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Julep AI 8

Assistant with text-to-SQL Performance

2024-02-26T11:31:24.067Z

<p>It should definitely help. I would approach it iteratively. Start with a simplified query to get a feel for how to best phrase your instructions and functions descriptions to get the Assistant to return the required variables, then progressively build out the complexity of the query.</p> <p>One thing you may also consider is to supply the Assistant with example queries as part of the knowledge database and explicitly reference to this file in your instructions.</p> <p>There is not a single dedicated resource I am aware of right now that explicitly addresses this point. However, you can find quite a few posts across the forum that discuss strategies for optimizing Assistant instructions.</p> <p>Without being familiar with the details of your DB and dataset, it’s a bit difficult to be too specific with recommendations. But to reiterate: solely based on my experience I can say that being extremely specific and detailed in the function description, in particular as it relates to the nature of values for each column in the SQL DB, has made a significant difference.</p> <p>If you are in a position to share some more concrete information about your data and your existing instructions and functions, then myself and/or others can help to take a closer look.</p>

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It should definitely help. I would approach it iteratively. Start with a simplified query to get a feel for how to best phrase your instructions and functions descriptions to get the Assistant to return the required variables, then progressively build out the complexity of the query. One thing you may also consider is to supply the Assistant with example queries as part of the knowledge database and explicitly reference to this file in your instructions. There is not a single dedicated resource I am aware of right now that explicitly addresses this point. However, you can find quite a few posts across the forum that discuss strategies for optimizing Assistant instructions. Without being familiar with the details of your DB and dataset, it’s a bit difficult to be too specific with recommendations. But to reiterate: solely based on my experience I can say that being extremely specific and detailed in the function description, in particular as it relates to the nature of values for each column in the SQL DB, has made a significant difference. If you are in a position to share some more concrete information about your data and your existing instructions and functions, then myself and/or others can help to take a closer look.

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https://community.openai.com/t/654892

https://community.openai.com/p/894127

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Assistant with text-to-SQL Performance

2024-02-26T12:17:02.487Z

<aside class="quote no-group" data-username="jr.2509" data-post="4" data-topic="654892"> <div class="title"> <div class="quote-controls"></div> <img loading="lazy" alt="" width="24" height="24" src="https://avatars.discourse-cdn.com/v4/letter/j/76d3ee/48.png" class="avatar"> jr.2509:</div> <blockquote> <p>I have found in practice that it helped to include a description of the SQL DB structure (i.e. the different columns and types of data/values in each column) in the instructions as well as to be specific about how the Assistant is expected to identify the inputs/variables required for the SQL query.</p> </blockquote> </aside> <p>Having done this for a while now, I can say this is the most critical piece. You MUST define you database tables as specifically as you can. No just table and field names – that’s jus the beginning. You have to also describe what each field is, what it does, and it’s relationship to the bigger picture. Ditto with the tables themselves. And keep in mind, when you are doing this, how your users are going to phrase their questions.</p> <p>For example, I have a table named “log” with a field called “type”. Not only do I need to describe what the table is and what “types” are possible, but also let the model know that some people may ask questions like, “show me the queries for this date” which means search the log table for field type = “query”.</p> <p>The more info you give the model about your data, beyond just the structure, the better it i going to be at answering questions.</p>

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 jr.2509: > > I have found in practice that it helped to include a description of the SQL DB structure (i.e. the different columns and types of data/values in each column) in the instructions as well as to be specific about how the Assistant is expected to identify the inputs/variables required for the SQL query. > > > Having done this for a while now, I can say this is the most critical piece. You MUST define you database tables as specifically as you can. No just table and field names – that’s jus the beginning. You have to also describe what each field is, what it does, and it’s relationship to the bigger picture. Ditto with the tables themselves. And keep in mind, when you are doing this, how your users are going to phrase their questions. For example, I have a table named “log” with a field called “type”. Not only do I need to describe what the table is and what “types” are possible, but also let the model know that some people may ask questions like, “show me the queries for this date” which means search the log table for field type = “query”. The more info you give the model about your data, beyond just the structure, the better it i going to be at answering questions.

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https://community.openai.com/t/654892

https://community.openai.com/p/894216

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Assistant with text-to-SQL Performance

