Ricerca conversazionale con OCI Generative AI

Fornire le informazioni per i segnaposto seguenti nel payload:

• <connector_name>: immettere un nome univoco per identificare il connettore. Ad esempio, "connettore command-r-plus cohere v01".
• <connector_description>: immettere una breve descrizione del connettore con una lunghezza massima di 30 caratteri. Ad esempio, "Il mio connettore per il modello GenAI cohere.command-r-plus".
• <compartment_OCID>: immettere l'OCID del compartimento in cui risiede il cluster OpenSearch.
• <genai_cohere_model>: immettere il nome del modello Cohere che si desidera utilizzare. Ad esempio, "cohere.command-r-plus".

POST _plugins/_ml/connectors/_create
{
     "name": "<connector_name>",
     "description": "<connector_description>",
     "version": 2,
     "protocol": "oci_sigv1",
     "parameters": {
         "endpoint": "inference.generativeai.us-chicago-1.oci.oraclecloud.com",
         "auth_type": "resource_principal"
     },
     "credential": {
     },
     "actions": [
         {
             "action_type": "predict",
             "method": "POST",
             "url": "https://${parameters.endpoint}/20231130/actions/chat",
             "request_body": "{\"compartmentId\":\"<compartment_OCID>\",\"servingMode\":{\"modelId\":\"<genai_cohere_model>\",\"servingType\":\"ON_DEMAND\"},\"chatRequest\":{\"message\":\"${parameters.prompt}\",\"maxTokens\":600,\"temperature\":1,\"frequencyPenalty\":0,\"presencePenalty\":0,\"topP\":0.75,\"topK\":0,\"isStream\":false,\"chatHistory\":[],\"apiFormat\":\"COHERE\"}}",
             "post_process_function": "def text = params['chatResponse']['text'].replace('\n', '\\\\n').replace('\"','');\n return '{\"name\":\"response\",\"dataAsMap\":{\"inferenceResponse\":{\"generatedTexts\":[{\"text\":\"' + text + '\"}]}}}'"
  
         }
     ]
 }

Fornire le informazioni per i segnaposto seguenti nel payload:

• <connector_name>: immettere un nome univoco per identificare il connettore. Ad esempio, "connettore meta-llama v1".
• <connector_description>: immettere una breve descrizione del connettore con una lunghezza massima di 30 caratteri. Ad esempio, "Il mio connettore per GenAI meta.llama"."
• <compartment_OCID>: immettere l'OCID del compartimento in cui risiede il cluster OpenSearch.
• <genai_llama_model>: immettere il nome del modello LLAMA che si desidera utilizzare. Ad esempio, "connettore meta-llama v1".

POST _plugins/_ml/connectors/_create
{
     "name": "<connector_name>",
     "description": "<connector_description>",
     "version": 2,
     "protocol": "oci_sigv1",
     "parameters": {
         "endpoint": "inference.generativeai.us-chicago-1.oci.oraclecloud.com",
         "auth_type": "resource_principal"
     },
     "credential": {
     },
     "actions": [
         {
             "action_type": "predict",
             "method": "POST",
             "url": "https://${parameters.endpoint}/20231130/actions/chat",
             "request_body": "{\"compartmentId\":\<compartment_OCID>\",\"servingMode\":{\"modelId\":\"<genai_llama_model>\",\"servingType\":\"ON_DEMAND\"},\"chatRequest\":{\"maxTokens\":600,\"temperature\":1,\"frequencyPenalty\":0,\"presencePenalty\":0,\"topP\":0.75,\"topK\":-1,\"isStream\":false,\"apiFormat\":\"GENERIC\",\"messages\":[{\"role\":\"USER\",\"content\":[{\"type\":\"TEXT\",\"text\":\"${parameters.prompt}\"}]}]}}",
              
              "post_process_function": "def text = params['chatResponse']['choices'][0]['message']['content'][0]['text'].replace('\n', '\\\\n').replace('\"','');\n return '{\"name\":\"response\",\"dataAsMap\":{\"inferenceResponse\":{\"generatedTexts\":[{\"text\":\"' + text + '\"}]}}}'"
 
 
 
         }
     ]
 }

Di seguito sono riportati alcuni esempi di modelli di connettore GenAI per i modelli OpenAI.

