohere API rate limits for production and evaluation keys. | Different Types of API Keys and Rate Limits | Cohere | This page describes Cohere API rate limits for production and evaluation keys. | ||
| /docs/reasoning | Reasoning Capabilities | Cohere | Reasoning models excel at tool use, agentic workflows, and complex problem-solving. This page provides a general overview of Cohere's reasoning capalities. | Reasoning Capabilities | Cohere | Reasoning models excel at tool use, agentic workflows, and complex problem-solving. This page provides a general overview of Cohere's reasoning capalities. | ||
| /docs/rerank | Cohere's Rerank Model (Details and Application) | Cohere | This page describes how Cohere's Rerank models work and how to use them. | Cohere's Rerank Model (Details and Application) | Cohere | This page describes how Cohere's Rerank models work and how to use them. | ||
| /docs/rerank-fine-tuning | Fine-tuning for Cohere's Rerank Model | Cohere | This document provides guidance on fine-tuning, evaluating, and improving rerank models. | Fine-tuning for Cohere's Rerank Model | Cohere | This document provides guidance on fine-tuning, evaluating, and improving rerank models. | ||
| /docs/rerank-improving-the-results | Improving the Rerank Fine-tuning Results | Cohere | Tips for achieving the best fine-tuned rerank model and troubleshooting guide for fine-tuned models. | Improving the Rerank Fine-tuning Results | Cohere | Tips for achieving the best fine-tuned rerank model and troubleshooting guide for fine-tuned models. | ||
| /docs/rerank-on-langchain | Cohere Rerank on LangChain (Integration Guide) | Cohere | This page describes how to integrate Cohere's ReRank models with LangChain. | Cohere Rerank on LangChain (Integration Guide) | Cohere | This page describes how to integrate Cohere's ReRank models with LangChain. | ||
| /docs/rerank-preparing-the-data | Preparing the Rerank Fine-tuning Data | Cohere | Learn how to prepare and format your data for fine-tuning Cohere's Rerank model. | Preparing the Rerank Fine-tuning Data | Cohere | Learn how to prepare and format your data for fine-tuning Cohere's Rerank model. | ||
| /docs/rerank-starting-the-training | Starting the Rerank Fine-Tuning | Cohere | How to start training a fine-tuning model for Rerank using both the Web UI and the Python SDK. | Starting the Rerank Fine-Tuning | Cohere | How to start training a fine-tuning model for Rerank using both the Web UI and the Python SDK. | ||
| /docs/rerank-understanding-the-results | Understanding the Rerank Fine-tuning Results | Cohere | Understand how fine-tuned models for Rerank are evaluated, and learn about the specific metrics used, including Accuracy, MRR, and nDCG. | Understanding the Rerank Fine-tuning Results | Cohere | Understand how fine-tuned models for Rerank are evaluated, and learn about the specific metrics used, including Accuracy, MRR, and nDCG. | ||
| /docs/reranking-best-practices | Best Practices for using Rerank | Cohere | Tips for optimal endpoint performance, including constraints on the number of documents, tokens per document, and tokens per query. | Best Practices for using Rerank | Cohere | Tips for optimal endpoint performance, including constraints on the number of documents, tokens per document, and tokens per query. | ||
| /docs/responsible-use | Command R and Command R+ Model Card | Cohere | This doc provides guidelines for using Cohere generation models ethically and constructively. | Command R and Command R+ Model Card | Cohere | This doc provides guidelines for using Cohere generation models ethically and constructively. | ||
| /docs/retrieval-augmented-generation-rag | Retrieval Augmented Generation (RAG) | Cohere | Guide on using Cohere's Retrieval Augmented Generation (RAG) capabilities such as document grounding and citations. | Retrieval Augmented Generation (RAG) | Cohere | Guide on using Cohere's Retrieval Augmented Generation (RAG) capabilities such as document grounding and citations. | ||
| /docs/safety-modes | Safety Modes | Cohere | The safety modes documentation describes how to use default and strict modes in order to exercise additional control over model output. | Safety Modes | Cohere | The safety modes documentation describes how to use default and strict modes in order to exercise additional control over model output. | ||
| /docs/semantic-search | Semantic Search | Cohere | This document provides a guide on building a simple semantic search engine using language models to search by meaning. It includes steps to embed text, build an index, conduct nearest neighbor search, and visualize the results. | Semantic Search | Cohere | This document provides a guide on building a simple semantic search engine using language models to search by meaning. It includes steps to embed text, build an index, conduct nearest neighbor search, and visualize the results. | ||
| /docs/semantic-search-embed | Semantic Search with Embeddings | Cohere | Examples on how to use the Embed endpoint to perform semantic search (API v2). | Semantic Search with Embeddings | Cohere | Examples on how to use the Embed endpoint to perform semantic search (API v2). | ||
| /docs/serving-platform | Serving Platform | Cohere | In this chapter, you'll get an overview of Cohere's serving platform. | Serving Platform | Cohere | In this chapter, you'll get an overview of Cohere's serving platform. | ||
| /docs/streaming | A Guide to Streaming Responses | Cohere | The document explains how the Chat API can stream events like text generation in real-time. | A Guide to Streaming Responses | Cohere | The document explains how the Chat API can stream events like text generation in real-time. | ||
| /docs/structured-outputs | How do Structured Outputs Work? | Cohere | This page describes how to get Cohere models to create outputs in a certain format, such as JSON, TOOLS, using parameters such as response_format. | How do Structured Outputs Work? | Cohere | This page describes how to get Cohere models to create outputs in a certain format, such as JSON, TOOLS, using parameters such as response_format. | ||
| /docs/summarizing-text | Summarizing Text with the Chat Endpoint | Cohere | Learn how to perform text summarization using Cohere's Chat endpoint with features like length control and RAG. | Summarizing Text with the Chat Endpoint | Cohere | Learn how to perform text summarization using Cohere's Chat endpoint with features like length control and RAG. | ||
| /docs/supported-languages | Supported Languages | Cohere | A list of languages that Cohere's multilingual embedding model provides. | Supported Languages | Cohere | A list of languages that Cohere's multilingual embedding model provides. | ||
| /docs/text-generation-tutorial | Cohere Text Generation Tutorial | Cohere | This page walks through how Cohere's generation models work and how to use them. | Cohere Text Generation Tutorial | Cohere | This page walks through how Cohere's generation models work and how to use them. | ||
| /docs/the-cohere-platform | An Overview of The Cohere Platform | Cohere | Cohere offers world-class Large Language Models (LLMs) like Command, Rerank, and Embed. These help developers and enterprises build LLM-powered applications. | An Overview of The Cohere Platform | Cohere | Cohere offers world-class Large Language Models (LLMs) like Command, Rerank, and Embed. These help developers and enterprises build LLM-powered applications. | ||
| /docs/tokens-and-tokenizers | A Guide to Tokens and Tokenizers | Cohere | This document describes how to use the tokenize and detokenize API endpoints. | A Guide to Tokens and Tokenizers | Cohere | This document describes how to use the tokenize and detokenize API endpoints. | ||
| /docs/tool-use-citations | Citations for tool use (function calling) | Cohere | Guide on accessing and utilizing citations generated by the Cohere Chat endpoint for tool use. It covers both non-streaming and streaming modes (API v2). | Citations for tool use (function calling) | Cohere | Guide on accessing and utilizing citations generated by the Cohere Chat endpoint for tool use. It covers both non-streaming and streaming modes (API v2). | ||
| /docs/tool-use-overview | Basic usage of tool use (function calling) | Cohere | An overview of using Cohere's tool use capabilities, enabling developers to build agentic workflows (API v2). | Basic usage of tool use (function calling) | Cohere | An overview of using Cohere's tool use capabilities, enabling developers to build agentic workflows (API v2). | ||
| /docs/tool-use-parameter-types | Parameter types for tool use (function calling) | Cohere | Guide on using structured outputs with tool parameters in the Cohere Chat API. Includes guide on supported parameter types and usage examples (API v2). | Parameter types for tool use (function calling) | Cohere | Guide on using structured outputs with tool parameters in the Cohere Chat API. Includes guide on supported parameter types and usage examples (API v2). | ||
| /docs/tool-use-streaming | Streaming for tool use (function calling) | Cohere | Guide on implementing streaming for tool use in Cohere's platform and details on the events stream (API v2). | Streaming for tool use (function calling) | Cohere | Guide on implementing streaming for tool use in Cohere's platform and details on the events stream (API v2). | ||
