{"id":17132,"date":"2025-01-29T14:01:30","date_gmt":"2025-01-29T12:01:30","guid":{"rendered":"https:\/\/spatialworld.fi\/?page_id=17132"},"modified":"2025-02-05T17:35:10","modified_gmt":"2025-02-05T15:35:10","slug":"17076-2-2-3","status":"publish","type":"page","link":"https:\/\/spatialworld.fi\/fi\/17076-2-2-3\/","title":{"rendered":"LLM RAG"},"content":{"rendered":"[vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text css=”” text_direction=”default”]\n

Kuinka kysy\u00e4 paikalliselta LLM:lt\u00e4 kysymys k\u00e4ytt\u00e4m\u00e4ll\u00e4 omaa asiakirjakontekstiasi<\/span><\/span><\/h1>\n[\/vc_column_text][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][image_with_animation image_url=”17216″ image_size=”medium_large” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”75%” max_width_mobile=”default”][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][vc_column_text css=”” text_direction=”default”]\n

RAG:n (Retrieval-Augmented Generation) tutkiminen FME:n, DuckDB:n ja Ollaman kanssa<\/span><\/h3>\n

RAG eli Retrieval-Augmented Generation kattaa AI-mallien ja ainutlaatuisten tietojesi v\u00e4lisen kuilun. Haemalla asiaankuuluvat tiedot asiakirjoistasi ja sy\u00f6tt\u00e4m\u00e4ll\u00e4 ne teko\u00e4lymalliin, voit luoda tarkempia, kontekstikohtaisia \u200b\u200bvastauksia. T\u00e4m\u00e4 opetusohjelma osoittaa, kuinka FME:n avulla luodaan matalakoodin RAG-ty\u00f6nkulku, jonka avulla voit kysy\u00e4 paikalliselta LLM:lt\u00e4 (kuten Ollamalta) kysymyksi\u00e4 k\u00e4ytt\u00e4m\u00e4ll\u00e4 asiakirjaasi kontekstina.<\/span><\/p>\n

Ohjaamme sinut n\u00e4iden vaiheiden l\u00e4pi:<\/span><\/p>\n

    \n
  1. Tekstin poimiminen asiakirjasta.<\/span><\/li>\n
  2. Tekstin valmistelu.<\/span><\/li>\n
  3. Tekstin esitt\u00e4minen vektoriavaruudessa.<\/span><\/li>\n
  4. Sen tallentaminen ja analysointi tietokantaan.<\/span><\/li>\n
  5. Haetaan osuvaa sis\u00e4lt\u00f6\u00e4.<\/span><\/li>\n
  6. Sis\u00e4ll\u00f6n k\u00e4ytt\u00e4minen AI-mallin kyselyyn.<\/span><\/li>\n<\/ol>\n
    \n

    Esimerkkimme: Kysyminen ALADIN-mallista<\/span><\/h3>\n

    Kuvittele, ett\u00e4 analysoit tieteellist\u00e4 artikkelia kaupunkien l\u00e4mp\u00f6saarista. P\u00e4iv\u00e4n kysymys on:\u00a0<\/span>“Mik\u00e4 on ALADIN-malli?”<\/span><\/strong>\u00a0🐫☀️<\/span><\/p>\n

    K\u00e4yt\u00e4mme FME:n tuottamaa ty\u00f6nkulkua paperin asiaankuuluvien osien hakemiseen ja kontekstitietoisen vastauksen antamiseen paikallisesta teko\u00e4lymallista.<\/span><\/p>\n

    Vaihe 1: Tekstin purkaminen asiakirjasta<\/span><\/h3>\n

    K\u00e4ytt\u00f6tapaus<\/span><\/strong>\u00a0: Aloita PDF-tiedostosta tai vastaavasta asiakirjasta ja muunna se luettavaksi tekstiksi k\u00e4sittely\u00e4 varten.<\/span><\/p>\n

    Esimerkkidokumenttimme:<\/span><\/p>\n

      \n
    • Otsikko<\/span><\/strong>\u00a0: Arvio kaupunkien vaikutuksista pintaan ja n\u00e4yt\u00f6n tason l\u00e4mp\u00f6tilaan ALADIN-ilmastopohjaisessa SURFEX-maapintamallissa Budapestille<\/span><\/li>\n
    • Tekij\u00e4t<\/span><\/strong>\u00a0: Zsebeh\u00e1zi & Mah\u00f3<\/span><\/li>\n
    • Julkaistu<\/span><\/strong>\u00a0:\u00a0<\/span>Atmosphere<\/span><\/em>\u00a0, voi. 12, ei. 6, 709<\/span><\/li>\n<\/ul>\n