Questa sezione descrive i passi per creare un indice senza utilizzare una pipeline di inclusione.

Utilizzare questa opzione per creare dati senza incorporarli,

1. Creare un indice di ricerca, come mostrato nell'esempio seguente:

   PUT /conversation-demo-index
   {
     "settings": {
       "index": {
         "number_of_shards": 1,
         "number_of_replicas": 0
       }
     },
     "mappings": {
       "properties": {
         "title": {
           "type": "text"
         },
         "text": {
           "type": "text"
         }
       }
     }
   }

2. Includere i dati nell'indice, come mostrato nell'esempio seguente:

   PUT /conversation-demo-index/_doc/1
   {
       "text": "The emergence of resistance of bacteria to antibiotics is a common phenomenon. Emergence of resistance often reflects evolutionary processes that take place during antibiotic therapy. The antibiotic treatment may select for bacterial strains with physiologically or genetically enhanced capacity to survive high doses of antibiotics. Under certain conditions, it may result in preferential growth of resistant bacteria, while growth of susceptible bacteria is inhibited by the drug. For example, antibacterial selection for strains having previously acquired antibacterial-resistance genes was demonstrated in 1943 by the Luria–Delbrück experiment. Antibiotics such as penicillin and erythromycin, which used to have a high efficacy against many bacterial species and strains, have become less effective, due to the increased resistance of many bacterial strains."
      
   }
   GET /conversation-demo-index/_doc/1
   PUT /conversation-demo-index/_doc/2
   {
     "text": "The successful outcome of antimicrobial therapy with antibacterial compounds depends on several factors. These include host defense mechanisms, the location of infection, and the pharmacokinetic and pharmacodynamic properties of the antibacterial. A bactericidal activity of antibacterials may depend on the bacterial growth phase, and it often requires ongoing metabolic activity and division of bacterial cells. These findings are based on laboratory studies, and in clinical settings have also been shown to eliminate bacterial infection. Since the activity of antibacterials depends frequently on its concentration, in vitro characterization of antibacterial activity commonly includes the determination of the minimum inhibitory concentration and minimum bactericidal concentration of an antibacterial. To predict clinical outcome, the antimicrobial activity of an antibacterial is usually combined with its pharmacokinetic profile, and several pharmacological parameters are used as markers of drug efficacy."
   }
    
   PUT /conversation-demo-index/_doc/3
   {
     "text": "Antibacterial antibiotics are commonly classified based on their mechanism of action, chemical structure, or spectrum of activity. Most target bacterial functions or growth processes. Those that target the bacterial cell wall (penicillins and cephalosporins) or the cell membrane (polymyxins), or interfere with essential bacterial enzymes (rifamycins, lipiarmycins, quinolones, and sulfonamides) have bactericidal activities. Those that target protein synthesis (macrolides, lincosamides and tetracyclines) are usually bacteriostatic (with the exception of bactericidal aminoglycosides). Further categorization is based on their target specificity. Narrow-spectrum antibacterial antibiotics target specific types of bacteria, such as Gram-negative or Gram-positive bacteria, whereas broad-spectrum antibiotics affect a wide range of bacteria. Following a 40-year hiatus in discovering new classes of antibacterial compounds, four new classes of antibacterial antibiotics have been brought into clinical use in the late 2000s and early 2010s: cyclic lipopeptides (such as daptomycin), glycylcyclines (such as tigecycline), oxazolidinones (such as linezolid), and lipiarmycins (such as fidaxomicin)"
   }
    