| /docs/tool-use-usage-patterns | Usage patterns for tool use (function calling) | Cohere | Guide on implementing various tool use patterns with the Cohere Chat endpoint such as parallel tool calling, multi-step tool use, and more (API v2). | Usage patterns for tool use (function calling) | Cohere | Guide on implementing various tool use patterns with the Cohere Chat endpoint such as parallel tool calling, multi-step tool use, and more (API v2). | ||
| /docs/tools | An Overview of Tool Use with Cohere | Cohere | Learn when to use leverage multi-step tool use in your workflows. | An Overview of Tool Use with Cohere | Cohere | Learn when to use leverage multi-step tool use in your workflows. | ||
| /docs/tools-on-langchain | Cohere Tools on LangChain (Integration Guide) | Cohere | Explore code examples for multi-step and single-step tool usage in chatbots, harnessing internet search and vector storage. | Cohere Tools on LangChain (Integration Guide) | Cohere | Explore code examples for multi-step and single-step tool usage in chatbots, harnessing internet search and vector storage. | ||
| /docs/usage-policy | Usage Policy | Cohere | Developers must outline and get approval for their use case to access the Cohere API, understanding the models and limitations. They should refer to model cards for detailed information and document potential harms of their application. Certain use cases, such as violence, hate speech, fraud, and privacy violations, are strictly prohibited. | Usage Policy | Cohere | Developers must outline and get approval for their use case to access the Cohere API, understanding the models and limitations. They should refer to model cards for detailed information and document potential harms of their application. Certain use cases, such as violence, hate speech, fraud, and privacy violations, are strictly prohibited. | ||
| /page/agent-api-calls | Building an LLM Agent with the Cohere API | Cohere | This page how to use Cohere's API to build an LLM-based agent. | Building an LLM Agent with the Cohere API | Cohere | This page how to use Cohere's API to build an LLM-based agent. | ||
| /page/agent-short-term-memory | Short-Term Memory Handling for Agents | Cohere | This page describes how to manage short-term memory in an agent built with Cohere models. | Short-Term Memory Handling for Agents | Cohere | This page describes how to manage short-term memory in an agent built with Cohere models. | ||
| /page/agentic-multi-stage-rag | Agentic Multi-Stage RAG with Cohere Tools API | Cohere | This page describes how to build a powerful, multi-stage agent with the Cohere platform. | Agentic Multi-Stage RAG with Cohere Tools API | Cohere | This page describes how to build a powerful, multi-stage agent with the Cohere platform. | ||
| /page/agentic-rag-mixed-data | Agentic RAG for PDFs with mixed data | Cohere | This page describes building a powerful, multi-step chatbot with Cohere's models. | Agentic RAG for PDFs with mixed data | Cohere | This page describes building a powerful, multi-step chatbot with Cohere's models. | ||
| /page/analysis-of-financial-forms | Analysis of Form 10-K/10-Q Using Cohere and RAG | Cohere | This page describes how to use Cohere's large language models to build an agent able to analyze financial forms like a 10-K or a 10-Q. | Analysis of Form 10-K/10-Q Using Cohere and RAG | Cohere | This page describes how to use Cohere's large language models to build an agent able to analyze financial forms like a 10-K or a 10-Q. | ||
| /page/analyzing-hacker-news | Analyzing Hacker News with Cohere | Cohere | This page describes building a generative-AI powered tool to analyze headlines with Cohere. | Analyzing Hacker News with Cohere | Cohere | This page describes building a generative-AI powered tool to analyze headlines with Cohere. | ||
| /page/article-recommender-with-text-embeddings | Article Recommender via Embedding & Classification | Cohere | This page describes how to build a generative-AI tool to recommend articles with Cohere. | Article Recommender via Embedding & Classification | Cohere | This page describes how to build a generative-AI tool to recommend articles with Cohere. | ||
| /page/aya-vision-intro | Introduction to Aya Vision | Cohere | In this notebook, we will explore the capabilities of Aya Vision, which can take text and image inputs to generates text responses. | Introduction to Aya Vision | Cohere | In this notebook, we will explore the capabilities of Aya Vision, which can take text and image inputs to generates text responses. | ||
| /page/basic-multi-step | Multi-Step Tool Use with Cohere | Cohere | This page describes how to create a multi-step, tool-using AI agent with Cohere's tool use functionality. | Multi-Step Tool Use with Cohere | Cohere | This page describes how to create a multi-step, tool-using AI agent with Cohere's tool use functionality. | ||
| /page/basic-rag | Basic RAG: Retrieval-Augmented Generation with Cohere | Cohere | This page describes how to work with Cohere's basic retrieval-augmented generation functionality. | Basic RAG: Retrieval-Augmented Generation with Cohere | Cohere | This page describes how to work with Cohere's basic retrieval-augmented generation functionality. | ||
| /page/basic-semantic-search | Basic Semantic Search with Cohere Models | Cohere | This page describes how to do basic semantic search with Cohere's models. | Basic Semantic Search with Cohere Models | Cohere | This page describes how to do basic semantic search with Cohere's models. | ||
| /page/basic-tool-use | Getting Started with Basic Tool Use | Cohere | This page describes how to work with Cohere's basic tool use functionality. | Getting Started with Basic Tool Use | Cohere | This page describes how to work with Cohere's basic tool use functionality. | ||
| /page/calendar-agent | Calendar Agent with Native Multi Step Tool | Cohere | This page describes how to use cohere Chat API with list_calendar_events and create_calendar_event tools to book appointments. | Calendar Agent with Native Multi Step Tool | Cohere | This page describes how to use cohere Chat API with list_calendar_events and create_calendar_event tools to book appointments. | ||
| /page/chunking-strategies | Effective Chunking Strategies for RAG | Cohere | This page describes various chunking strategies you can use to get better RAG performance. | Effective Chunking Strategies for RAG | Cohere | This page describes various chunking strategies you can use to get better RAG performance. | ||
| /page/command-a-translate | Document Translation with Command A Translate | Cohere | This page describes how to use Command A Translate for automated translation across 23 languages with industry-leading performance. | Document Translation with Command A Translate | Cohere | This page describes how to use Command A Translate for automated translation across 23 languages with industry-leading performance. | ||
| /page/convfinqa-finetuning-wandb | Finetuning on Cohere's Platform | Cohere | An example of finetuning using Cohere's platform and a financial dataset. | Finetuning on Cohere's Platform | Cohere | An example of finetuning using Cohere's platform and a financial dataset. | ||
| /page/cookbooks | Cookbooks | Cohere | Explore a range of AI guides and get started with Cohere's generative platform, ready-made and best-practice optimized. | Cookbooks | Cohere | Explore a range of AI guides and get started with Cohere's generative platform, ready-made and best-practice optimized. | ||
| /page/creating-a-qa-bot | Creating a QA Bot From Technical Documentation | Cohere | This page describes how to use Cohere to build a simple question-answering system. | Creating a QA Bot From Technical Documentation | Cohere | This page describes how to use Cohere to build a simple question-answering system. | ||
| /page/csv-agent | Financial CSV Agent with Langchain | Cohere | This page describes how to use Cohere's models to build an agent able to work with CSV data. | Financial CSV Agent with Langchain | Cohere | This page describes how to use Cohere's models to build an agent able to work with CSV data. | ||
| /page/csv-agent-native-api | Financial CSV Agent with Native Multi-Step Cohere API | Cohere | This page describes how to use Cohere's models and its native API to build an agent able to work with CSV data. | Financial CSV Agent with Native Multi-Step Cohere API | Cohere | This page describes how to use Cohere's models and its native API to build an agent able to work with CSV data. | ||
| /page/data-analyst-agent | A Data Analyst Agent Built with Cohere and Langchain | Cohere | This page describes how to build a data-analysis system out of Cohere's models. | A Data Analyst Agent Built with Cohere and Langchain | Cohere | This page describes how to build a data-analysis system out of Cohere's models. | ||
| /page/deploy-finetuned-model-aws-marketplace | Deploy your finetuned model on AWS Marketplace | Cohere | Learn how to deploy your finetuned model on AWS Marketplace. | Deploy your finetuned model on AWS Marketplace | Cohere | Learn how to deploy your finetuned model on AWS Marketplace. | ||
| /page/document-parsing-for-enterprises | Advanced Document Parsing For Enterprises | Cohere | This page describes how to use Cohere's models to build a document-parsing agent. | Advanced Document Parsing For Enterprises | Cohere | This page describes how to use Cohere's models to build a document-parsing agent. | ||