      K\u00e4ytetty lukija: PDF<\/span><\/strong><\/p>\n

      T\u00e4m\u00e4 ominaisuustyyppi edustaa PDF-tiedostoista purettua tekstisis\u00e4lt\u00f6\u00e4.<\/span><\/p>\n

      FME:n kooditon muotoilu tekee yhteyden muodostamisesta PDF-tiedostoon yksinkertaista. Ved\u00e4 ja pudota tiedosto, m\u00e4\u00e4rit\u00e4 asetukset ja olet valmis k\u00e4sittelem\u00e4\u00e4n teksti\u00e4. Voit my\u00f6s poimia kuvia, tekstin sijaintia jne. Kaikki n\u00e4m\u00e4 voivat sy\u00f6tt\u00e4\u00e4 sis\u00e4isen tietokantaasi.<\/span><\/p>\n[\/vc_column_text][image_with_animation image_url=”17215″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”][vc_column_text css=”” text_direction=”default”]\n

      \n
      \n
      \n
      Teksti poimittu, yksi ominaisuus sivua kohden<\/span><\/em><\/figcaption><\/figure>\n<\/div>\n<\/figcaption><\/figure>\n<\/div>\n
      \n
      \n
      \n

      Vaihe 2: Tekstin valmistelu upottamista varten (tietokannan objektit)<\/span><\/h3>\n

      Jaa suuri teksti pienemmiksi, merkityksellisiksi paloiksi, jotta se voidaan tallentaa saadaksesi tarvittavat tiedot tietokantaan. Jokaisella palalla on my\u00f6hemmin sen koordinaatit vektoriavaruudessa.<\/span><\/p>\n

      K\u00e4ytetyt muuntajat<\/span><\/strong>\u00a0:<\/span><\/p>\n

        \n
      • StringReplacer<\/span><\/strong>\u00a0: Puhdistaa ja normalisoi tekstin.<\/span><\/li>\n
      • SubstringExtractor<\/span><\/strong>\u00a0: Jakaa tekstin hallittavissa oleviin osiin (esim. 1 000 merkki\u00e4), joissa on p\u00e4\u00e4llekk\u00e4isi\u00e4 segmenttej\u00e4 kontekstin s\u00e4ilytt\u00e4miseksi paremmin. K\u00e4ytimme FME:t\u00e4 saadaksemme s\u00e4\u00e4nn\u00f6llisen lauseen oikein.<\/span><\/li>\n<\/ul>\n

        On t\u00e4rke\u00e4\u00e4 tasapainottaa kappaleen koko ja sis\u00e4lt\u00f6. Teko\u00e4lyn on helpompi k\u00e4sitell\u00e4 pienempi\u00e4 paloja, mutta niiden on silti sis\u00e4llett\u00e4v\u00e4 tarpeeksi merkityksellist\u00e4 tietoa, jotta kysymykseesi voidaan vastata tehokkaasti.<\/span><\/p>\n<\/div>\n<\/div>\n[\/vc_column_text][image_with_animation image_url=”17214″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”][\/vc_column][\/vc_row][vc_row type=”in_container” full_screen_row_position=”middle” column_margin=”default” column_direction=”default” column_direction_tablet=”default” column_direction_phone=”default” scene_position=”center” text_color=”dark” text_align=”left” row_border_radius=”none” row_border_radius_applies=”bg” overflow=”visible” overlay_strength=”0.3″ gradient_direction=”left_to_right” shape_divider_position=”bottom” bg_image_animation=”none”][vc_column column_padding=”no-extra-padding” column_padding_tablet=”inherit” column_padding_phone=”inherit” column_padding_position=”all” column_element_direction_desktop=”default” column_element_spacing=”default” desktop_text_alignment=”default” tablet_text_alignment=”default” phone_text_alignment=”default” background_color_opacity=”1″ background_hover_color_opacity=”1″ column_backdrop_filter=”none” column_shadow=”none” column_border_radius=”none” column_link_target=”_self” column_position=”default” gradient_direction=”left_to_right” overlay_strength=”0.3″ width=”1\/1″ tablet_width_inherit=”default” animation_type=”default” bg_image_animation=”none” border_type=”simple” column_border_width=”none” column_border_style=”solid”][image_with_animation image_url=”17091″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”][vc_column_text css=”” text_direction=”default”]\n

        \n

        Vaihe 3: Tekstin esitt\u00e4minen vektoriavaruudessa<\/span><\/h3>\n

        Muunna tekstipalojen merkitys numeerisiksi esityksiksi (vektoreiksi) vertailua ja hakua varten.<\/span><\/p>\n

