Nhazi okwunchọgharị
Ọrụ ọhaneze OK
380+ Models
Enweghị akara mmiri
Enweghị mkpa ịbanye
Móòdù:
+ GPT-5, Claude, Gemini
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Akaụntụ
Na-arụ ọrụ n'ihe ịchọrọ...
Wepụta okwu ndị dị mkpa site na ngwe ọ bụla na AI n'efu. SEO na nlekọta ọdịnaya dị mfe.
Etu esi eji ya Nhazi okwunchọgharị
1
Tinye init gị
Tinye ngwe, bubata faịlụ, mọọbụ depụta ihe ịchọrọ. Achọrọ akaụntụ ọbụla.
2
Pịa mepụta
Anyị AI na-arụ ọrụ gị n'ime sekọnd na-eji ihe kacha mma open-source models.
3
Bubata na akwado
Bubata, debata, mọọbụ kesaa nsonaazụ gị. Free maka ojiji nkeonwe na nke azụmahịa.
Jiri tùlè a site na API
Megharịa ihenhọrọ a site na koodị gị. OpenAI-compatible REST endpoint, Bearer-token auth, enweghị SDK ọzọ achọrọ. Token costs match the web interface.
curl -X POST https://api.free.ai/v1/chat/ \
-H "Authorization: Bearer sk-free-..." \
-H "Content-Type: application/json" \
-d '{"model": "qwen7b", "messages": [{"role": "user", "content": "Summarize this: ..."}]}'Ngwaọrụ AI n'efu dị na-emetụtara
Nhazi okwunchọgharị — FAQ
Na-ewepụ okwu ndị dị mkpa na okwu ndị ọzọ site n'ọdịnihu ọ bụla (akwụkwọ, blọgụ, transcript, nkọwa ngwaahịa). Móòdù atọ: (1) Keywords SEO na-enyocha-n'ihi tagging, (2) isiokwu dị elu + isiokwu, (3) aha ndị ahụ - ndị mmadụ/ebe/ọrụ, (4) tag-style lowercase-hyphen-joined tags, (5) academic index terms. Results scored 0-100 by TF-IDF, raw frequency, or semantic relevance. Export as CSV, TXT, or JSON.
Ya - a 1,500-okwu isiokwu extracts na ~350 tokens na diff Qwen 3 30B model, comfortably n'ime 2,500 anonymous ma ọ bụ 10,000 signed-up kwa ụbọchị pool. No signed-up chọrọ maka gị mbụ extracting.
Ndị a na-eji ihe ndị a na-eme ihe na-eme ka ọ bụrụ na ị na-enye ha ($ 99 + / mo). Ngwaọrụ a na-eme ihe na-adịghị mma - ọ na-ewepụ okwu ndị na-achọ okwu site na ọdịnaya gị ma na-enyocha nghọta ha. Workflow: jiri nke a chọpụta ihe ndị a na-ekwu okwu na isiokwu gị, wee tinye ha na Ahrefs / Semrush iji hụ na ọnụọgụ ọchụchọ kwa ọnwa.
Motọ atọ nke ịkụ nzọ: (1) TF-IDF (difference) na-enye okwu abụọ na-emekarị na akwụkwọ a na-emekarị na English - kacha mma maka SEO ebe ha bụ okwu dị iche iche. (2) Frequency nke raw na-enyocha ihe na-eme n'ụzọ ziri ezi - kacha mma mgbe ịchọrọ ịhụ ihe ị na-ekwu. (3) Semantic relevance scores by proximity to the documents main thesis even if a phrase appears only once - best for topical coherence.
N-gram bụ ntọala nke okwu N. "ụdị ihu igwe" bụ 2-gram. "ọdịna-ike mmụọ nsọ" bụ 3-gram. Tọghataa chip ndị ahụ ka ha wepụta naanị 1-grams (asụsụ dị iche iche dị ka "photosynthesis"), 2-grams (ihe a na-ekwu dị ka "carbon footprint"), ma ọ bụ 3-grams (ọnụ-ọnụ dị ka "carbon footprint calculator"). Ọtụtụ SEO ọrụ na-achọ 2+3-grams - okwu dị iche iche bụ nke dịkarịa ala.