| /page/elasticsearch-and-cohere | End-to-end RAG using Elasticsearch and Cohere | Cohere | This page contains a basic tutorial on how to get Cohere and ElasticSearch to work well together. | End-to-end RAG using Elasticsearch and Cohere | Cohere | This page contains a basic tutorial on how to get Cohere and ElasticSearch to work well together. | ||
| /page/embed-jobs | Semantic Search with Cohere Embed Jobs | Cohere | This page contains a basic tutorial on how to use Cohere's Embed Jobs functionality. | Semantic Search with Cohere Embed Jobs | Cohere | This page contains a basic tutorial on how to use Cohere's Embed Jobs functionality. | ||
| /page/embed-jobs-serverless-pinecone | Serverless Semantic Search with Cohere and Pinecone | Cohere | This page contains a basic tutorial on how to get Cohere and the Pinecone vector database to work well together. | Serverless Semantic Search with Cohere and Pinecone | Cohere | This page contains a basic tutorial on how to get Cohere and the Pinecone vector database to work well together. | ||
| /page/finetune-on-sagemaker | Finetuning Cohere Models on AWS Sagemaker | Cohere | Learn how to finetune one of Cohere's models on AWS Sagemaker. | Finetuning Cohere Models on AWS Sagemaker | Cohere | Learn how to finetune one of Cohere's models on AWS Sagemaker. | ||
| /page/fueling-generative-content | Fueling Generative Content with Keyword Research | Cohere | This page contains a basic workflow for using Cohere's models to come up with keyword content ideas. | Fueling Generative Content with Keyword Research | Cohere | This page contains a basic workflow for using Cohere's models to come up with keyword content ideas. | ||
| /page/grounded-summarization | Grounded Summarization Using Command R | Cohere | This page contains a basic tutorial on how to do grounded summarization with Cohere's models. | Grounded Summarization Using Command R | Cohere | This page contains a basic tutorial on how to do grounded summarization with Cohere's models. | ||
| /page/hello-world-meet-ai | Hello World! Explore Language AI with Cohere | Cohere | This page contains a breakdown of some of what can be achieved with Cohere's LLM platform. | Hello World! Explore Language AI with Cohere | Cohere | This page contains a breakdown of some of what can be achieved with Cohere's LLM platform. | ||
| /page/long-form-general-strategies | Long-Form Text Strategies with Cohere | Cohere | This discusses ways of getting Cohere's LLM platform to perform well in generating long-form text. | Long-Form Text Strategies with Cohere | Cohere | This discusses ways of getting Cohere's LLM platform to perform well in generating long-form text. | ||
| /page/migrate-csv-agent | Migrating away from create_csv_agent in langchain-cohere | Cohere | This page contains a tutorial on how to build a CSV agent without the deprecated create_csv_agent abstraction in langchain-cohere v0.3.5 and beyond. | Migrating away from create_csv_agent in langchain-cohere | Cohere | This page contains a tutorial on how to build a CSV agent without the deprecated create_csv_agent abstraction in langchain-cohere v0.3.5 and beyond. | ||
| /page/migrating-prompts | Migrating Monolithic Prompts to Command A with RAG | Cohere | This page contains a discussion of how to automatically migrating monolothic prompts. | Migrating Monolithic Prompts to Command A with RAG | Cohere | This page contains a discussion of how to automatically migrating monolothic prompts. | ||
| /page/multilingual-search | Multilingual Search with Cohere and Langchain | Cohere | This page contains a basic tutorial on how to do search across different languages with Cohere's LLM platform. | Multilingual Search with Cohere and Langchain | Cohere | This page contains a basic tutorial on how to do search across different languages with Cohere's LLM platform. | ||
| /page/pdf-extractor | PDF Extractor with Native Multi Step Tool Use | Cohere | This page describes how to create an AI agent able to extract information from PDFs. | PDF Extractor with Native Multi Step Tool Use | Cohere | This page describes how to create an AI agent able to extract information from PDFs. | ||
| /page/pondr | Pondr, Fostering Connection through Good Conversation | Cohere | This page contains a basic tutorial on how tplay an AI-powered version of the icebreaking game 'Pondr'. | Pondr, Fostering Connection through Good Conversation | Cohere | This page contains a basic tutorial on how tplay an AI-powered version of the icebreaking game 'Pondr'. | ||
| /page/rag-cohere-mongodb | Build Chatbots with MongoDB and Cohere | Cohere | This page describes how to build a chatbot that provides actionable insights on technology company market reports. | Build Chatbots with MongoDB and Cohere | Cohere | This page describes how to build a chatbot that provides actionable insights on technology company market reports. | ||
| /page/rag-evaluation-deep-dive | Deep Dive Into Evaluating RAG Outputs | Cohere | This page contains information on evaluating the output of RAG systems. | Deep Dive Into Evaluating RAG Outputs | Cohere | This page contains information on evaluating the output of RAG systems. | ||
| /page/rag-with-chat-embed | RAG With Chat Embed and Rerank via Pinecone | Cohere | This page contains a basic tutorial on how to build a RAG-powered chatbot. | RAG With Chat Embed and Rerank via Pinecone | Cohere | This page contains a basic tutorial on how to build a RAG-powered chatbot. | ||
| /page/rerank-demo | Learn How Cohere's Rerank Models Work | Cohere | This page contains a basic tutorial on how Cohere's ReRank models work and how to use them. | Learn How Cohere's Rerank Models Work | Cohere | This page contains a basic tutorial on how Cohere's ReRank models work and how to use them. | ||
| /page/retrieval-eval-pydantic-ai | Retrieval evaluation using LLM-as-a-judge via Pydantic AI | Cohere | This page contains a tutorial on how to evaluate retrieval systems using LLMs as judges via Pydantic AI. | Retrieval evaluation using LLM-as-a-judge via Pydantic AI | Cohere | This page contains a tutorial on how to evaluate retrieval systems using LLMs as judges via Pydantic AI. | ||
| /page/sql-agent | Build a SQL Agent with Cohere's LLM Platform | Cohere | This page contains a tutorial on how to build a SQL agent with Cohere's LLM platform. | Build a SQL Agent with Cohere's LLM Platform | Cohere | This page contains a tutorial on how to build a SQL agent with Cohere's LLM platform. | ||
| /page/sql-agent-cohere-langchain | SQL Agent with Cohere and LangChain (i-5O Case Study) | Cohere | This page contains a tutorial on how to build a SQL agent with Cohere and LangChain in the manufacturing industry. | SQL Agent with Cohere and LangChain (i-5O Case Study) | Cohere | This page contains a tutorial on how to build a SQL agent with Cohere and LangChain in the manufacturing industry. | ||
| /page/summarization-evals | Evaluating Text Summarization Models | Cohere | This page discusses how to evaluate a model's text summarization. | Evaluating Text Summarization Models | Cohere | This page discusses how to evaluate a model's text summarization. | ||
| /page/text-classification-using-embeddings | Text Classification Using Embeddings | Cohere | This page discusses the creation of a text classification model using word vector embeddings. | Text Classification Using Embeddings | Cohere | This page discusses the creation of a text classification model using word vector embeddings. | ||
| /page/topic-modeling-ai-papers | Topic Modeling System for AI Papers | Cohere | This page discusses how to create a topic-modeling system for papers focused on AI papers. | Topic Modeling System for AI Papers | Cohere | This page discusses how to create a topic-modeling system for papers focused on AI papers. | ||
| /page/wikipedia-search-with-weaviate | Wikipedia Semantic Search with Cohere + Weaviate | Cohere | This page contains a description of building a Wikipedia-focused search engine with Cohere's LLM platform and the Weaviate vector database. | Wikipedia Semantic Search with Cohere + Weaviate | Cohere | This page contains a description of building a Wikipedia-focused search engine with Cohere's LLM platform and the Weaviate vector database. | ||
| /page/wikipedia-semantic-search | Wikipedia Semantic Search with Cohere Embedding Archives | Cohere | This page contains a description of building a Wikipedia-focused semantic search engine with Cohere's LLM platform and the Weaviate vector database. | Wikipedia Semantic Search with Cohere Embedding Archives | Cohere | This page contains a description of building a Wikipedia-focused semantic search engine with Cohere's LLM platform and the Weaviate vector database. | ||
| /reference/about | Working with Cohere's API and SDK | Cohere | Cohere's NLP platform provides customizable large language models and tools for developers to build AI applications. | Working with Cohere's API and SDK | Cohere | Cohere's NLP platform provides customizable large language models and tools for developers to build AI applications. | ||
| /reference/chat | Chat | Cohere | Generates a text response to a user message and streams it down, token by token. | Chat | Cohere | Generates a text response to a user message and streams it down, token by token. | ||