2024-02-26T14:03:27.775Z

<p>hi <a class="mention" href="/u/jr.2509">@jr.2509</a> , i appreciate your effort to answer my questions, to put you more in context I’m working on the odoo database</p> <pre><code class="lang-auto">[ { "table_name": "product_tic_category", "fields": [ "id", "code", "create_uid", "write_uid", "description", "create_date", "write_date" ] }, { "table_name": "ir_module_module_dependency", "fields": [ "id", "name", "module_id", "auto_install_required" ] }, { "table_name": "data_frame", "fields": [ "id", "create_uid", "write_uid", "quest_text", "sql_query", "create_date", "write_date", "fine_tune_module_id" ] }, { "table_name": "model_model", "fields": [ "id", "create_uid", "write_uid", "name", "create_date", "write_date", "fine_tune_module_id" ] }, { "table_name": "ir_model_fields", "fields": [ "id", "relation_field_id", "model_id", "related_field_id", "size", "create_uid", "write_uid", "name", "complete_name", "model", "relation", "relation_field", "ttype", "related", "state", "on_delete", "domain", "relation_table", "column1", "column2", "depends", "field_description", "help", "compute", "copied", "required", "readonly", "index", "translate", "group_expand", "selectable", "store", "create_date", "write_date", "tracking" ] }, { "table_name": "res_partner", "fields": [ "id", "company_id", "create_date", "name", "title", "parent_id", "user_id", "state_id", "country_id", "industry_id", "color", "commercial_partner_id", "create_uid", "write_uid", "display_name", "ref", "lang", "tz", "vat", "company_registry", "website", "function", "type", "street", "street2", "zip", "city", "email", "phone", "mobile", "commercial_company_name", "company_name", "date", "comment", "partner_latitude", "partner_longitude", "active", "employee", "is_company", "partner_share", "write_date", "fax", "ice", "patente_code", "cin", "contact_address_complete", "message_main_attachment_id", "message_bounce", "email_normalized", "signup_token", "signup_type", "signup_expiration", "calendar_last_notif_ack", "team_id", "ocn_token", "partner_gid", "additional_info", "phone_sanitized", "supplier_rank", "customer_rank", "invoice_warn", "invoice_warn_msg", "debit_limit", "last_time_entries_checked", "sale_warn", "sale_warn_msg" ] }, { "table_name": "res_users", "fields": [ "id", "company_id", "partner_id", "active", "create_date", "login", "password", "action_id", "create_uid", "write_uid", "signature", "share", "write_date", "totp_secret", "notification_type", "odoobot_state", "odoobot_failed", "sale_team_id", "target_sales_won", "target_sales_done", "target_sales_invoiced" ] }, { "table_name": "fine_tune_module", "fields": [ "id", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "data_frame_model_model_rel", "fields": [ "data_frame_id", "model_model_id" ] }, { "table_name": "resource_calendar", "fields": [ "id", "company_id", "create_uid", "write_uid", "name", "tz", "active", "two_weeks_calendar", "create_date", "write_date", "hours_per_day" ] }, { "table_name": "resource_calendar_attendance", "fields": [ "id", "calendar_id", "resource_id", "sequence", "create_uid", "write_uid", "name", "dayofweek", "day_period", "week_type", "display_type", "date_from", "date_to", "create_date", "write_date", "hour_from", "hour_to" ] }, { "table_name": "resource_resource", "fields": [ "id", "company_id", "user_id", "calendar_id", "create_uid", "write_uid", "name", "resource_type", "tz", "active", "create_date", "write_date", "time_efficiency" ] }, { "table_name": "resource_calendar_leaves", "fields": [ "id", "company_id", "calendar_id", "resource_id", "create_uid", "write_uid", "name", "time_type", "date_from", "date_to", "create_date", "write_date" ] }, { "table_name": "ir_logging", "fields": [ "id", "create_uid", "write_uid", "name", "type", "dbname", "level", "path", "func", "line", "message", "create_date", "write_date" ] }, { "table_name": "ir_module_category", "fields": [ "id", "create_uid", "create_date", "write_date", "write_uid", "parent_id", "name", "sequence", "description", "visible", "exclusive" ] }, { "table_name": "ir_module_module", "fields": [ "id", "create_uid", "create_date", "write_date", "write_uid", "website", "summary", "name", "author", "icon", "state", "latest_version", "shortdesc", "category_id", "description", "application", "demo", "web", "license", "sequence", "auto_install", "to_buy", "maintainer", "published_version", "url", "contributors", "menus_by_module", "reports_by_module", "views_by_module" ] }, { "table_name": "ir_profile", "fields": [ "id", "sql_count", "entry_count", "session", "name", "init_stack_trace", "sql", "traces_async", "traces_sync", "qweb", "create_date", "duration" ] }, { "table_name": "utm_campaign", "fields": [ "id", "user_id", "stage_id", "color", "create_uid", "write_uid", "name", "title", "is_auto_campaign", "create_date", "write_date", "company_id" ] }, { "table_name": "utm_tag_rel", "fields": [ "tag_id", "campaign_id" ] }, { "table_name": "utm_tag", "fields": [ "id", "color", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "utm_stage", "fields": [ "id", "sequence", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "utm_medium", "fields": [ "id", "create_uid", "write_uid", "name", "active", "create_date", "write_date" ] }, { "table_name": "utm_source", "fields": [ "id", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "res_users_apikeys", "fields": [ "id", "name", "user_id", "scope", "index", "key", "create_date" ] }, { "table_name": "ir_model_fields_group_rel", "fields": [ "field_id", "group_id" ] }, { "table_name": "res_groups", "fields": [ "id", "name", "category_id", "color", "create_uid", "write_uid", "comment", "share", "create_date", "write_date" ] }, { "table_name": "ir_model_fields_selection", "fields": [ "id", "field_id", "sequence", "create_uid", "write_uid", "value", "name", "create_date", "write_date" ] }, { "table_name": "ir_model_constraint", "fields": [ "id", "model", "module", "create_uid", "write_uid", "name", "definition", "type", "message", "write_date", "create_date" ] }, { "table_name": "ir_model_relation", "fields": [ "id", "model", "module", "create_uid", "write_uid", "name", "write_date", "create_date" ] }, { "table_name": "ir_model_access", "fields": [ "id", "model_id", "group_id", "create_uid", "write_uid", "name", "active", "perm_read", "perm_write", "perm_create", "perm_unlink", "create_date", "write_date" ] }, { "table_name": "ir_model_data", "fields": [ "id", "create_uid", "create_date", "write_date", "write_uid", "res_id", "noupdate", "name", "module", "model" ] }, { "table_name": "ir_ui_menu", "fields": [ "id", "sequence", "parent_id", "create_uid", "write_uid", "parent_path", "web_icon", "action", "name", "active", "create_date", "write_date" ] }, { "table_name": "wizard_ir_model_menu_create", "fields": [ "id", "menu_id", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "ir_sequence", "fields": [ "id", "number_next", "number_increment", "padding", "company_id", "create_uid", "write_uid", "name", "code", "implementation", "prefix", "suffix", "active", "use_date_range", "create_date", "write_date" ] }, { "table_name": "ir_sequence_date_range", "fields": [ "id", "sequence_id", "number_next", "create_uid", "write_uid", "date_from", "date_to", "create_date", "write_date" ] }, { "table_name": "ir_ui_menu_group_rel", "fields": [ "menu_id", "gid" ] }, { "table_name": "ir_ui_view_custom", "fields": [ "id", "ref_id", "user_id", "create_uid", "write_uid", "arch", "create_date", "write_date" ] }, { "table_name": "ir_ui_view_group_rel", "fields": [ "view_id", "group_id" ] }, { "table_name": "reset_view_arch_wizard", "fields": [ "id", "view_id", "compare_view_id", "create_uid", "write_uid", "reset_mode", "create_date", "write_date" ] }, { "table_name": "ir_asset", "fields": [ "id", "sequence", "create_uid", "write_uid", "name", "bundle", "directive", "path", "target", "active", "create_date", "write_date" ] }, { "table_name": "ir_actions", "fields": [ "id", "binding_model_id", "create_uid", "write_uid", "type", "binding_type", "binding_view_types", "name", "help", "create_date", "write_date" ] }, { "table_name": "ir_act_window", "fields": [ "id", "binding_model_id", "create_uid", "write_uid", "type", "binding_type", "binding_view_types", "name", "help", "create_date", "write_date", "view_id", "res_id", "limit", "search_view_id", "domain", "context", "res_model", "target", "view_mode", "usage", "filter" ] }, { "table_name": "ir_act_window_group_rel", "fields": [ "act_id", "gid" ] }, { "table_name": "ir_act_window_view", "fields": [ "id", "sequence", "view_id", "act_window_id", "create_uid", "write_uid", "view_mode", "multi", "create_date", "write_date" ] }, { "table_name": "ir_ui_view", "fields": [ "id", "priority", "inherit_id", "create_uid", "write_uid", "name", "model", "key", "type", "arch_fs", "field_parent", "mode", "arch_db", "arch_prev", "arch_updated", "active", "create_date", "write_date", "customize_show" ] }, { "table_name": "ir_act_url", "fields": [ "id", "binding_model_id", "create_uid", "write_uid", "type", "binding_type", "binding_view_types", "name", "help", "create_date", "write_date", "target", "url" ] }, { "table_name": "res_partner_bank", "fields": [ "id", "partner_id", "bank_id", "sequence", "currency_id", "company_id", "create_uid", "write_uid", "acc_number", "sanitized_acc_number", "acc_holder_name", "active", "allow_out_payment", "create_date", "write_date", "message_main_attachment_id", "aba_routing" ] }, { "table_name": "rel_server_actions", "fields": [ "server_id", "action_id" ] }, { "table_name": "ir_act_server_group_rel", "fields": [ "act_id", "gid" ] }, { "table_name": "ir_server_object_lines", "fields": [ "id", "server_id", "col1", "create_uid", "write_uid", "evaluation_type", "value", "create_date", "write_date" ] }, { "table_name": "ir_actions_todo", "fields": [ "id", "action_id", "sequence", "create_uid", "write_uid", "state", "name", "create_date", "write_date" ] }, { "table_name": "ir_act_client", "fields": [ "id", "binding_model_id", "create_uid", "write_uid", "type", "binding_type", "binding_view_types", "name", "help", "create_date", "write_date", "tag", "target", "res_model", "context", "params_store" ] }, { "table_name": "ir_act_report_xml", "fields": [ "id", "binding_model_id", "create_uid", "write_uid", "type", "binding_type", "binding_view_types", "name", "help", "create_date", "write_date", "paperformat_id", "model", "report_type", "report_name", "report_file", "attachment", "print_report_name", "multi", "attachment_use" ] }, { "table_name": "res_groups_report_rel", "fields": [ "uid", "gid" ] }, { "table_name": "report_paperformat", "fields": [ "id", "page_height", "page_width", "header_spacing", "dpi", "create_uid", "write_uid", "name", "format", "orientation", "default", "header_line", "disable_shrinking", "create_date", "write_date", "margin_top", "margin_bottom", "margin_left", "margin_right" ] }, { "table_name": "ir_cron", "fields": [ "id", "ir_actions_server_id", "user_id", "interval_number", "numbercall", "priority", "create_uid", "write_uid", "interval_type", "cron_name", "active", "doall", "nextcall", "lastcall", "create_date", "write_date" ] }, { "table_name": "ir_cron_trigger", "fields": [ "id", "cron_id", "create_uid", "write_uid", "call_at", "create_date", "write_date" ] }, { "table_name": "ir_filters", "fields": [ "id", "user_id", "action_id", "create_uid", "write_uid", "name", "model_id", "domain", "context", "sort", "is_default", "active", "create_date", "write_date" ] }, { "table_name": "ir_default", "fields": [ "id", "field_id", "user_id", "company_id", "create_uid", "write_uid", "condition", "json_value", "create_date", "write_date" ] }, { "table_name": "ir_exports", "fields": [ "id", "create_uid", "write_uid", "name", "resource", "create_date", "write_date" ] }, { "table_name": "ir_exports_line", "fields": [ "id", "export_id", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "ir_rule", "fields": [ "id", "model_id", "create_uid", "write_uid", "name", "domain_force", "active", "perm_read", "perm_write", "perm_create", "perm_unlink", "global", "create_date", "write_date" ] }, { "table_name": "rule_group_rel", "fields": [ "rule_group_id", "group_id" ] }, { "table_name": "ir_config_parameter", "fields": [ "id", "create_uid", "write_uid", "key", "value", "create_date", "write_date" ] }, { "table_name": "ir_property", "fields": [ "id", "company_id", "fields_id", "value_integer", "create_uid", "write_uid", "name", "res_id", "value_reference", "type", "value_text", "value_datetime", "create_date", "write_date", "value_float", "value_binary" ] }, { "table_name": "ir_module_module_exclusion", "fields": [ "id", "module_id", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "ir_demo", "fields": [ "id", "create_uid", "write_uid", "create_date", "write_date" ] }, { "table_name": "ir_demo_failure", "fields": [ "id", "module_id", "wizard_id", "create_uid", "write_uid", "error", "create_date", "write_date" ] }, { "table_name": "ir_demo_failure_wizard", "fields": [ "id", "create_uid", "write_uid", "create_date", "write_date" ] }, { "table_name": "report_layout", "fields": [ "id", "view_id", "sequence", "create_uid", "write_uid", "image", "pdf", "name", "create_date", "write_date" ] }, { "table_name": "base_enable_profiling_wizard", "fields": [ "id", "create_uid", "write_uid", "duration", "expiration", "create_date", "write_date" ] }, { "table_name": "res_country", "fields": [ "id", "address_view_id", "currency_id", "phone_code", "create_uid", "write_uid", "code", "name_position", "name", "vat_label", "address_format", "state_required", "zip_required", "create_date", "write_date" ] }, { "table_name": "res_country_res_country_group_rel", "fields": [ "res_country_id", "res_country_group_id" ] }, { "table_name": "res_country_group", "fields": [ "id", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "res_currency", "fields": [ "id", "name", "symbol", "decimal_places", "create_uid", "write_uid", "full_name", "position", "currency_unit_label", "currency_subunit_label", "rounding", "active", "create_date", "write_date" ] }, { "table_name": "res_country_state", "fields": [ "id", "country_id", "create_uid", "write_uid", "name", "code", "create_date", "write_date" ] }, { "table_name": "res_lang", "fields": [ "id", "create_uid", "write_uid", "name", "code", "iso_code", "url_code", "direction", "date_format", "time_format", "week_start", "grouping", "decimal_point", "thousands_sep", "active", "create_date", "write_date" ] }, { "table_name": "res_partner_category", "fields": [ "id", "color", "parent_id", "create_uid", "write_uid", "parent_path", "name", "active", "create_date", "write_date" ] }, { "table_name": "res_partner_res_partner_category_rel", "fields": [ "category_id", "partner_id" ] }, { "table_name": "res_partner_title", "fields": [ "id", "create_uid", "write_uid", "name", "shortcut", "create_date", "write_date" ] }, { "table_name": "res_partner_industry", "fields": [ "id", "create_uid", "write_uid", "name", "full_name", "active", "create_date", "write_date" ] }, { "table_name": "res_bank", "fields": [ "id", "state", "country", "create_uid", "write_uid", "name", "street", "street2", "zip", "city", "email", "phone", "bic", "active", "create_date", "write_date" ] }, { "table_name": "res_config", "fields": [ "id", "create_uid", "write_uid", "create_date", "write_date" ] }, { "table_name": "res_config_installer", "fields": [ "id", "create_uid", "write_uid", "create_date", "write_date" ] }, { "table_name": "res_currency_rate", "fields": [ "id", "currency_id", "company_id", "create_uid", "write_uid", "name", "rate", "create_date", "write_date" ] }, { "table_name": "res_company_users_rel", "fields": [ "cid", "user_id" ] }, { "table_name": "res_groups_users_rel", "fields": [ "gid", "uid" ] }, { "table_name": "res_groups_implied_rel", "fields": [ "gid", "hid" ] }, { "table_name": "res_users_log", "fields": [ "id", "create_uid", "write_uid", "create_date", "write_date" ] }, { "table_name": "res_users_identitycheck", "fields": [ "id", "create_uid", "write_uid", "request", "password", "create_date", "write_date" ] }, { "table_name": "crm_convert_lead_mass_lead_rel", "fields": [ "crm_lead2opportunity_partner_mass_id", "crm_lead_id" ] }, { "table_name": "crm_lead2opportunity_partner_mass_res_users_rel", "fields": [ "crm_lead2opportunity_partner_mass_id", "res_users_id" ] }, { "table_name": "crm_merge_opportunity", "fields": [ "id", "user_id", "team_id", "create_uid", "write_uid", "create_date", "write_date" ] }, { "table_name": "merge_opportunity_rel", "fields": [ "merge_id", "opportunity_id" ] }, { "table_name": "crm_lead_pls_update", "fields": [ "id", "create_uid", "write_uid", "pls_start_date", "create_date", "write_date" ] }, { "table_name": "crm_lead_pls_update_crm_lead_scoring_frequency_field_rel", "fields": [ "crm_lead_pls_update_id", "crm_lead_scoring_frequency_field_id" ] }, { "table_name": "crm_activity_report", "fields": [ "id", "lead_create_date", "date_conversion", "date_deadline", "date_closed", "subtype_id", "mail_activity_type_id", "author_id", "date", "body", "lead_id", "user_id", "team_id", "country_id", "company_id", "stage_id", "partner_id", "lead_type", "active", "won_status" ] }, { "table_name": "crm_lead", "fields": [ "id", "campaign_id", "source_id", "medium_id", "message_main_attachment_id", "message_bounce", "user_id", "team_id", "company_id", "stage_id", "color", "recurring_plan", "partner_id", "title", "lang_id", "state_id", "country_id", "lost_reason_id", "create_uid", "write_uid", "phone_sanitized", "email_normalized", "email_cc", "name", "referred", "type", "priority", "contact_name", "partner_name", "function", "email_from", "phone", "mobile", "phone_state", "email_state", "website", "street", "street2", "zip", "city", "date_deadline", "lead_properties", "description", "expected_revenue", "prorated_revenue", "recurring_revenue", "recurring_revenue_monthly", "recurring_revenue_monthly_prorated", "active", "date_closed", "date_action_last", "date_open", "date_last_stage_update", "date_conversion", "create_date", "write_date", "day_open", "day_close", "probability", "automated_probability", "won_status", "days_to_convert", "days_exceeding_closing", "reveal_id", "iap_enrich_done", "lead_mining_request_id" ] }, { "table_name": "crm_iap_lead_mining_request", "fields": [ "id", "lead_number", "team_id", "user_id", "company_size_min", "company_size_max", "contact_number", "preferred_role_id", "seniority_id", "create_uid", "write_uid", "name", "state", "search_type", "error_type", "lead_type", "contact_filter_type", "filter_on_size", "create_date", "write_date" ] }, { "table_name": "crm_iap_lead_helpers", "fields": [ "id", "create_uid", "write_uid", "create_date", "write_date" ] }, { "table_name": "crm_iap_lead_industry", "fields": [ "id", "color", "sequence", "create_uid", "write_uid", "reveal_ids", "name", "create_date", "write_date" ] }, { "table_name": "crm_iap_lead_role", "fields": [ "id", "color", "create_uid", "write_uid", "reveal_id", "name", "create_date", "write_date" ] }, { "table_name": "crm_iap_lead_seniority", "fields": [ "id", "create_uid", "write_uid", "reveal_id", "name", "create_date", "write_date" ] }, { "table_name": "crm_iap_lead_mining_request_crm_tag_rel", "fields": [ "crm_iap_lead_mining_request_id", "crm_tag_id" ] }, { "table_name": "crm_iap_lead_mining_request_res_country_rel", "fields": [ "crm_iap_lead_mining_request_id", "res_country_id" ] }, { "table_name": "crm_iap_lead_mining_request_res_country_state_rel", "fields": [ "crm_iap_lead_mining_request_id", "res_country_state_id" ] }, { "table_name": "crm_iap_lead_industry_crm_iap_lead_mining_request_rel", "fields": [ "crm_iap_lead_mining_request_id", "crm_iap_lead_industry_id" ] }, { "table_name": "crm_iap_lead_mining_request_crm_iap_lead_role_rel", "fields": [ "crm_iap_lead_mining_request_id", "crm_iap_lead_role_id" ] }, { "table_name": "uom_category", "fields": [ "id", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "uom_uom", "fields": [ "id", "category_id", "create_uid", "write_uid", "uom_type", "name", "factor", "rounding", "active", "create_date", "write_date" ] }, { "table_name": "account_analytic_plan", "fields": [ "id", "parent_id", "company_id", "color", "create_uid", "write_uid", "name", "parent_path", "complete_name", "default_applicability", "description", "create_date", "write_date" ] }, { "table_name": "account_analytic_account", "fields": [ "id", "message_main_attachment_id", "plan_id", "root_plan_id", "company_id", "partner_id", "create_uid", "write_uid", "name", "code", "active", "create_date", "write_date" ] }, { "table_name": "account_analytic_distribution_model", "fields": [ "id", "partner_id", "partner_category_id", "company_id", "create_uid", "write_uid", "analytic_distribution", "create_date", "write_date", "product_id", "product_categ_id", "account_prefix" ] }, </code></pre> <p>i can’t upload all the database due the characters limits</p>