    
   PUT /conversation-demo-index/_doc/4
   {
     "text": "The Desert Land Act of 1877 was passed to allow settlement of arid lands in the west and allotted 640 acres (2.6 km2) to settlers for a fee of $.25 per acre and a promise to irrigate the land. After three years, a fee of one dollar per acre would be paid and the land would be owned by the settler. This act brought mostly cattle and sheep ranchers into Montana, many of whom grazed their herds on the Montana prairie for three years, did little to irrigate the land and then abandoned it without paying the final fees. Some farmers came with the arrival of the Great Northern and Northern Pacific Railroads throughout the 1880s and 1890s, though in relatively small numbers"
   }
    
   PUT /conversation-demo-index/_doc/5
   {
     "text": "In the early 1900s, James J. Hill of the Great Northern began promoting settlement in the Montana prairie to fill his trains with settlers and goods. Other railroads followed suit. In 1902, the Reclamation Act was passed, allowing irrigation projects to be built in Montana's eastern river valleys. In 1909, Congress passed the Enlarged Homestead Act that expanded the amount of free land from 160 to 320 acres (0.6 to 1.3 km2) per family and in 1912 reduced the time to prove up on a claim to three years. In 1916, the Stock-Raising Homestead Act allowed homesteads of 640 acres in areas unsuitable for irrigation.  This combination of advertising and changes in the Homestead Act drew tens of thousands of homesteaders, lured by free land, with World War I bringing particularly high wheat prices. In addition, Montana was going through a temporary period of higher-than-average precipitation. Homesteaders arriving in this period were known as Honyockers, or scissorbills. Though the word honyocker, possibly derived from the ethnic slur hunyak, was applied in a derisive manner at homesteaders as being greenhorns, new at his business or unprepared, the reality was that a majority of these new settlers had previous farming experience, though there were also many who did not"
   }
    
   PUT /conversation-demo-index/_doc/6
   {
     "text": "In June 1917, the U.S. Congress passed the Espionage Act of 1917 which was later extended by the Sedition Act of 1918, enacted in May 1918. In February 1918, the Montana legislature had passed the Montana Sedition Act, which was a model for the federal version. In combination, these laws criminalized criticism of the U.S. government, military, or symbols through speech or other means. The Montana Act led to the arrest of over 200 individuals and the conviction of 78, mostly of German or Austrian descent. Over 40 spent time in prison. In May 2006, then-Governor Brian Schweitzer posthumously issued full pardons for all those convicted of violating the Montana Sedition Act."
   }
    
   PUT /conversation-demo-index/_doc/7
   {
     "text": "When the U.S. entered World War II on December 8, 1941, many Montanans already had enlisted in the military to escape the poor national economy of the previous decade. Another 40,000-plus Montanans entered the armed forces in the first year following the declaration of war, and over 57,000 joined up before the war ended. These numbers constituted about 10 percent of the state's total population, and Montana again contributed one of the highest numbers of soldiers per capita of any state. Many Native Americans were among those who served, including soldiers from the Crow Nation who became Code Talkers. At least 1500 Montanans died in the war. Montana also was the training ground for the First Special Service Force or Devil's Brigade a joint U.S-Canadian commando-style force that trained at Fort William Henry Harrison for experience in mountainous and winter conditions before deployment. Air bases were built in Great Falls, Lewistown, Cut Bank and Glasgow, some of which were used as staging areas to prepare planes to be sent to allied forces in the Soviet Union. During the war, about 30 Japanese balloon bombs were documented to have landed in Montana, though no casualties nor major forest fires were attributed to them"
   }

Poiché questa procedura non utilizza una pipeline di inclusione con questo indice, le integrazioni non vengono generate per i documenti di testo durante l'inclusione. Ciò significa che solo la ricerca BM25 viene utilizzata per recuperare i documenti pertinenti.