| /reference/chat-stream | Chat with Streaming | Cohere | Generates a text response to a user message. To learn how to use the Chat API and RAG follow our Text Generation guides(https://docs.cohere. | Chat with Streaming | Cohere | Generates a text response to a user message. To learn how to use the Chat API and RAG follow our Text Generation guides(https://docs.cohere. | ||
| /reference/classify | Classify | Cohere | This endpoint makes a prediction about which label fits the specified text inputs best. | Classify | Cohere | This endpoint makes a prediction about which label fits the specified text inputs best. | ||
| /reference/create-embed-job | Create an Embed Job | Cohere | This API launches an async Embed job for a Dataset(https://docs.cohere.com/docs/datasets) of type embed-input. | Create an Embed Job | Cohere | This API launches an async Embed job for a Dataset(https://docs.cohere.com/docs/datasets) of type embed-input. | ||
| /reference/embed | Embed API (v2) | Cohere | This endpoint returns text embeddings. An embedding is a list of floating point numbers that captures semantic information about the text that it represents. | Embed API (v2) | Cohere | This endpoint returns text embeddings. An embedding is a list of floating point numbers that captures semantic information about the text that it represents. | ||
| /reference/errors | Errors (status codes and description) | Cohere | Understand Cohere's HTTP response codes and how to handle errors in various programming languages. | Errors (status codes and description) | Cohere | Understand Cohere's HTTP response codes and how to handle errors in various programming languages. | ||
| /reference/list-connectors | List Connectors | Cohere | Returns a list of connectors ordered by descending creation date (newer first). See 'Managing your Connector'(https://docs.cohere. | List Connectors | Cohere | Returns a list of connectors ordered by descending creation date (newer first). See 'Managing your Connector'(https://docs.cohere. | ||
| /reference/list-models | List Models | Cohere | Returns a list of models available for use. | List Models | Cohere | Returns a list of models available for use. | ||
| /reference/listfinetunedmodels | Lists fine-tuned models. | Cohere | Returns a list of fine-tuned models that the user has access to. | Lists fine-tuned models. | Cohere | Returns a list of fine-tuned models that the user has access to. | ||
| /reference/rerank | Rerank API (v2) | Cohere | This endpoint takes in a query and a list of texts and produces an ordered array with each text assigned a relevance score. | Rerank API (v2) | Cohere | This endpoint takes in a query and a list of texts and produces an ordered array with each text assigned a relevance score. | ||
| /reference/teams-and-roles | Teams and Roles on the Cohere Platform | Cohere | The document outlines how to work in teams on the Cohere platform, including inviting others, managing roles, and access permissions for Owners and Users. | Teams and Roles on the Cohere Platform | Cohere | The document outlines how to work in teams on the Cohere platform, including inviting others, managing roles, and access permissions for Owners and Users. | ||
| /reference/tokenize | Tokenize | Cohere | This endpoint splits input text into smaller units called tokens using byte-pair encoding (BPE). | Tokenize | Cohere | This endpoint splits input text into smaller units called tokens using byte-pair encoding (BPE). | ||
| /v1/docs/advanced-prompt-engineering-techniques | Advanced Prompt Engineering Techniques | Cohere | This page describes advanced ways of controlling prompt engineering. | Advanced Prompt Engineering Techniques | Cohere | This page describes advanced ways of controlling prompt engineering. | ||
| /v1/docs/aya | Aya Family of Models | Cohere | Understand Cohere Labs groundbreaking multilingual Aya models, which aim to bring many more languages into generative AI. | Aya Family of Models | Cohere | Understand Cohere Labs groundbreaking multilingual Aya models, which aim to bring many more languages into generative AI. | ||
| /v1/docs/chat-improving-the-results | Improving the Chat Fine-tuning Results | Cohere | Learn how to refine data, iterate on hyperparameters, and troubleshoot to fine-tune your Chat model effectively. | Improving the Chat Fine-tuning Results | Cohere | Learn how to refine data, iterate on hyperparameters, and troubleshoot to fine-tune your Chat model effectively. | ||
| /v1/docs/cohere-embed | Cohere's Embed Models (Details and Application) | Cohere | Explore Embed models for text classification and embedding generation in English and multiple languages, with details on dimensions and endpoints. | Cohere's Embed Models (Details and Application) | Cohere | Explore Embed models for text classification and embedding generation in English and multiple languages, with details on dimensions and endpoints. | ||
| /v1/docs/cohere-works-everywhere | Cohere SDK Cloud Platform Compatibility | Cohere | This page describes various places you can use Cohere's SDK. | Cohere SDK Cloud Platform Compatibility | Cohere | This page describes various places you can use Cohere's SDK. | ||
| /v1/docs/command-a | Command A | Cohere | Command A is a performant mode good at tool use, RAG, agents, and multilingual use cases. It has 111 billion parameters and a 256k context length. | Command A | Cohere | Command A is a performant mode good at tool use, RAG, agents, and multilingual use cases. It has 111 billion parameters and a 256k context length. | ||
| /v1/docs/command-r7b | Cohere's Command R7B Model | Cohere | Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused large language models. It excels at RAG, tool use, and agents. | Cohere's Command R7B Model | Cohere | Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused large language models. It excels at RAG, tool use, and agents. | ||
| /v1/docs/fine-tuning | Introduction to Fine-Tuning with Cohere Models | Cohere | Fine-tune Cohere's large language models for specific tasks, styles, and formats with custom data. | Introduction to Fine-Tuning with Cohere Models | Cohere | Fine-tune Cohere's large language models for specific tasks, styles, and formats with custom data. | ||
| /v1/docs/introduction-to-text-generation-at-cohere | Introduction to Text Generation at Cohere | Cohere | This page describes how a large language model generates textual output. | Introduction to Text Generation at Cohere | Cohere | This page describes how a large language model generates textual output. | ||
| /v1/docs/models | An Overview of Cohere's Models | Cohere | Cohere has a variety of models that cover many different use cases. If you need more customization, you can train a model to tune it to your specific use case. | An Overview of Cohere's Models | Cohere | Cohere has a variety of models that cover many different use cases. If you need more customization, you can train a model to tune it to your specific use case. | ||
| /v1/docs/multi-step-tool-use | Multi-step Tool Use (Agents) | Cohere | "Cohere's tool use feature enhances AI capabilities by connecting external tools for dynamic, adaptable, and sequential actions." | Multi-step Tool Use (Agents) | Cohere | "Cohere's tool use feature enhances AI capabilities by connecting external tools for dynamic, adaptable, and sequential actions." | ||
| /v1/docs/overview-rag-connectors | An Overview of Cohere's RAG Connectors | Cohere | This page describes how to work with Cohere's retrieval-augmented generation connectors. | An Overview of Cohere's RAG Connectors | Cohere | This page describes how to work with Cohere's retrieval-augmented generation connectors. | ||
| /v1/docs/rate-limits | Different Types of API Keys and Rate Limits | Cohere | This page describes Cohere API rate limits for production and evaluation keys. | Different Types of API Keys and Rate Limits | Cohere | This page describes Cohere API rate limits for production and evaluation keys. | ||
| /v1/docs/rerank | Cohere's Rerank Model (Details and Application) | Cohere | This page describes how Cohere's Rerank models work and how to use them. | Cohere's Rerank Model (Details and Application) | Cohere | This page describes how Cohere's Rerank models work and how to use them. | ||
| /v1/docs/retrieval-augmented-generation-rag | Retrieval Augmented Generation (RAG) | Cohere | Generate text with external data and inline citations using Retrieval Augmented Generation and Cohere's Chat API. | Retrieval Augmented Generation (RAG) | Cohere | Generate text with external data and inline citations using Retrieval Augmented Generation and Cohere's Chat API. | ||
| /v1/docs/semantic-search-embed | Semantic Search with Embeddings | Cohere | Examples on how to use the Embed endpoint to perform semantic search (API v1). | Semantic Search with Embeddings | Cohere | Examples on how to use the Embed endpoint to perform semantic search (API v1). | ||
| /v1/docs/the-cohere-platform | An Overview of The Cohere Platform | Cohere | Cohere offers world-class Large Language Models (LLMs) like Command, Rerank, and Embed. These help developers and enterprises build LLM-powered applications. | An Overview of The Cohere Platform | Cohere | Cohere offers world-class Large Language Models (LLMs) like Command, Rerank, and Embed. These help developers and enterprises build LLM-powered applications. | ||
| /v1/page/cookbooks | Cookbooks | Cohere | Explore a range of AI guides and get started with Cohere's generative platform, ready-made and best-practice optimized. | Cookbooks | Cohere | Explore a range of AI guides and get started with Cohere's generative platform, ready-made and best-practice optimized. | ||