        K\u00e4ytetyt muuntajat<\/span><\/strong>\u00a0:<\/span><\/p>\n

          \n
        • HTTPCaller<\/span><\/strong>\u00a0: muodostaa yhteyden Ollaman API:hen vektoriesitysten luomiseksi.<\/span><\/li>\n<\/ul>\n

          T\u00e4ss\u00e4 vaiheessa jokainen tekstipala muunnetaan vektoriesitykseen\u00a0<\/span>,<\/span><\/strong>\u00a0joka vangitsee sen semanttisen merkityksen. N\u00e4iden vektorien avulla j\u00e4rjestelm\u00e4 voi verrata tekstikappaleita niiden sis\u00e4ll\u00f6n perusteella, mik\u00e4 mahdollistaa samankaltaisuuteen perustuvat haut my\u00f6hemmin.<\/span><\/p>\n

          Miksi k\u00e4ytt\u00e4\u00e4 Ollamaa?<\/span><\/strong><\/p>\n

            \n
          • Paikalliset teko\u00e4lymallit varmistavat tietojen yksityisyyden (t\u00e4ss\u00e4 nomic-embed-text).<\/span><\/li>\n
          • REST API tekee integroinnista yksinkertaista ja joustavaa, koska upottamista varten on oma p\u00e4\u00e4tepiste.<\/span><\/li>\n<\/ul>\n
            <\/div>\n[\/vc_column_text][image_with_animation image_url=”17213″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”][vc_column_text css=”” text_direction=”default”]\n

            HttpCaller-parametrit<\/span><\/em><\/p>\n

            \n
            \n
            <\/div>\n<\/div>\n<\/div>\n[\/vc_column_text][image_with_animation image_url=”17212″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”][vc_column_text css=”” text_direction=”default”]\n

            Kyselyn Json-tuloste, yli 700 ulottuvuuden vektori, joka edustaa tiedon sijaintia<\/span><\/em><\/p>\n

            \n
            <\/div>\n<\/div>\n[\/vc_column_text][vc_column_text css=”” text_direction=”default”]\n
            \n

            Vaihe 4: Tallenna ja etsi relevanttia teksti\u00e4<\/span><\/h3>\n

            Tallenna vektoriesitykset ja l\u00f6yd\u00e4 nopeasti kysymyksellesi osuvin teksti.<\/span><\/p>\n

              \n
            • FeatureWriter<\/span><\/strong>\u00a0: Tallentaa vektoritiedot Parquet-tiedostoon tehokkuuden parantamiseksi.<\/span><\/li>\n<\/ul>\n[\/vc_column_text][image_with_animation image_url=”17211″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”][vc_column_text css=”” text_direction=”default”]\n

              Tallentaa teksti\u00e4 ja vektoreita v\u00e4liaikaiseen parkettitiedostoon<\/span><\/em><\/p>\n

              Upota kysymyksesi toiseen haaraan. Kysy\u00e4ksesi l\u00e4himm\u00e4t vektorit tarvitset kysymyksesi sijainnin.<\/span><\/p>\n[\/vc_column_text][image_with_animation image_url=”17210″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”][vc_column_text css=”” text_direction=”default”]\n

              Kysymysten luominen ja kutsu upottamiseen p\u00e4\u00e4tepisteeseen<\/span><\/em><\/p>\n

              Lataa tietokantaan ja tee kysely SQLExecutorin kautta.<\/span><\/p>\n[\/vc_column_text][image_with_animation image_url=”17209″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”][vc_column_text css=”” text_direction=”default”]\n