Dopo aver creato un indice di ricerca senza il plugin k-NN, quando si esegue il seguente comando per controllare un documento indicizzato, viene restituito solo il testo, come segue:

Richiesta:

GET /conversation-demo-index/_doc/1

Risposta:

{
  "_index": "conversation-demo-index",
  "_id": "1",
  "_version": 1,
  "_seq_no": 0,
  "_primary_term": 1,
  "found": true,
  "_source": {
    "text": "The emergence of resistance of bacteria to antibiotics is a common phenomenon. Emergence of resistance often reflects evolutionary processes that take place during antibiotic therapy. The antibiotic treatment may select for bacterial strains with physiologically or genetically enhanced capacity to survive high doses of antibiotics. Under certain conditions, it may result in preferential growth of resistant bacteria, while growth of susceptible bacteria is inhibited by the drug. For example, antibacterial selection for strains having previously acquired antibacterial-resistance genes was demonstrated in 1943 by the Luria–Delbrück experiment. Antibiotics such as penicillin and erythromycin, which used to have a high efficacy against many bacterial species and strains, have become less effective, due to the increased resistance of many bacterial strains."
  }
}

Per utilizzare la ricerca ibrida anziché BM25 per arricchire la parte di recupero della pipeline RAG, è necessario impostare una pipeline di inclusione e utilizzare un modello pre-addestrato importato in modo che le integrazioni dei documenti vengano create al momento dell'inclusione.

Utilizzare questa opzione per creare l'indice con una pipeline di inclusione per generare automaticamente l'incorporamento dei dati durante l'inclusione.

1. Ai fini di questa procedura guidata, utilizzare una delle seguenti opzioni:

   • Opzione 1: registrare e distribuire un modello pre-addestrato ospitato in OCI Search con OpenSearch utilizzando i passi descritti in Uso di un modello pre-addestrato OpenSearch. Questa opzione è la più semplice da usare, non è necessario configurare criteri IAM aggiuntivi e il payload non è complesso come il payload per l'opzione successiva.

   • Opzione 2: importare, registrare e distribuire un modello pre-addestrato OpenSearch utilizzando i passi descritti in Modelli personalizzati. Ciò include il caricamento del file modello in un bucket di storage degli oggetti, quindi la specifica dell'URL di storage degli oggetti del file modello quando si registra il modello.

   • Opzione 3: è inoltre possibile registrare e distribuire un modello di incorporamento GenAI remoto, ad esempio cohere.embed-english-v3.0, nel cluster utilizzando il connettore GenAI. È necessario prima creare un connettore, quindi registrare e distribuire il modello utilizzando l'ID connettore come descritto nei passi riportati di seguito.

     Nota
     
     

     Se si utilizza il modello ON-DEMAND, tenere aggiornate le notifiche di deprecazione del modello dal servizio GenAI e aggiornare il connettore quando necessario per evitare potenziali interruzioni del servizio. Fare riferimento alla sezione Modelli di base pre-addestrati nell'AI generativa per i modelli di incorporamento supportati per selezionare un modello di incorporamento dall'elenco dei modelli supportati.

     Se si utilizza il modello DEDICATED, modificare il parametro servingType nell'esempio di payload seguente da ON-DEMAND a DEDICATED.

   Prendere nota dell'ID modello restituito quando si registra e si distribuisce il modello.

2. Creare una pipeline di inclusione utilizzando il comando model id del passo precedente, come mostrato nell'esempio seguente:

   PUT _ingest/pipeline/minil12-test-pipeline
   {
     "description": "pipeline for RAG demo index",
     "processors" : [
       {
         "text_embedding": {
           "model_id": "<embedding_model_ID>",
           "field_map": {
              "text": "passage_embedding"
           }
         }
       }
     ]
   }

3. Creare un indice di ricerca con una pipeline di inclusione, come mostrato nell'esempio riportato di seguito.

   PUT /conversation-demo-index-knn
   {
       "settings": {
           "index.knn": true,
           "default_pipeline": "minil12-test-pipeline"
       },
       "mappings": {
           "properties": {
               "passage_embedding": {
                   "type": "knn_vector",
                   "dimension": <model_dimension>,
                   "method": {
                       "name":"hnsw",
                       "engine":"lucene",
                       "space_type": "l2",
                       "parameters":{
                           "m":512,
                           "ef_construction": 245
                       }
                   }
               },
               "text": {
                   "type": "text"
               }
           }
       }
   }