| /v2/docs/building-a-chatbot-with-cohere | Building a Chatbot with Cohere | Cohere | This page describes building a generative-AI powered chatbot with Cohere. | Building a Chatbot with Cohere | Cohere | This page describes building a generative-AI powered chatbot with Cohere. | ||
| /v2/docs/building-an-agent-with-cohere | Building a Generative AI Agent with Cohere | Cohere | This page describes building a generative-AI powered agent with Cohere. | Building a Generative AI Agent with Cohere | Cohere | This page describes building a generative-AI powered agent with Cohere. | ||
| /v2/docs/chat-api | Using the Cohere Chat API for Text Generation | Cohere | How to use the Chat API endpoint with Cohere LLMs to generate text responses in a conversational interface | Using the Cohere Chat API for Text Generation | Cohere | How to use the Chat API endpoint with Cohere LLMs to generate text responses in a conversational interface | ||
| /v2/docs/classify-starting-the-training | Train and deploy a fine-tuned model. | Cohere | Fine-tune classification models with Cohere's Web UI or Python SDK using custom datasets. (V2) | Train and deploy a fine-tuned model. | Cohere | Fine-tune classification models with Cohere's Web UI or Python SDK using custom datasets. (V2) | ||
| /v2/docs/command-r | Cohere's Command R Model | Cohere | Command R is a conversational model that excels in language tasks and supports multiple languages, making it ideal for coding use cases. | Cohere's Command R Model | Cohere | Command R is a conversational model that excels in language tasks and supports multiple languages, making it ideal for coding use cases. | ||
| /v2/docs/command-r-plus | Cohere's Command R+ Model | Cohere | Command R+ is Cohere's optimized for conversational interaction and long-context tasks, best suited for complex RAG workflows and multi-step tool use. | Cohere's Command R+ Model | Cohere | Command R+ is Cohere's optimized for conversational interaction and long-context tasks, best suited for complex RAG workflows and multi-step tool use. | ||
| /v2/docs/command-r7b | Cohere's Command R7B Model | Cohere | Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused large language models. It excels at RAG, tool use, and agents. | Cohere's Command R7B Model | Cohere | Command R7B is the smallest, fastest, and final model in our R family of enterprise-focused large language models. It excels at RAG, tool use, and agents. | ||
| /v2/docs/how-does-cohere-pricing-work | How Does Cohere's Pricing Work? | Cohere | This page details Cohere's pricing model. Our models can be accessed directly through our API, allowing for the creation of scalable production workloads. | How Does Cohere's Pricing Work? | Cohere | This page details Cohere's pricing model. Our models can be accessed directly through our API, allowing for the creation of scalable production workloads. | ||
| /v2/docs/migrating-v1-to-v2 | Migrating From API v1 to API v2 | Cohere | The document serves as a reference for developers looking to update their existing Cohere API v1 implementations to the new v2 standard. | Migrating From API v1 to API v2 | Cohere | The document serves as a reference for developers looking to update their existing Cohere API v1 implementations to the new v2 standard. | ||
| /v2/docs/models | An Overview of Cohere's Models | Cohere | Cohere has a variety of models that cover many different use cases. If you need more customization, you can train a model to tune it to your specific use case. | An Overview of Cohere's Models | Cohere | Cohere has a variety of models that cover many different use cases. If you need more customization, you can train a model to tune it to your specific use case. | ||
| /v2/docs/rag-with-cohere | Building RAG models with Cohere | Cohere | This page walks through building a retrieval-augmented generation model with Cohere. | Building RAG models with Cohere | Cohere | This page walks through building a retrieval-augmented generation model with Cohere. | ||
| /v2/docs/rate-limits | Different Types of API Keys and Rate Limits | Cohere | This page describes Cohere API rate limits for production and evaluation keys. | Different Types of API Keys and Rate Limits | Cohere | This page describes Cohere API rate limits for production and evaluation keys. | ||
| /v2/docs/reranking-with-cohere | Master Reranking with Cohere Models | Cohere | This page contains a tutorial on using Cohere's ReRank models. | Master Reranking with Cohere Models | Cohere | This page contains a tutorial on using Cohere's ReRank models. | ||
| /v2/docs/retrieval-augmented-generation-rag | Retrieval Augmented Generation (RAG) | Cohere | Guide on using Cohere's Retrieval Augmented Generation (RAG) capabilities such as document grounding and citations. | Retrieval Augmented Generation (RAG) | Cohere | Guide on using Cohere's Retrieval Augmented Generation (RAG) capabilities such as document grounding and citations. | ||
| /v2/docs/safety-modes | Safety Modes | Cohere | The safety modes documentation describes how to use default and strict modes in order to exercise additional control over model output. | Safety Modes | Cohere | The safety modes documentation describes how to use default and strict modes in order to exercise additional control over model output. | ||
| /v2/docs/semantic-search-with-cohere | Semantic Search with Cohere Models | Cohere | This is a tutorial describing how to leverage Cohere's models for semantic search. | Semantic Search with Cohere Models | Cohere | This is a tutorial describing how to leverage Cohere's models for semantic search. | ||
| /v2/docs/structured-outputs | How do Structured Outputs Work? | Cohere | This page describes how to get Cohere models to create outputs in a certain format, such as JSON, TOOLS, using parameters such as response_format. | How do Structured Outputs Work? | Cohere | This page describes how to get Cohere models to create outputs in a certain format, such as JSON, TOOLS, using parameters such as response_format. | ||
| /v2/docs/text-generation-tutorial | Cohere Text Generation Tutorial | Cohere | This page walks through how Cohere's generation models work and how to use them. | Cohere Text Generation Tutorial | Cohere | This page walks through how Cohere's generation models work and how to use them. | ||
| No rows found, please edit your search term. | ||||||
Heading structure
Found 188 row(s).
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| 13 | 8 | /page/agentic-rag-mixed-data |
| 7 | 7 | / |
| 23 | 7 | /docs/migrating-v1-to-v2 |
| 8 | 7 | /docs/embed-jobs-api |
| 23 | 7 | /v2/docs/migrating-v1-to-v2 |
| 11 | 7 | /page/csv-agent |
| 8 | 7 | /docs/chat-on-langchain |
| 7 | 6 | /reference/embed |
| 7 | 6 | /reference/classify |
| 7 | 6 | /docs/llamaindex |
| 7 | 6 | /docs/semantic-search |
| 7 | 6 | /reference/create-embed-job |
| 7 | 6 | /page/fueling-generative-content |
| 8 | 6 | /page/agent-short-term-memory |
| 6 | 6 | /page/migrate-csv-agent |
| 7 | 6 | /reference/tokenize |
| 6 | 5 | /reference/rerank |
| 6 | 5 | /reference/chat |
| 6 | 5 | /reference/chat-stream |
| 6 | 5 | /v1/docs/retrieval-augmented-generation-rag |
| 5 | 5 | /page/summarization-evals |
| 7 | 5 | /page/agent-api-calls |
| 6 | 5 | /reference/listfinetunedmodels |
| 6 | 5 | /docs/chat-preparing-the-data |
| 6 | 5 | /docs/classify-preparing-the-data |
| 6 | 5 | /reference/list-connectors |
| 5 | 4 | /docs/cohere-and-langchain |
| 5 | 4 | /docs/fine-tuning |
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| 8 | 4 | /page/hello-world-meet-ai |
| 6 | 4 | /page/sql-agent |
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| 5 | 4 | /docs/embed-on-langchain |
| 5 | 4 | /docs/rerank-preparing-the-data |
| 5 | 4 | /docs/rerank-understanding-the-results |
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| 4 | 3 | /docs/rerank-on-langchain |
| 10 | 3 | /v2/docs/semantic-search-with-cohere |
| 7 | 3 | /v2/docs/building-an-agent-with-cohere |
| 9 | 2 | /reference/errors |
| 10 | 2 | /docs/deprecations |
| 3 | 2 | /docs/foundation-models |
| 4 | 2 | /docs/multimodal-embeddings |
| 11 | 2 | /docs/datasets |
| 11 | 2 | /page/rag-evaluation-deep-dive |
| 9 | 2 | /page/analysis-of-financial-forms |
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| 4 | 2 | /docs/chat-understanding-the-results |
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404 URLs
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Redirected URLs
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Skipped URLs
Found 200 row(s).
External URLs
397 external URL(s) Found 200 row(s).
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Found 20 row(s).
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TOP slowest URLs
Found 20 row(s).
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Content types
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Source domains
| Domain | Totals | HTML | Redirect |
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| docs.cohere.com | 434 / 204MB / 73s | 426 / 204MB / 71s | 8 / 848B / 2.2s |
HTTP headers
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| Referrer-Policy | 196 | 1 | strict-origin-when-cross-origin | ||
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| No rows found, please edit your search term. | |||||
HTTP header values
Found 66 row(s).