Ya - ndesịta mkpocha difọ́ọ̀ltụ̀ na-ewepụ "the", "na", "n'ime", "is", wdg. n'asụsụ 14. I nwere ike ịgbakwunye okwu mkpocha emeredịkachọrọ site na "Wọrds to exclude" field - bara uru maka ịkwụsị aha brand gị, koodu ngwaahịa, mọọbụ legalese boilerplate nke ga-eme ka nsonaazụ ahụ dị n'ụzọ ọzọ.
Google na-enyocha ajụjụ site na onye ọrụ: ozi ("gịnị bụ X"), nlegharị anya ("ọfịs ụlọ ahịa Nike"), azụmahịa ("n'ezie na-agba ọsọ shoes 2026"), transactional ("buy air max online"). Ngwaọrụ ahụ na-egosipụta okwu gị na-ewepụ ya na ihe ị ga-eme ka ị mara ma isiokwu gị ga-egosipụta maka ndị ọkà mmụta sayensị, ndị na-azụ ahịa, ma ọ bụ n'ụzọ ziri ezi-ntụgharị. N'ihi na ị ga-egosipụta ọdịnaya na SERP.
99 asụsụ site na Qwen 2.5 model. Quality kacha elu na English, Spanish, French, Portuguese, German, Chinese, Japanese. Lower-ressource asụsụ ọrụ ma 3-gram extraction nwere ike ịbụ na-adịghị ike - kwụsịghachi na 1 + 2-grams maka Arabic, Hindi, Turkish, wdg
UI na-agbagharị ngwe otu n'otu oge. Maka n'ịkpụpụta n'ụdị n'ụdị /v1/chat/ API n'ime isiokwu Python - na-agbagharị n'elu CMS gị, POST akụkụ isiokwu ọ bụla, gbanye JSON. Ogo onye na-eweta, 1,000 oku / awa na akaụntụ ọbụla, obere nkwụsị na pro. Docs na /api/.
RAKE na YAKE bụ algorithms lexical classical - ngwa ngwa ma ọ bụ na-adịghị ike na semantic nuance (ha na-ahapụ "tech startup" ma ọ bụrụ na akwụkwọ ahụ na-ekwu "technology startup" mana ọ bụghị "tech" naanị). KeyBERT na-eji BERT embeddings - mma semantic quality ma dị njọ ma ọ chọrọ nrụnye. TextRank bụ graph-based. Our approach uses a 7B-parameter LLM nke na-eme ihe niile nke elu-shot plus search-intent classification - slower per call but zero setup, free, and no ML engineering required.
Na fréèksị na TF-IDF móòdù, ọ̀bụla - okwunchọgharị ọbụla bụ nke a ga-apụta site na ngwe isi ma ọ bụ na mgbanwe ntụgharị nke nta. Na semàntik-n'ihe dị mkpa módù ahụ, móòdù ahụ nwere ike ịnabata ụdị okwu-nsụgharị nke a na-ahụ maka ya ("pulmonology" ọbụla ma ọ bụrụ na ngwe gị kwuru "pulmon doctor visit"). Módù amụba-okwu na-achọkwa okwu-nsụgharị nke a na-ahụ maka ya.
Ya - POST ka /v1/chat/ na sistem n'otu n'otu na-akpụpụta ihuakwụkwọ a (lee n'isi mmalite nke téèbụ̀ maka n'eziokwu). Na-eziga JSON nke e mepụtara. Ọ dị mma maka ngwe-nhazi dashboards mọọbụ CMS plugins. Nnweta ikike, n'ime ọnwa. Doktọ na /api/.
Olee otú ị ga-esi họrọ ihenhọrọ a?