4. Includere i dati utilizzando la pipeline di inclusione dal passo precedente, come mostrato nell'esempio seguente

   PUT /conversation-demo-index-knn/_doc/1
   {
       "text": "The emergence of resistance of bacteria to antibiotics is a common phenomenon. Emergence of resistance often reflects evolutionary processes that take place during antibiotic therapy. The antibiotic treatment may select for bacterial strains with physiologically or genetically enhanced capacity to survive high doses of antibiotics. Under certain conditions, it may result in preferential growth of resistant bacteria, while growth of susceptible bacteria is inhibited by the drug. For example, antibacterial selection for strains having previously acquired antibacterial-resistance genes was demonstrated in 1943 by the Luria–Delbrück experiment. Antibiotics such as penicillin and erythromycin, which used to have a high efficacy against many bacterial species and strains, have become less effective, due to the increased resistance of many bacterial strains."
      
   }
   GET /conversation-demo-index-knn/_doc/1
   PUT /conversation-demo-index-knn/_doc/2
   {
     "text": "The successful outcome of antimicrobial therapy with antibacterial compounds depends on several factors. These include host defense mechanisms, the location of infection, and the pharmacokinetic and pharmacodynamic properties of the antibacterial. A bactericidal activity of antibacterials may depend on the bacterial growth phase, and it often requires ongoing metabolic activity and division of bacterial cells. These findings are based on laboratory studies, and in clinical settings have also been shown to eliminate bacterial infection. Since the activity of antibacterials depends frequently on its concentration, in vitro characterization of antibacterial activity commonly includes the determination of the minimum inhibitory concentration and minimum bactericidal concentration of an antibacterial. To predict clinical outcome, the antimicrobial activity of an antibacterial is usually combined with its pharmacokinetic profile, and several pharmacological parameters are used as markers of drug efficacy."
   }
    
   PUT /conversation-demo-index-knn/_doc/3
   {
     "text": "Antibacterial antibiotics are commonly classified based on their mechanism of action, chemical structure, or spectrum of activity. Most target bacterial functions or growth processes. Those that target the bacterial cell wall (penicillins and cephalosporins) or the cell membrane (polymyxins), or interfere with essential bacterial enzymes (rifamycins, lipiarmycins, quinolones, and sulfonamides) have bactericidal activities. Those that target protein synthesis (macrolides, lincosamides and tetracyclines) are usually bacteriostatic (with the exception of bactericidal aminoglycosides). Further categorization is based on their target specificity. Narrow-spectrum antibacterial antibiotics target specific types of bacteria, such as Gram-negative or Gram-positive bacteria, whereas broad-spectrum antibiotics affect a wide range of bacteria. Following a 40-year hiatus in discovering new classes of antibacterial compounds, four new classes of antibacterial antibiotics have been brought into clinical use in the late 2000s and early 2010s: cyclic lipopeptides (such as daptomycin), glycylcyclines (such as tigecycline), oxazolidinones (such as linezolid), and lipiarmycins (such as fidaxomicin)"
   }
    
    
   PUT /conversation-demo-index-knn/_doc/4
   {
     "text": "The Desert Land Act of 1877 was passed to allow settlement of arid lands in the west and allotted 640 acres (2.6 km2) to settlers for a fee of $.25 per acre and a promise to irrigate the land. After three years, a fee of one dollar per acre would be paid and the land would be owned by the settler. This act brought mostly cattle and sheep ranchers into Montana, many of whom grazed their herds on the Montana prairie for three years, did little to irrigate the land and then abandoned it without paying the final fees. Some farmers came with the arrival of the Great Northern and Northern Pacific Railroads throughout the 1880s and 1890s, though in relatively small numbers"
   }
    