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| Location | 1 | /docs/the-cohere-platform |
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| Referrer-Policy | 196 | strict-origin-when-cross-origin |
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| Vary | 196 | rsc, next-router-state-tree, next-router-prefetch, next-router-segment-prefetch |
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| No rows found, please edit your search term. | ||
HTTP Caching by content type (only from crawlable domains)
| Content type | Cache type | URLs 🔽 | AVG lifetime | MIN lifetime | MAX lifetime |
|---|---|---|---|---|---|
| HTML | Cache-Control | 238 | 0 s | 0 s | 0 s |
| HTML | Cache-Control + ETag | 188 | 0 s | 0 s | 0 s |
| Redirect | Cache-Control + ETag | 8 | 0 s | 0 s | 0 s |
HTTP Caching by domain
| Domain | Cache type | URLs 🔽 | AVG lifetime | MIN lifetime | MAX lifetime |
|---|---|---|---|---|---|
| docs.cohere.com | Cache-Control | 238 | 0 s | 0 s | 0 s |
| docs.cohere.com | Cache-Control + ETag | 196 | 0 s | 0 s | 0 s |
HTTP Caching by domain and content type
| Domain | Content type | Cache type | URLs 🔽 | AVG lifetime | MIN lifetime | MAX lifetime |
|---|---|---|---|---|---|---|
| docs.cohere.com | HTML | Cache-Control | 238 | 0 s | 0 s | 0 s |
| docs.cohere.com | HTML | Cache-Control + ETag | 188 | 0 s | 0 s | 0 s |
| docs.cohere.com | Redirect | Cache-Control + ETag | 8 | 0 s | 0 s | 0 s |
DNS info
| DNS resolving tree |
|---|
| docs.cohere.com |
| cname.vercel-dns.com |
| IPv4: cname.vercel-dns.com. |
| IPv4: 76.76.21.241 |
| IPv4: 66.33.60.193 |
| DNS server: 127.0.0.53 |
SSL/TLS info
| Info | Text |
|---|---|
| Issuer | C = US, O = Let's Encrypt, CN = R12 |
| Subject | CN = docs.cohere.com |
| Valid from | Feb 25 21:55:34 2026 GMT (VALID already 26.6 day(s)) |
| Valid to | May 26 21:55:33 2026 GMT (VALID still for 63.4 day(s)) |
| Supported protocols | TLSv1.2, TLSv1.3 |
| RAW certificate output | Certificate: Data: Version: 3 (0x2) Serial Number: 05:b2:ac:3d:a2:1b:25:af:5c:3c:4f:5c:1e:64:69:41:f5:d6 Signature Algorithm: sha256WithRSAEncryption Issuer: C = US, O = Let's Encrypt, CN = R12 Validity Not Before: Feb 25 21:55:34 2026 GMT Not After : May 26 21:55:33 2026 GMT Subject: CN = docs.cohere.com Subject Public Key Info: Public Key Algorithm: rsaEncryption Public-Key: (2048 bit) Modulus: 00:9a:da:1e:14:29:97:d2:f4:c8:d0:d4:1b:6e:11: 74:ab:d5:9e:bb:6c:8d:30:ee:c0:a1:89:e1:e9:5a: 9a:fd:de:c5:96:3a:dd:ea:da:31:fd:70:fb:cd:e1: e3:43:e7:0d:3f:b9:9d:ef:fb:ad:33:3e:27:7e:7b: 08:de:b2:7b:3c:76:67:df:36:6f:f7:bd:d8:a3:27: 6d:74:51:83:90:b5:37:a0:a6:58:95:31:cd:01:d1: 6b:be:c8:49:b4:af:09:1f:e2:d7:b6:29:97:5c:ed: 97:c1:e7:fc:08:53:07:f2:68:8e:b4:0f:f8:7e:c4: 36:04:b9:39:56:3c:bb:4a:d3:a7:6e:05:a6:92:4f: 74:0f:d4:8d:eb:08:f9:05:e1:e8:a2:17:47:73:23: b1:42:45:94:ad:88:dc:36:64:9a:c6:cd:cf:4b:8e: d9:67:9d:d2:30:b0:a3:8d:cd:c8:52:5e:39:27:bb: c5:52:45:7b:08:d8:4b:f3:0c:75:d0:54:be:50:57: 60:84:d5:1c:63:64:58:1c:61:c9:a7:e7:95:66:cf: 3a:7a:a1:be:cb:2d:b3:03:00:0c:e6:97:86:0a:2f: b5:ad:02:62:b5:ed:4d:da:36:ab:e2:63:bd:85:15: e2:3a:c3:41:77:78:6b:17:d1:0a:e3:79:d5:08:87: dd:b9 Exponent: 65537 (0x10001) X509v3 extensions: X509v3 Key Usage: critical Digital Signature, Key Encipherment X509v3 Extended Key Usage: TLS Web Server Authentication X509v3 Basic Constraints: critical CA:FALSE X509v3 Subject Key Identifier: 61:F7:19:79:25:68:2A:6A:13:E4:A1:41:63:7F:C2:A8:A2:62:D8:BE X509v3 Authority Key Identifier: 00:B5:29:F2:2D:8E:6F:31:E8:9B:4C:AD:78:3E:FA:DC:E9:0C:D1:D2 Authority Information Access: CA Issuers - URI:http://r12.i.lencr.org/ X509v3 Subject Alternative Name: DNS:docs.cohere.com X509v3 Certificate Policies: Policy: 2.23.140.1.2.1 X509v3 CRL Distribution Points: Full Name: URI:http://r12.c.lencr.org/87.crl CT Precertificate SCTs: Signed Certificate Timestamp: Version : v1 (0x0) Log ID : 16:83:2D:AB:F0:A9:25:0F:0F:F0:3A:A5:45:FF:C8:BF: C8:23:D0:87:4B:F6:04:29:27:F8:E7:1F:33:13:F5:FA Timestamp : Feb 25 22:54:04.231 2026 GMT Extensions: none Signature : ecdsa-with-SHA256 30:44:02:20:17:19:24:5A:A4:1D:54:DC:5B:9D:27:44: 88:79:8E:EF:A2:AC:63:F7:10:F5:22:85:47:EE:77:E6: 87:02:C4:7B:02:20:2F:27:BA:35:5C:1C:20:5B:DC:95: 3E:80:25:A0:46:5A:20:1B:89:60:79:9F:2B:4B:D0:EB: 81:7F:D4:08:60:3E Signed Certificate Timestamp: Version : v1 (0x0) Log ID : 0E:57:94:BC:F3:AE:A9:3E:33:1B:2C:99:07:B3:F7:90: DF:9B:C2:3D:71:32:25:DD:21:A9:25:AC:61:C5:4E:21 Timestamp : Feb 25 22:54:04.187 2026 GMT Extensions: none Signature : ecdsa-with-SHA256 30:45:02:21:00:8D:C6:A2:28:1D:7A:72:34:96:25:05: 7A:5B:3D:63:AC:28:B0:26:4D:D7:06:0C:46:DF:10:11: 4F:A1:E9:D1:82:02:20:41:99:60:AE:A6:39:96:9C:47: 2F:72:7E:D5:22:5F:C3:62:6B:4D:D6:34:E8:F9:C2:89: BF:59:6E:E7:39:A7:20 Signature Algorithm: sha256WithRSAEncryption Signature Value: 05:24:0d:c2:9c:ff:7c:4d:6e:f6:e8:ac:89:c6:32:e1:0a:e5: 1b:73:ba:a8:2e:c2:f9:d7:b6:e6:0e:ed:40:06:b0:87:ca:fb: d1:a5:19:88:ed:57:d4:28:65:c4:1f:e9:bb:44:22:37:95:11: e2:28:45:24:56:4d:fa:a3:92:ed:3c:f5:d2:5d:1b:5c:7b:e3: 22:6a:4d:c2:29:b2:97:fa:03:80:5e:b5:68:11:c6:44:5f:ba: 02:4c:14:09:12:5e:56:f8:79:1d:fa:29:35:e3:a0:12:9f:a1: 5e:bb:72:72:ef:ab:e7:73:bf:4b:60:6d:51:7b:3b:7a:53:a9: 43:66:09:5a:86:8b:92:26:42:aa:27:f6:ac:4e:22:02:59:a2: bd:70:21:85:da:14:3a:6f:1f:19:69:e7:4e:17:d7:24:7c:41: bf:79:c2:0b:d2:dc:7c:cd:3a:16:6b:f7:48:b5:f6:63:9c:10: 0e:c6:8e:6f:10:bd:38:f4:fa:21:aa:4b:25:54:7d:80:2d:d9: 70:89:84:fa:ae:7d:d4:26:ef:be:8f:60:7e:bd:35:9d:97:79: 32:33:7b:76:cd:69:81:5d:c8:9c:8c:8e:21:0f:c5:20:67:7c: 2f:d8:70:8c:a8:20:d8:1a:a5:3e:ab:90:f4:33:b5:54:10:59: 8e:0a:c0:8f |