              \n
              #Delimiter to send the requests to in memory duckdb in sequence\r\nFME_SQL_DELIMITER ;\r\n#Install  Vector Similarity Search Extension to be able to create an index and speed up search\r\nINSTALL vss;\r\nLOAD vss;\r\n#Create temp table from parquet. FLOAT[768] is an array of 768 dims (same as embedding from nomic)\r\nCREATE TABLE embeddings AS SELECT id, text, json_extract(content, '$')::FLOAT[768] AS embedding FROM read_parquet('embed.parquet');\r\n#Index creation, metric is specified to cosine but this is the default one) \r\nCREATE INDEX idx_embeddings_vss ON embeddings USING HNSW(embedding) WITH (metric = 'cosine');\r\n#Select the text from the 5 top answers (meaning closest in vector space)\r\nSELECT text FROM embeddings ORDER BY array_distance(embedding,@Value(_response_body)::FLOAT[768]) LIMIT 5;<\/pre>\n<\/blockquote>\n[\/vc_column_text][vc_column_text css=”” text_direction=”default”]\n
              DuckDB antaa sinun tehd\u00e4 kyselyit\u00e4 n\u00e4ist\u00e4 vektoreista l\u00f6yt\u00e4\u00e4ksesi kysymyksesi kannalta oleellisimmat. Jos haluat k\u00e4ytt\u00e4\u00e4 toista tietokantaa, FME tekee vaihtamisesta saumatonta.<\/span><\/p>\n
              Miksi k\u00e4ytt\u00e4\u00e4 FME:t\u00e4?<\/span><\/strong>\u00a0: FME:n koodittomien tietokantaliittimien avulla voit mukauttaa ty\u00f6nkulkua mihin tahansa tietokantaan ilman teknisi\u00e4 esteit\u00e4.<\/span><\/p>\n[\/vc_column_text][vc_column_text css=”” text_direction=”default”]\n
              \n
              Vaihe 5: Kysy AI-mallilta kysymyksesi<\/span><\/h3>\n
              K\u00e4ytt\u00f6tapaus<\/span><\/strong>\u00a0: Yhdist\u00e4 haettu teksti kysymykseesi ja l\u00e4het\u00e4 se teko\u00e4lymalliin vastausta varten.<\/span><\/p>\n
              K\u00e4ytetyt muuntajat<\/span><\/strong>\u00a0:<\/span><\/p>\n
                \n
              • AttributeCreator<\/span><\/strong>\u00a0: Yhdist\u00e4\u00e4 kysymyksen ja haetun tekstin. Valmistelee POST-pyynt\u00f6si JSON-rungon.<\/span><\/li>\n
              • StringCleaner<\/span><\/strong>\u00a0: Auttaa poistamaan kielletyt merkit JSON-muodossa l\u00e4hetett\u00e4v\u00e4st\u00e4 tekstist\u00e4.<\/span><\/li>\n
              • HTTPCaller<\/span><\/strong>\u00a0: L\u00e4hett\u00e4\u00e4 yhdistetyt tiedot Ollamaan vastausten luomista varten. Varo kontekstin kokoa ja aikakatkaisua.<\/span><\/li>\n<\/ul>\n
                Teko\u00e4lymalli k\u00e4ytt\u00e4\u00e4 haettuja tekstikappaleita kontekstina luodakseen tietoisen ja asiaankuuluvan vastauksen kysymykseesi.<\/span><\/p>\n[\/vc_column_text][image_with_animation image_url=”17208″ image_size=”full” animation_type=”entrance” animation=”None” animation_movement_type=”transform_y” hover_animation=”none” alignment=”center” border_radius=”none” box_shadow=”none” image_loading=”default” max_width=”100%” max_width_mobile=”default”][vc_column_text css=”” text_direction=”default”]\n
                \n
                {\r\n  \"model\": \"@Value(model)\",\r\n  \"prompt\": \" Please use the following article context : @Value(text). Then, based on it and your knowledge, answer : @Value(prompt)\",\r\n  \"stream\": false,\r\n  \"options\": {\r\n    \"num_ctx\": 4000\r\n  }\r\n}<\/pre>\n<\/blockquote>\n[\/vc_column_text][vc_column_text css=”” text_direction=”default”]\n
                \n
                Vaihe 6: Tulosten esitt\u00e4minen<\/span><\/h3>\n
                Muotoile ja toimita teko\u00e4lyn vastaus haluamassasi muodossa.<\/span><\/p>\n
                K\u00e4ytetyt muuntajat<\/span><\/strong>\u00a0:<\/span><\/p>\n
                  \n
                • JsonFragmenter<\/span><\/strong>\u00a0: Purkaa vastauskent\u00e4n teko\u00e4lyn JSON-l\u00e4hd\u00f6st\u00e4.<\/span><\/li>\n
                • FeatureWriter<\/span><\/strong>\u00a0: Tulostaa tulokset tiedostoon tai tietokantaan.<\/span><\/li>\n<\/ul>\n