   PUT /conversation-demo-index-knn/_doc/5
   {
     "text": "In the early 1900s, James J. Hill of the Great Northern began promoting settlement in the Montana prairie to fill his trains with settlers and goods. Other railroads followed suit. In 1902, the Reclamation Act was passed, allowing irrigation projects to be built in Montana's eastern river valleys. In 1909, Congress passed the Enlarged Homestead Act that expanded the amount of free land from 160 to 320 acres (0.6 to 1.3 km2) per family and in 1912 reduced the time to prove up on a claim to three years. In 1916, the Stock-Raising Homestead Act allowed homesteads of 640 acres in areas unsuitable for irrigation.  This combination of advertising and changes in the Homestead Act drew tens of thousands of homesteaders, lured by free land, with World War I bringing particularly high wheat prices. In addition, Montana was going through a temporary period of higher-than-average precipitation. Homesteaders arriving in this period were known as Honyockers, or scissorbills. Though the word honyocker, possibly derived from the ethnic slur hunyak, was applied in a derisive manner at homesteaders as being greenhorns, new at his business or unprepared, the reality was that a majority of these new settlers had previous farming experience, though there were also many who did not"
   }
    
   PUT /conversation-demo-index-knn/_doc/6
   {
     "text": "In June 1917, the U.S. Congress passed the Espionage Act of 1917 which was later extended by the Sedition Act of 1918, enacted in May 1918. In February 1918, the Montana legislature had passed the Montana Sedition Act, which was a model for the federal version. In combination, these laws criminalized criticism of the U.S. government, military, or symbols through speech or other means. The Montana Act led to the arrest of over 200 individuals and the conviction of 78, mostly of German or Austrian descent. Over 40 spent time in prison. In May 2006, then-Governor Brian Schweitzer posthumously issued full pardons for all those convicted of violating the Montana Sedition Act."
   }
    
   PUT /conversation-demo-index-knn/_doc/7
   {
     "text": "When the U.S. entered World War II on December 8, 1941, many Montanans already had enlisted in the military to escape the poor national economy of the previous decade. Another 40,000-plus Montanans entered the armed forces in the first year following the declaration of war, and over 57,000 joined up before the war ended. These numbers constituted about 10 percent of the state's total population, and Montana again contributed one of the highest numbers of soldiers per capita of any state. Many Native Americans were among those who served, including soldiers from the Crow Nation who became Code Talkers. At least 1500 Montanans died in the war. Montana also was the training ground for the First Special Service Force or Devil's Brigade a joint U.S-Canadian commando-style force that trained at Fort William Henry Harrison for experience in mountainous and winter conditions before deployment. Air bases were built in Great Falls, Lewistown, Cut Bank and Glasgow, some of which were used as staging areas to prepare planes to be sent to allied forces in the Soviet Union. During the war, about 30 Japanese balloon bombs were documented to have landed in Montana, though no casualties nor major forest fires were attributed to them"
   }

Dopo aver creato l'indice di ricerca con il plugin k-NN, quando si esegue il seguente comando per controllare un documento indicizzato, la risposta include le integrazioni, come indicato di seguito.

Richiesta:

GET /conversation-demo-index-knn/_doc/1

Risposta:

{
  "_index": "conversation-demo-index-knn",
  "_id": "1",
  "_version": 1,
  "_seq_no": 0,
  "_primary_term": 1,
  "found": true,
  "_source": {
    "passage_embedding": [
      -0.02929831,
      -0.04421867,
      -0.10647401,
      0.07105031,
      0.004921746,
      -0.04529944,
      -0.092778176,
      0.14189903,
      -0.0016610072,
      0.08001712,
      0.053442925,
      -0.022703059,
      0.039608333,
      0.042299673,
      ..............

Eseguire questo comando per creare l'ID conversazione:

POST /_plugins/_ml/memory/conversation
{
  "name": "rag-conversation"
}

Risposta:

{
  "conversation_id": "<conversation_ID>"
}