| RAW protocols output | === ssl2 === s_client: Unknown option: -ssl2 s_client: Use -help for summary. === ssl3 === s_client: Unknown option: -ssl3 s_client: Use -help for summary. === tls1 === 40F720EE3F780000:error:0A0000BF:SSL routines:tls_setup_handshake:no protocols available:../ssl/statem/statem_lib.c:104: CONNECTED(00000003) --- no peer certificate available --- No client certificate CA names sent --- SSL handshake has read 0 bytes and written 7 bytes Verification: OK --- New, (NONE), Cipher is (NONE) Secure Renegotiation IS NOT supported Compression: NONE Expansion: NONE No ALPN negotiated Early data was not sent Verify return code: 0 (ok) --- === tls1_1 === 40C75F6754790000:error:0A0000BF:SSL routines:tls_setup_handshake:no protocols available:../ssl/statem/statem_lib.c:104: CONNECTED(00000003) --- no peer certificate available --- No client certificate CA names sent --- SSL handshake has read 0 bytes and written 7 bytes Verification: OK --- New, (NONE), Cipher is (NONE) Secure Renegotiation IS NOT supported Compression: NONE Expansion: NONE No ALPN negotiated Early data was not sent Verify return code: 0 (ok) --- === tls1_2 === depth=2 C = US, O = Internet Security Research Group, CN = ISRG Root X1 verify return:1 depth=1 C = US, O = Let's Encrypt, CN = R12 verify return:1 depth=0 CN = docs.cohere.com verify return:1 CONNECTED(00000003) --- Certificate chain 0 s:CN = docs.cohere.com i:C = US, O = Let's Encrypt, CN = R12 a:PKEY: rsaEncryption, 2048 (bit); sigalg: RSA-SHA256 v:NotBefore: Feb 25 21:55:34 2026 GMT; NotAfter: May 26 21:55:33 2026 GMT 1 s:C = US, O = Let's Encrypt, CN = R12 i:C = US, O = Internet Security Research Group, CN = ISRG Root X1 a:PKEY: rsaEncryption, 2048 (bit); sigalg: RSA-SHA256 v:NotBefore: Mar 13 00:00:00 2024 GMT; NotAfter: Mar 12 23:59:59 2027 GMT --- Server certificate -----BEGIN CERTIFICATE----- MIIE7zCCA9egAwIBAgISBbKsPaIbJa9cPE9cHmRpQfXWMA0GCSqGSIb3DQEBCwUA MDMxCzAJBgNVBAYTAlVTMRYwFAYDVQQKEw1MZXQncyBFbmNyeXB0MQwwCgYDVQQD EwNSMTIwHhcNMjYwMjI1MjE1NTM0WhcNMjYwNTI2MjE1NTMzWjAaMRgwFgYDVQQD Ew9kb2NzLmNvaGVyZS5jb20wggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIB AQCa2h4UKZfS9MjQ1BtuEXSr1Z67bI0w7sChieHpWpr93sWWOt3q2jH9cPvN4eND 5w0/uZ3v+60zPid+ewjesns8dmffNm/3vdijJ210UYOQtTegpliVMc0B0Wu+yEm0 rwkf4te2KZdc7ZfB5/wIUwfyaI60D/h+xDYEuTlWPLtK06duBaaST3QP1I3rCPkF 4eiiF0dzI7FCRZStiNw2ZJrGzc9LjtlnndIwsKONzchSXjknu8VSRXsI2EvzDHXQ VL5QV2CE1RxjZFgcYcmn55Vmzzp6ob7LLbMDAAzml4YKL7WtAmK17U3aNqviY72F FeI6w0F3eGsX0QrjedUIh925AgMBAAGjggIUMIICEDAOBgNVHQ8BAf8EBAMCBaAw EwYDVR0lBAwwCgYIKwYBBQUHAwEwDAYDVR0TAQH/BAIwADAdBgNVHQ4EFgQUYfcZ eSVoKmoT5KFBY3/CqKJi2L4wHwYDVR0jBBgwFoAUALUp8i2ObzHom0yteD763OkM 0dIwMwYIKwYBBQUHAQEEJzAlMCMGCCsGAQUFBzAChhdodHRwOi8vcjEyLmkubGVu Y3Iub3JnLzAaBgNVHREEEzARgg9kb2NzLmNvaGVyZS5jb20wEwYDVR0gBAwwCjAI BgZngQwBAgEwLgYDVR0fBCcwJTAjoCGgH4YdaHR0cDovL3IxMi5jLmxlbmNyLm9y Zy84Ny5jcmwwggEDBgorBgEEAdZ5AgQCBIH0BIHxAO8AdQAWgy2r8KklDw/wOqVF /8i/yCPQh0v2BCkn+OcfMxP1+gAAAZyXAmvHAAAEAwBGMEQCIBcZJFqkHVTcW50n RIh5ju+irGP3EPUihUfud+aHAsR7AiAvJ7o1XBwgW9yVPoAloEZaIBuJYHmfK0vQ 64F/1AhgPgB2AA5XlLzzrqk+MxssmQez95Dfm8I9cTIl3SGpJaxhxU4hAAABnJcC a5sAAAQDAEcwRQIhAI3GoigdenI0liUFels9Y6wosCZN1wYMRt8QEU+h6dGCAiBB mWCupjmWnEcvcn7VIl/DYmtN1jTo+cKJv1lu5zmnIDANBgkqhkiG9w0BAQsFAAOC AQEABSQNwpz/fE1u9uisicYy4QrlG3O6qC7C+de25g7tQAawh8r70aUZiO1X1Chl xB/pu0QiN5UR4ihFJFZN+qOS7Tz10l0bXHvjImpNwimyl/oDgF61aBHGRF+6AkwU CRJeVvh5HfopNeOgEp+hXrtycu+r53O/S2BtUXs7elOpQ2YJWoaLkiZCqif2rE4i AlmivXAhhdoUOm8fGWnnThfXJHxBv3nCC9LcfM06Fmv3SLX2Y5wQDsaObxC9OPT6 IapLJVR9gC3ZcImE+q591Cbvvo9gfr01nZd5MjN7ds1pgV3InIyOIQ/FIGd8L9hw jKgg2BqlPquQ9DO1VBBZjgrAjw== -----END CERTIFICATE----- subject=CN = docs.cohere.com issuer=C = US, O = Let's Encrypt, CN = R12 --- No client certificate CA names sent Peer signing digest: SHA256 Peer signature type: RSA-PSS Server Temp Key: X25519, 253 bits --- SSL handshake has read 3150 bytes and written 305 bytes Verification: OK --- New, TLSv1.2, Cipher is ECDHE-RSA-AES128-GCM-SHA256 Server public key is 2048 bit Secure Renegotiation IS supported Compression: NONE Expansion: NONE No ALPN negotiated SSL-Session: Protocol : TLSv1.2 Cipher : ECDHE-RSA-AES128-GCM-SHA256 Session-ID: 603822ED932A11EE2B6036FD3543461C707F517DD03585143D16813A42347288 Session-ID-ctx: Master-Key: 9D25F208922798EB117E73F7CB251B3AC9AA943567B3505896413CF961545D5E58556B6D1A5E1AD903CE19F09D20F2FF PSK identity: None PSK identity hint: None SRP username: None TLS session ticket: 0000 - 2c 04 d0 0a 50 8d be 51-80 56 91 c4 50 8b 5b 82 ,...P..Q.V..P.[. 0010 - af 9b 20 b6 ba a1 07 2b-8c f5 3a 8e ea 90 12 ec .. ....+..:..... 0020 - dd 42 46 9a 62 e2 aa 3d-5c 89 5a 5f a2 cf 61 90 .BF.b..=\.Z_..a. 0030 - 06 4a 9c 39 93 08 dc 15-3f 12 83 9b 40 82 dd 62 .J.9....?...@..b 0040 - 70 e6 bb eb 1a e0 cf a5-1c 06 ad af 7b ae 9d fa p...........{... 0050 - df 41 a1 98 13 0b 78 bd-36 67 08 da f2 fe ed 44 .A....x.6g.....D 0060 - 31 b0 ae 7f 82 67 cd be-bd 19 84 25 a8 4d 58 98 1....g.....%.MX. 0070 - dd 8e 7f 99 0a c7 0a bc-86 dd 84 ........... Start Time: 1774353420 Timeout : 7200 (sec) Verify return code: 0 (ok) Extended master secret: yes --- DONE === tls1_3 === depth=2 C = US, O = Internet Security Research Group, CN = ISRG Root X1 verify return:1 depth=1 C = US, O = Let's Encrypt, CN = R12 verify return:1 depth=0 CN = docs.cohere.com verify return:1 CONNECTED(00000003) --- Certificate chain 0 s:CN = docs.cohere.com i:C = US, O = Let's Encrypt, CN = R12 a:PKEY: rsaEncryption, 2048 (bit); sigalg: RSA-SHA256 v:NotBefore: Feb 25 21:55:34 2026 GMT; NotAfter: May 26 21:55:33 2026 GMT 1 s:C = US, O = Let's Encrypt, CN = R12 i:C = US, O = Internet Security Research Group, CN = ISRG Root X1 a:PKEY: rsaEncryption, 2048 (bit); sigalg: RSA-SHA256 v:NotBefore: Mar 13 00:00:00 2024 GMT; NotAfter: Mar 12 23:59:59 2027 GMT --- Server certificate -----BEGIN CERTIFICATE----- MIIE7zCCA9egAwIBAgISBbKsPaIbJa9cPE9cHmRpQfXWMA0GCSqGSIb3DQEBCwUA MDMxCzAJBgNVBAYTAlVTMRYwFAYDVQQKEw1MZXQncyBFbmNyeXB0MQwwCgYDVQQD EwNSMTIwHhcNMjYwMjI1MjE1NTM0WhcNMjYwNTI2MjE1NTMzWjAaMRgwFgYDVQQD Ew9kb2NzLmNvaGVyZS5jb20wggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIB AQCa2h4UKZfS9MjQ1BtuEXSr1Z67bI0w7sChieHpWpr93sWWOt3q2jH9cPvN4eND 5w0/uZ3v+60zPid+ewjesns8dmffNm/3vdijJ210UYOQtTegpliVMc0B0Wu+yEm0 rwkf4te2KZdc7ZfB5/wIUwfyaI60D/h+xDYEuTlWPLtK06duBaaST3QP1I3rCPkF 