aea2b2b31c

hi [@jr.2509](/u/jr.2509) , i appreciate your effort to answer my questions, to put you more in context I’m working on the odoo database ``` [ { "table_name": "product_tic_category", "fields": [ "id", "code", "create_uid", "write_uid", "description", "create_date", "write_date" ] }, { "table_name": "ir_module_module_dependency", "fields": [ "id", "name", "module_id", "auto_install_required" ] }, { "table_name": "data_frame", "fields": [ "id", "create_uid", "write_uid", "quest_text", "sql_query", "create_date", "write_date", "fine_tune_module_id" ] }, { "table_name": "model_model", "fields": [ "id", "create_uid", "write_uid", "name", "create_date", "write_date", "fine_tune_module_id" ] }, { "table_name": "ir_model_fields", "fields": [ "id", "relation_field_id", "model_id", "related_field_id", "size", "create_uid", "write_uid", "name", "complete_name", "model", "relation", "relation_field", "ttype", "related", "state", "on_delete", "domain", "relation_table", "column1", "column2", "depends", "field_description", "help", "compute", "copied", "required", "readonly", "index", "translate", "group_expand", "selectable", "store", "create_date", "write_date", "tracking" ] }, { "table_name": "res_partner", "fields": [ "id", "company_id", "create_date", "name", "title", "parent_id", "user_id", "state_id", "country_id", "industry_id", "color", "commercial_partner_id", "create_uid", "write_uid", "display_name", "ref", "lang", "tz", "vat", "company_registry", "website", "function", "type", "street", "street2", "zip", "city", "email", "phone", "mobile", "commercial_company_name", "company_name", "date", "comment", "partner_latitude", "partner_longitude", "active", "employee", "is_company", "partner_share", "write_date", "fax", "ice", "patente_code", "cin", "contact_address_complete", "message_main_attachment_id", "message_bounce", "email_normalized", "signup_token", "signup_type", "signup_expiration", "calendar_last_notif_ack", "team_id", "ocn_token", "partner_gid", "additional_info", "phone_sanitized", "supplier_rank", "customer_rank", "invoice_warn", "invoice_warn_msg", "debit_limit", "last_time_entries_checked", "sale_warn", "sale_warn_msg" ] }, { "table_name": "res_users", "fields": [ "id", "company_id", "partner_id", "active", "create_date", "login", "password", "action_id", "create_uid", "write_uid", "signature", "share", "write_date", "totp_secret", "notification_type", "odoobot_state", "odoobot_failed", "sale_team_id", "target_sales_won", "target_sales_done", "target_sales_invoiced" ] }, { "table_name": "fine_tune_module", "fields": [ "id", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "data_frame_model_model_rel", "fields": [ "data_frame_id", "model_model_id" ] }, { "table_name": "resource_calendar", "fields": [ "id", "company_id", "create_uid", "write_uid", "name", "tz", "active", "two_weeks_calendar", "create_date", "write_date", "hours_per_day" ] }, { "table_name": "resource_calendar_attendance", "fields": [ "id", "calendar_id", "resource_id", "sequence", "create_uid", "write_uid", "name", "dayofweek", "day_period", "week_type", "display_type", "date_from", "date_to", "create_date", "write_date", "hour_from", "hour_to" ] }, { "table_name": "resource_resource", "fields": [ "id", "company_id", "user_id", "calendar_id", "create_uid", "write_uid", "name", "resource_type", "tz", "active", "create_date", "write_date", "time_efficiency" ] }, { "table_name": "resource_calendar_leaves", "fields": [ "id", "company_id", "calendar_id", "resource_id", "create_uid", "write_uid", "name", "time_type", "date_from", "date_to", "create_date", "write_date" ] }, { "table_name": "ir_logging", "fields": [ "id", "create_uid", "write_uid", "name", "type", "dbname", "level", "path", "func", "line", "message", "create_date", "write_date" ] }, { "table_name": "ir_module_category", "fields": [ "id", "create_uid", "create_date", "write_date", "write_uid", "parent_id", "name", "sequence", "description", "visible", "exclusive" ] }, { "table_name": "ir_module_module", "fields": [ "id", "create_uid", "create_date", "write_date", "write_uid", "website", "summary", "name", "author", "icon", "state", "latest_version", "shortdesc", "category_id", "description", "application", "demo", "web", "license", "sequence", "auto_install", "to_buy", "maintainer", "published_version", "url", "contributors", "menus_by_module", "reports_by_module", "views_by_module" ] }, { "table_name": "ir_profile", "fields": [ "id", "sql_count", "entry_count", "session", "name", "init_stack_trace", "sql", "traces_async", "traces_sync", "qweb", "create_date", "duration" ] }, { "table_name": "utm_campaign", "fields": [ "id", "user_id", "stage_id", "color", "create_uid", "write_uid", "name", "title", "is_auto_campaign", "create_date", "write_date", "company_id" ] }, { "table_name": "utm_tag_rel", "fields": [ "tag_id", "campaign_id" ] }, { "table_name": "utm_tag", "fields": [ "id", "color", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "utm_stage", "fields": [ "id", "sequence", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "utm_medium", "fields": [ "id", "create_uid", "write_uid", "name", "active", "create_date", "write_date" ] }, { "table_name": "utm_source", "fields": [ "id", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "res_users_apikeys", "fields": [ "id", "name", "user_id", "scope", "index", "key", "create_date" ] }, { "table_name": "ir_model_fields_group_rel", "fields": [ "field_id", "group_id" ] }, { "table_name": "res_groups", "fields": [ "id", "name", "category_id", "color", "create_uid", "write_uid", "comment", "share", "create_date", "write_date" ] }, { "table_name": "ir_model_fields_selection", "fields": [ "id", "field_id", "sequence", "create_uid", "write_uid", "value", "name", "create_date", "write_date" ] }, { "table_name": "ir_model_constraint", "fields": [ "id", "model", "module", "create_uid", "write_uid", "name", "definition", "type", "message", "write_date", "create_date" ] }, { "table_name": "ir_model_relation", "fields": [ "id", "model", "module", "create_uid", "write_uid", "name", "write_date", "create_date" ] }, { "table_name": "ir_model_access", "fields": [ "id", "model_id", "group_id", "create_uid", "write_uid", "name", "active", "perm_read", "perm_write", "perm_create", "perm_unlink", "create_date", "write_date" ] }, { "table_name": "ir_model_data", "fields": [ "id", "create_uid", "create_date", "write_date", "write_uid", "res_id", "noupdate", "name", "module", "model" ] }, { "table_name": "ir_ui_menu", "fields": [ "id", "sequence", "parent_id", "create_uid", "write_uid", "parent_path", "web_icon", "action", "name", "active", "create_date", "write_date" ] }, { "table_name": "wizard_ir_model_menu_create", "fields": [ "id", "menu_id", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "ir_sequence", "fields": [ "id", "number_next", "number_increment", "padding", "company_id", "create_uid", "write_uid", "name", "code", "implementation", "prefix", "suffix", "active", "use_date_range", "create_date", "write_date" ] }, { "table_name": "ir_sequence_date_range", "fields": [ "id", "sequence_id", "number_next", "create_uid", "write_uid", "date_from", "date_to", "create_date", "write_date" ] }, { "table_name": "ir_ui_menu_group_rel", "fields": [ "menu_id", "gid" ] }, { "table_name": "ir_ui_view_custom", "fields": [ "id", "ref_id", "user_id", "create_uid", "write_uid", "arch", "create_date", "write_date" ] }, { "table_name": "ir_ui_view_group_rel", "fields": [ "view_id", "group_id" ] }, { "table_name": "reset_view_arch_wizard", "fields": [ "id", "view_id", "compare_view_id", "create_uid", "write_uid", "reset_mode", "create_date", "write_date" ] }, { "table_name": "ir_asset", "fields": [ "id", "sequence", "create_uid", "write_uid", "name", "bundle", "directive", "path", "target", "active", "create_date", "write_date" ] }, { "table_name": "ir_actions", "fields": [ "id", "binding_model_id", "create_uid", "write_uid", "type", "binding_type", "binding_view_types", "name", "help", "create_date", "write_date" ] }, { "table_name": "ir_act_window", "fields": [ "id", "binding_model_id", "create_uid", "write_uid", "type", "binding_type", "binding_view_types", "name", "help", "create_date", "write_date", "view_id", "res_id", "limit", "search_view_id", "domain", "context", "res_model", "target", "view_mode", "usage", "filter" ] }, { "table_name": "ir_act_window_group_rel", "fields": [ "act_id", "gid" ] }, { "table_name": "ir_act_window_view", "fields": [ "id", "sequence", "view_id", "act_window_id", "create_uid", "write_uid", "view_mode", "multi", "create_date", "write_date" ] }, { "table_name": "ir_ui_view", "fields": [ "id", "priority", "inherit_id", "create_uid", "write_uid", "name", "model", "key", "type", "arch_fs", "field_parent", "mode", "arch_db", "arch_prev", "arch_updated", "active", "create_date", "write_date", "customize_show" ] }, { "table_name": "ir_act_url", "fields": [ "id", "binding_model_id", "create_uid", "write_uid", "type", "binding_type", "binding_view_types", "name", "help", "create_date", "write_date", "target", "url" ] }, { "table_name": "res_partner_bank", "fields": [ "id", "partner_id", "bank_id", "sequence", "currency_id", "company_id", "create_uid", "write_uid", "acc_number", "sanitized_acc_number", "acc_holder_name", "active", "allow_out_payment", "create_date", "write_date", "message_main_attachment_id", "aba_routing" ] }, { "table_name": "rel_server_actions", "fields": [ "server_id", "action_id" ] }, { "table_name": "ir_act_server_group_rel", "fields": [ "act_id", "gid" ] }, { "table_name": "ir_server_object_lines", "fields": [ "id", "server_id", "col1", "create_uid", "write_uid", "evaluation_type", "value", "create_date", "write_date" ] }, { "table_name": "ir_actions_todo", "fields": [ "id", "action_id", "sequence", "create_uid", "write_uid", "state", "name", "create_date", "write_date" ] }, { "table_name": "ir_act_client", "fields": [ "id", "binding_model_id", "create_uid", "write_uid", "type", "binding_type", "binding_view_types", "name", "help", "create_date", "write_date", "tag", "target", "res_model", "context", "params_store" ] }, { "table_name": "ir_act_report_xml", "fields": [ "id", "binding_model_id", "create_uid", "write_uid", "type", "binding_type", "binding_view_types", "name", "help", "create_date", "write_date", "paperformat_id", "model", "report_type", "report_name", "report_file", "attachment", "print_report_name", "multi", "attachment_use" ] }, { "table_name": "res_groups_report_rel", "fields": [ "uid", "gid" ] }, { "table_name": "report_paperformat", "fields": [ "id", "page_height", "page_width", "header_spacing", "dpi", "create_uid", "write_uid", "name", "format", "orientation", "default", "header_line", "disable_shrinking", "create_date", "write_date", "margin_top", "margin_bottom", "margin_left", "margin_right" ] }, { "table_name": "ir_cron", "fields": [ "id", "ir_actions_server_id", "user_id", "interval_number", "numbercall", "priority", "create_uid", "write_uid", "interval_type", "cron_name", "active", "doall", "nextcall", "lastcall", "create_date", "write_date" ] }, { "table_name": "ir_cron_trigger", "fields": [ "id", "cron_id", "create_uid", "write_uid", "call_at", "create_date", "write_date" ] }, { "table_name": "ir_filters", "fields": [ "id", "user_id", "action_id", "create_uid", "write_uid", "name", "model_id", "domain", "context", "sort", "is_default", "active", "create_date", "write_date" ] }, { "table_name": "ir_default", "fields": [ "id", "field_id", "user_id", "company_id", "create_uid", "write_uid", "condition", "json_value", "create_date", "write_date" ] }, { "table_name": "ir_exports", "fields": [ "id", "create_uid", "write_uid", "name", "resource", "create_date", "write_date" ] }, { "table_name": "ir_exports_line", "fields": [ "id", "export_id", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "ir_rule", "fields": [ "id", "model_id", "create_uid", "write_uid", "name", "domain_force", "active", "perm_read", "perm_write", "perm_create", "perm_unlink", "global", "create_date", "write_date" ] }, { "table_name": "rule_group_rel", "fields": [ "rule_group_id", "group_id" ] }, { "table_name": "ir_config_parameter", "fields": [ "id", "create_uid", "write_uid", "key", "value", "create_date", "write_date" ] }, { "table_name": "ir_property", "fields": [ "id", "company_id", "fields_id", "value_integer", "create_uid", "write_uid", "name", "res_id", "value_reference", "type", "value_text", "value_datetime", "create_date", "write_date", "value_float", "value_binary" ] }, { "table_name": "ir_module_module_exclusion", "fields": [ "id", "module_id", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "ir_demo", "fields": [ "id", "create_uid", "write_uid", "create_date", "write_date" ] }, { "table_name": "ir_demo_failure", "fields": [ "id", "module_id", "wizard_id", "create_uid", "write_uid", "error", "create_date", "write_date" ] }, { "table_name": "ir_demo_failure_wizard", "fields": [ "id", "create_uid", "write_uid", "create_date", "write_date" ] }, { "table_name": "report_layout", "fields": [ "id", "view_id", "sequence", "create_uid", "write_uid", "image", "pdf", "name", "create_date", "write_date" ] }, { "table_name": "base_enable_profiling_wizard", "fields": [ "id", "create_uid", "write_uid", "duration", "expiration", "create_date", "write_date" ] }, { "table_name": "res_country", "fields": [ "id", "address_view_id", "currency_id", "phone_code", "create_uid", "write_uid", "code", "name_position", "name", "vat_label", "address_format", "state_required", "zip_required", "create_date", "write_date" ] }, { "table_name": "res_country_res_country_group_rel", "fields": [ "res_country_id", "res_country_group_id" ] }, { "table_name": "res_country_group", "fields": [ "id", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "res_currency", "fields": [ "id", "name", "symbol", "decimal_places", "create_uid", "write_uid", "full_name", "position", "currency_unit_label", "currency_subunit_label", "rounding", "active", "create_date", "write_date" ] }, { "table_name": "res_country_state", "fields": [ "id", "country_id", "create_uid", "write_uid", "name", "code", "create_date", "write_date" ] }, { "table_name": "res_lang", "fields": [ "id", "create_uid", "write_uid", "name", "code", "iso_code", "url_code", "direction", "date_format", "time_format", "week_start", "grouping", "decimal_point", "thousands_sep", "active", "create_date", "write_date" ] }, { "table_name": "res_partner_category", "fields": [ "id", "color", "parent_id", "create_uid", "write_uid", "parent_path", "name", "active", "create_date", "write_date" ] }, { "table_name": "res_partner_res_partner_category_rel", "fields": [ "category_id", "partner_id" ] }, { "table_name": "res_partner_title", "fields": [ "id", "create_uid", "write_uid", "name", "shortcut", "create_date", "write_date" ] }, { "table_name": "res_partner_industry", "fields": [ "id", "create_uid", "write_uid", "name", "full_name", "active", "create_date", "write_date" ] }, { "table_name": "res_bank", "fields": [ "id", "state", "country", "create_uid", "write_uid", "name", "street", "street2", "zip", "city", "email", "phone", "bic", "active", "create_date", "write_date" ] }, { "table_name": "res_config", "fields": [ "id", "create_uid", "write_uid", "create_date", "write_date" ] }, { "table_name": "res_config_installer", "fields": [ "id", "create_uid", "write_uid", "create_date", "write_date" ] }, { "table_name": "res_currency_rate", "fields": [ "id", "currency_id", "company_id", "create_uid", "write_uid", "name", "rate", "create_date", "write_date" ] }, { "table_name": "res_company_users_rel", "fields": [ "cid", "user_id" ] }, { "table_name": "res_groups_users_rel", "fields": [ "gid", "uid" ] }, { "table_name": "res_groups_implied_rel", "fields": [ "gid", "hid" ] }, { "table_name": "res_users_log", "fields": [ "id", "create_uid", "write_uid", "create_date", "write_date" ] }, { "table_name": "res_users_identitycheck", "fields": [ "id", "create_uid", "write_uid", "request", "password", "create_date", "write_date" ] }, { "table_name": "crm_convert_lead_mass_lead_rel", "fields": [ "crm_lead2opportunity_partner_mass_id", "crm_lead_id" ] }, { "table_name": "crm_lead2opportunity_partner_mass_res_users_rel", "fields": [ "crm_lead2opportunity_partner_mass_id", "res_users_id" ] }, { "table_name": "crm_merge_opportunity", "fields": [ "id", "user_id", "team_id", "create_uid", "write_uid", "create_date", "write_date" ] }, { "table_name": "merge_opportunity_rel", "fields": [ "merge_id", "opportunity_id" ] }, { "table_name": "crm_lead_pls_update", "fields": [ "id", "create_uid", "write_uid", "pls_start_date", "create_date", "write_date" ] }, { "table_name": "crm_lead_pls_update_crm_lead_scoring_frequency_field_rel", "fields": [ "crm_lead_pls_update_id", "crm_lead_scoring_frequency_field_id" ] }, { "table_name": "crm_activity_report", "fields": [ "id", "lead_create_date", "date_conversion", "date_deadline", "date_closed", "subtype_id", "mail_activity_type_id", "author_id", "date", "body", "lead_id", "user_id", "team_id", "country_id", "company_id", "stage_id", "partner_id", "lead_type", "active", "won_status" ] }, { "table_name": "crm_lead", "fields": [ "id", "campaign_id", "source_id", "medium_id", "message_main_attachment_id", "message_bounce", "user_id", "team_id", "company_id", "stage_id", "color", "recurring_plan", "partner_id", "title", "lang_id", "state_id", "country_id", "lost_reason_id", "create_uid", "write_uid", "phone_sanitized", "email_normalized", "email_cc", "name", "referred", "type", "priority", "contact_name", "partner_name", "function", "email_from", "phone", "mobile", "phone_state", "email_state", "website", "street", "street2", "zip", "city", "date_deadline", "lead_properties", "description", "expected_revenue", "prorated_revenue", "recurring_revenue", "recurring_revenue_monthly", "recurring_revenue_monthly_prorated", "active", "date_closed", "date_action_last", "date_open", "date_last_stage_update", "date_conversion", "create_date", "write_date", "day_open", "day_close", "probability", "automated_probability", "won_status", "days_to_convert", "days_exceeding_closing", "reveal_id", "iap_enrich_done", "lead_mining_request_id" ] }, { "table_name": "crm_iap_lead_mining_request", "fields": [ "id", "lead_number", "team_id", "user_id", "company_size_min", "company_size_max", "contact_number", "preferred_role_id", "seniority_id", "create_uid", "write_uid", "name", "state", "search_type", "error_type", "lead_type", "contact_filter_type", "filter_on_size", "create_date", "write_date" ] }, { "table_name": "crm_iap_lead_helpers", "fields": [ "id", "create_uid", "write_uid", "create_date", "write_date" ] }, { "table_name": "crm_iap_lead_industry", "fields": [ "id", "color", "sequence", "create_uid", "write_uid", "reveal_ids", "name", "create_date", "write_date" ] }, { "table_name": "crm_iap_lead_role", "fields": [ "id", "color", "create_uid", "write_uid", "reveal_id", "name", "create_date", "write_date" ] }, { "table_name": "crm_iap_lead_seniority", "fields": [ "id", "create_uid", "write_uid", "reveal_id", "name", "create_date", "write_date" ] }, { "table_name": "crm_iap_lead_mining_request_crm_tag_rel", "fields": [ "crm_iap_lead_mining_request_id", "crm_tag_id" ] }, { "table_name": "crm_iap_lead_mining_request_res_country_rel", "fields": [ "crm_iap_lead_mining_request_id", "res_country_id" ] }, { "table_name": "crm_iap_lead_mining_request_res_country_state_rel", "fields": [ "crm_iap_lead_mining_request_id", "res_country_state_id" ] }, { "table_name": "crm_iap_lead_industry_crm_iap_lead_mining_request_rel", "fields": [ "crm_iap_lead_mining_request_id", "crm_iap_lead_industry_id" ] }, { "table_name": "crm_iap_lead_mining_request_crm_iap_lead_role_rel", "fields": [ "crm_iap_lead_mining_request_id", "crm_iap_lead_role_id" ] }, { "table_name": "uom_category", "fields": [ "id", "create_uid", "write_uid", "name", "create_date", "write_date" ] }, { "table_name": "uom_uom", "fields": [ "id", "category_id", "create_uid", "write_uid", "uom_type", "name", "factor", "rounding", "active", "create_date", "write_date" ] }, { "table_name": "account_analytic_plan", "fields": [ "id", "parent_id", "company_id", "color", "create_uid", "write_uid", "name", "parent_path", "complete_name", "default_applicability", "description", "create_date", "write_date" ] }, { "table_name": "account_analytic_account", "fields": [ "id", "message_main_attachment_id", "plan_id", "root_plan_id", "company_id", "partner_id", "create_uid", "write_uid", "name", "code", "active", "create_date", "write_date" ] }, { "table_name": "account_analytic_distribution_model", "fields": [ "id", "partner_id", "partner_category_id", "company_id", "create_uid", "write_uid", "analytic_distribution", "create_date", "write_date", "product_id", "product_categ_id", "account_prefix" ] }, ``` i can’t upload all the database due the characters limits