                  FME:n avulla voit tulostaa tulokset JSON- tai CSV-muodossa tai jopa ty\u00f6nt\u00e4\u00e4 ne suoraan kojelaudoihin tai muihin alavirran j\u00e4rjestelmiin.<\/span><\/p>\n
                  \n
                  Miksi k\u00e4ytt\u00e4\u00e4 t\u00e4t\u00e4 ty\u00f6nkulkua?<\/span><\/h3>\n
                  FME:n vahvuudet<\/span><\/strong>\u00a0:<\/span><\/p>\n
                    \n
                  1. Yhdist\u00e4 ilman koodia<\/span><\/strong>\u00a0: Ty\u00f6skentele helposti PDF-tiedostojen, tietokantojen tai sovellusliittymien kanssa ilman koodia.<\/span><\/li>\n
                  2. Joustavat tietokantavaihtoehdot<\/span><\/strong>\u00a0: Vaihda tietokantoja (esim. DuckDB PostgreSQL:\u00e4\u00e4n) v\u00e4h\u00e4ll\u00e4 vaivalla.<\/span><\/li>\n
                  3. Saumaton API-integraatio<\/span><\/strong>\u00a0: Yhdist\u00e4 vaivattomasti sovellusliittymiin, kuten Ollaman REST-sovellusliittym\u00e4\u00e4n teko\u00e4lypohjaista k\u00e4sittely\u00e4 varten.<\/span><\/li>\n<\/ol>\n
                    Ollaman edut<\/span><\/strong>\u00a0:<\/span><\/p>\n
                      \n
                    • Paikalliset teko\u00e4lymallit<\/span><\/strong>\u00a0: Varmistaa tietojen yksityisyyden ja hallinnan.<\/span><\/li>\n
                    • REST API<\/span><\/strong>\u00a0: Yksinkertaistaa vektorien luomista ja kyselyit\u00e4.<\/span><\/li>\n<\/ul>\n
                      DuckDB edut<\/span><\/strong>\u00a0:<\/span><\/p>\n
                        \n
                      • Muistissa oleva suorituskyky<\/span><\/strong>\u00a0: Nopea k\u00e4sittely ja kyselyn suoritus.<\/span><\/li>\n
                      • SQL-yhteensopivuus<\/span><\/strong>\u00a0: Tuttu syntaksi helpottaa integrointia.<\/span><\/li>\n
                      • Asennusta ei tarvita<\/span><\/strong>\u00a0: Koska aiomme julkaista t\u00e4m\u00e4n FMEHubissa, t\u00e4m\u00e4 on kevyemp\u00e4\u00e4 kuin esimerkiksi Postgresin asentaminen.<\/span><\/li>\n<\/ul>\n
                        \n
                        Seuraavat vaiheet: Ty\u00f6nkulun parantaminen<\/span><\/h3>\n
                        T\u00e4ss\u00e4 on muutamia tapoja rakentaa t\u00e4lle perustalle:<\/span><\/p>\n
                          \n
                        1. Laajenna tietol\u00e4hteit\u00e4<\/span><\/strong>\u00a0: Integroi reaaliaikaiset sovellusliittym\u00e4t, pilvitallennus, rasteridata tai verkkokaappaus. FME:ll\u00e4 kaikki tiedot ovat tavoitettavissa.<\/span><\/li>\n
                        2. Paranna automaatiota<\/span><\/strong>\u00a0: Ota ty\u00f6nkulku k\u00e4ytt\u00f6\u00f6n FME-palvelimella aikataulutusta ja skaalautuvuutta varten.<\/span><\/li>\n
                        3. Kokeile lis\u00e4asetuksia<\/span><\/strong>\u00a0: K\u00e4yt\u00e4 tarkempia malleja tai parametreja, kuten l\u00e4mp\u00f6tilaa, n\u00e4hd\u00e4ksesi, kuinka se vaikuttaa vastaukseen.<\/span><\/li>\n
                        4. Vuorovaikutteiset tulokset<\/span><\/strong>\u00a0: Ty\u00f6nn\u00e4 tulokset kojelaudoille tai ilmoituksiin saadaksesi hy\u00f6dyllisi\u00e4 oivalluksia.<\/span><\/li>\n<\/ol>\n
                          \n
                          T\u00e4m\u00e4 ty\u00f6nkulku n\u00e4ytt\u00e4\u00e4, kuinka FME voi yksinkertaistaa RAG-ty\u00f6nkulkuja yhdist\u00e4m\u00e4ll\u00e4 asiakirjasi paikallisiin teko\u00e4lymalleihin, kuten Ollama. Pienell\u00e4 vaivalla voit rakentaa joustavia ja tehokkaita ratkaisuja, jotka on r\u00e4\u00e4t\u00e4l\u00f6ity tarpeisiisi. Jos olet kiinnostunut jatkamaan t\u00e4t\u00e4 l\u00e4hestymistapaa, ota rohkeasti yhteytt\u00e4 saadaksesi lis\u00e4tietoja!<\/span><\/p>\n[\/vc_column_text][\/vc_column][\/vc_row]","protected":false},"excerpt":{"rendered":"
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