4eiiF0dzI7FCRZStiNw2ZJrGzc9LjtlnndIwsKONzchSXjknu8VSRXsI2EvzDHXQ VL5QV2CE1RxjZFgcYcmn55Vmzzp6ob7LLbMDAAzml4YKL7WtAmK17U3aNqviY72F FeI6w0F3eGsX0QrjedUIh925AgMBAAGjggIUMIICEDAOBgNVHQ8BAf8EBAMCBaAw EwYDVR0lBAwwCgYIKwYBBQUHAwEwDAYDVR0TAQH/BAIwADAdBgNVHQ4EFgQUYfcZ eSVoKmoT5KFBY3/CqKJi2L4wHwYDVR0jBBgwFoAUALUp8i2ObzHom0yteD763OkM 0dIwMwYIKwYBBQUHAQEEJzAlMCMGCCsGAQUFBzAChhdodHRwOi8vcjEyLmkubGVu Y3Iub3JnLzAaBgNVHREEEzARgg9kb2NzLmNvaGVyZS5jb20wEwYDVR0gBAwwCjAI BgZngQwBAgEwLgYDVR0fBCcwJTAjoCGgH4YdaHR0cDovL3IxMi5jLmxlbmNyLm9y Zy84Ny5jcmwwggEDBgorBgEEAdZ5AgQCBIH0BIHxAO8AdQAWgy2r8KklDw/wOqVF /8i/yCPQh0v2BCkn+OcfMxP1+gAAAZyXAmvHAAAEAwBGMEQCIBcZJFqkHVTcW50n RIh5ju+irGP3EPUihUfud+aHAsR7AiAvJ7o1XBwgW9yVPoAloEZaIBuJYHmfK0vQ 64F/1AhgPgB2AA5XlLzzrqk+MxssmQez95Dfm8I9cTIl3SGpJaxhxU4hAAABnJcC a5sAAAQDAEcwRQIhAI3GoigdenI0liUFels9Y6wosCZN1wYMRt8QEU+h6dGCAiBB mWCupjmWnEcvcn7VIl/DYmtN1jTo+cKJv1lu5zmnIDANBgkqhkiG9w0BAQsFAAOC AQEABSQNwpz/fE1u9uisicYy4QrlG3O6qC7C+de25g7tQAawh8r70aUZiO1X1Chl xB/pu0QiN5UR4ihFJFZN+qOS7Tz10l0bXHvjImpNwimyl/oDgF61aBHGRF+6AkwU CRJeVvh5HfopNeOgEp+hXrtycu+r53O/S2BtUXs7elOpQ2YJWoaLkiZCqif2rE4i AlmivXAhhdoUOm8fGWnnThfXJHxBv3nCC9LcfM06Fmv3SLX2Y5wQDsaObxC9OPT6 IapLJVR9gC3ZcImE+q591Cbvvo9gfr01nZd5MjN7ds1pgV3InIyOIQ/FIGd8L9hw jKgg2BqlPquQ9DO1VBBZjgrAjw== -----END CERTIFICATE----- subject=CN = docs.cohere.com issuer=C = US, O = Let's Encrypt, CN = R12 --- No client certificate CA names sent Peer signing digest: SHA256 Peer signature type: RSA-PSS Server Temp Key: X25519, 253 bits --- SSL handshake has read 3106 bytes and written 313 bytes Verification: OK --- New, TLSv1.3, Cipher is TLS_AES_128_GCM_SHA256 Server public key is 2048 bit Secure Renegotiation IS NOT supported Compression: NONE Expansion: NONE No ALPN negotiated Early data was not sent Verify return code: 0 (ok) --- DONE --- Post-Handshake New Session Ticket arrived: SSL-Session: Protocol : TLSv1.3 Cipher : TLS_AES_128_GCM_SHA256 Session-ID: EE2BCD4B17CAE2DCC4AAF953E4555489BBAC35B16677BBC96C795DA789154700 Session-ID-ctx: Resumption PSK: 9BB36FC5D66B6F803A4AC4EACE76EC958CEB5B775ACB4D6CAA3472A1C9AC02AA PSK identity: None PSK identity hint: None SRP username: None TLS session ticket lifetime hint: 604800 (seconds) TLS session ticket: 0000 - 6b 57 fc e7 96 c1 44 5d-c5 a4 48 4f 31 4b 64 d3 kW....D]..HO1Kd. 0010 - 43 a6 bc 56 1f 52 16 f9-a2 54 e6 e1 9f 08 6d 28 C..V.R...T....m( 0020 - 5a 54 8e 99 b8 e3 52 f7-25 07 18 52 6c 5c 6e b4 ZT....R.%..Rl\n. 0030 - 39 35 b3 fe d1 06 2c 68-0a f6 83 26 bf 1d 9d cc 95....,h...&.... 0040 - 33 61 31 24 95 cd 00 41-d9 d6 a2 8c 36 f0 d9 25 3a1$...A....6..% 0050 - 29 91 38 72 be 14 8d 2f-ef c5 b2 7f b5 40 28 dc ).8r.../.....@(. 0060 - a1 ca 71 17 22 ff 2e f8-ce ..q.".... Start Time: 1774353420 Timeout : 7200 (sec) Verify return code: 0 (ok) Extended master secret: no Max Early Data: 0 --- read R BLOCK |
Crawler stats
| Basic stats | |
|---|---|
| Total execution time | 47 s |
| Total URLs | 434 |
| Total size | 204 MB |
| Requests - total time | 73 s |
| Requests - avg time | 169 ms |
| Requests - min time | 10 ms |
| Requests - max time | 784 ms |
| Requests by status | 200: 188 307: 1 308: 7 403: 238 |
Analysis stats
Found 21 row(s).
| Class::method | Exec time 🔽 | Exec count |
|---|---|---|
| BestPracticeAnalyzer::checkHeadingStructure | 1.8 s | 426 |
| BestPracticeAnalyzer::checkNonClickablePhoneNumbers | 1.8 s | 426 |
| AccessibilityAnalyzer::checkMissingLabels | 1.2 s | 188 |
| AccessibilityAnalyzer::checkMissingAriaLabels | 1.1 s | 188 |
| AccessibilityAnalyzer::checkMissingRoles | 853 ms | 188 |
| BestPracticeAnalyzer::checkMaxDOMDepth | 801 ms | 426 |
| AccessibilityAnalyzer::checkMissingLang | 729 ms | 188 |
| SslTlsAnalyzer::getTLSandSSLCertificateInfo | 399 ms | 1 |
| BestPracticeAnalyzer::checkInlineSvg | 190 ms | 426 |
| BestPracticeAnalyzer::checkMissingQuotesOnAttributes | 60 ms | 426 |
| AccessibilityAnalyzer::checkImageAltAttributes | 31 ms | 188 |
| SecurityAnalyzer::checkHtmlSecurity | 26 ms | 426 |
| SeoAndOpenGraphAnalyzer::analyzeHeadings | 24 ms | 1 |
| SecurityAnalyzer::checkHeaders | 10 ms | 426 |
| SeoAndOpenGraphAnalyzer::analyzeSeo | 0 ms | 1 |
| SeoAndOpenGraphAnalyzer::analyzeOpenGraph | 0 ms | 1 |
| BestPracticeAnalyzer::checkMetaDescriptionUniqueness | 0 ms | 1 |
| BestPracticeAnalyzer::checkTitleUniqueness | 0 ms | 1 |
| BestPracticeAnalyzer::checkBrotliSupport | 0 ms | 1 |
| BestPracticeAnalyzer::checkAvifSupport | 0 ms | 1 |
| BestPracticeAnalyzer::checkWebpSupport | 0 ms | 1 |
| No rows found, please edit your search term. | ||
Content processor stats
Found 12 row(s).
| Class::method | Exec time 🔽 | Exec count |
|---|---|---|
| NextJsProcessor::applyContentChangesBeforeUrlParsing | 1.2 s | 426 |
| JavaScriptProcessor::findUrls | 1 s | 426 |
| HtmlProcessor::findUrls | 423 ms | 434 |
| CssProcessor::findUrls | 51 ms | 426 |
| AstroProcessor::findUrls | 21 ms | 426 |
| AstroProcessor::applyContentChangesBeforeUrlParsing | 0 ms | 426 |
| NextJsProcessor::findUrls | 0 ms | 426 |
| JavaScriptProcessor::applyContentChangesBeforeUrlParsing | 0 ms | 426 |
| HtmlProcessor::applyContentChangesBeforeUrlParsing | 0 ms | 434 |
| SvelteProcessor::applyContentChangesBeforeUrlParsing | 0 ms | 426 |
| SvelteProcessor::findUrls | 0 ms | 426 |
| CssProcessor::applyContentChangesBeforeUrlParsing | 0 ms | 426 |
| No rows found, please edit your search term. | ||
Crawler info
| Version | 2.1.0.20260317 |
|---|---|
| Executed At | 2026-03-24 11:56:14 |
| Command | siteone-crawler --url=https://docs.cohere.com --markdown-export-dir=/tmp/siteone-cohere --markdown-exclude-selector=header,footer,nav,.sidebar,.menu,.breadcrumb,script,style --ignore-regex=/changelog/ --timeout=30 --workers=5 --disable-javascript --disable-styles --disable-fonts --disable-images --disable-files --no-color --hide-progress-bar --output=text |
| Hostname | ubuntu-8gb-hel1-1 |
| User-Agent | Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/26.0.0.0 Safari/537.36 siteone-crawler/2.1.0.20260317 |