api

positive

chatgpt

gpt-4

api

assistants

https://community.openai.com/t/654892

https://community.openai.com/p/894417

Python Programming

Artificial Intelligence

Assistant with text-to-SQL Performance

2024-02-26T14:04:22.949Z

<p>and the instructions that I used are:</p> <pre><code class="lang-auto">```you name is Sale Assistant and you have access to files to answer employee questions about company policies make sure to answer the User about any question related to Sale module You are an SQL and odoo 16.0 expert I have passed a JSON file to you that contains a list of objects. Each object represents a table, and within each object, there are fields associated with that table. Here's a sample structure of the JSON file: [ { "table_name": "product_template", "fields": [ "id", "message_main_attachment_id", "sequence", "categ_id", "uom_id", "uom_po_id", "company_id", "color", ... ] }, { "table_name": "product_category", "fields": [ "id", "parent_id", "create_uid", "write_uid", "name", "complete_name", ... ] }, // ... additional tables and fields ] Please answer the user's question based on the file provided and some of the available table name and fields that provide in the file Constraint: 1.Please understand the user's intention based on the user's question, and use the given table structure definition that in the file to create a grammatically correct sql query . If sql is not required, answer the user's question directly.. 2.make sure for evry question that user provide you generate one sql query 3.Always limit the query to a maximum of 50 results unless the user specifies in the question the specific number of rows of data he wishes to obtain. 4.Please be careful not to mistake the relationship between tables and columns when generating SQL. 5.Please check the correctness of the SQL and ensure that the query performance is optimized under correct conditions. 6. - and make sure to respond with one sql query - select 5 fields at most for each SQL query for example 'SELECT * FROM product_template' should be 'SELECT id, company_id, name, description, type FROM product_template' - please ensure that all dependent fields are in the odoo 17.0 database table - for each SQL query generated, double-check if all fields are current User Question: can you give me all tables in database ? Please think step by step and respond according to the following JSON format:"{"sql": "SQL Query to run"}" Ensure the response is correct json and can be parsed by Python json.loads.``` </code></pre>

aea2b2b31c

and the instructions that I used are: ``` ```you name is Sale Assistant and you have access to files to answer employee questions about company policies make sure to answer the User about any question related to Sale module You are an SQL and odoo 16.0 expert I have passed a JSON file to you that contains a list of objects. Each object represents a table, and within each object, there are fields associated with that table. Here's a sample structure of the JSON file: [ { "table_name": "product_template", "fields": [ "id", "message_main_attachment_id", "sequence", "categ_id", "uom_id", "uom_po_id", "company_id", "color", ... ] }, { "table_name": "product_category", "fields": [ "id", "parent_id", "create_uid", "write_uid", "name", "complete_name", ... ] }, // ... additional tables and fields ] Please answer the user's question based on the file provided and some of the available table name and fields that provide in the file Constraint: 1.Please understand the user's intention based on the user's question, and use the given table structure definition that in the file to create a grammatically correct sql query . If sql is not required, answer the user's question directly.. 2.make sure for evry question that user provide you generate one sql query 3.Always limit the query to a maximum of 50 results unless the user specifies in the question the specific number of rows of data he wishes to obtain. 4.Please be careful not to mistake the relationship between tables and columns when generating SQL. 5.Please check the correctness of the SQL and ensure that the query performance is optimized under correct conditions. 6. - and make sure to respond with one sql query - select 5 fields at most for each SQL query for example 'SELECT * FROM product_template' should be 'SELECT id, company_id, name, description, type FROM product_template' - please ensure that all dependent fields are in the odoo 17.0 database table - for each SQL query generated, double-check if all fields are current User Question: can you give me all tables in database ? Please think step by step and respond according to the following JSON format:"{"sql": "SQL Query to run"}" Ensure the response is correct json and can be parsed by Python json.loads.``` ```

api

neutral

chatgpt

gpt-4

api

assistants

https://community.openai.com/t/654892

https://community.openai.com/p/894418

SQL Database

Embeddings (6)

Assistant with text-to-SQL Performance

2024-02-26T14:06:15.818Z

<p>for example when I asked the assistant the question: “give me all the users in the database” it should give me<code> "SELECT * FROM res_users;"</code><br> but he gives me<code> "SELECT * FROM users;"</code> which is not available in the database.</p> <p>or<br> for example, it gives me this SQL response :<br> <code>"SELECT id, name, login, active FROM res_users LIMIT 50"</code><br> which has the correct tables res_users but res_users doesn’t have the “name” field.</p> <p>thank you in advance</p>

aea2b2b31c

for example when I asked the assistant the question: “give me all the users in the database” it should give me `"SELECT * FROM res_users;"` but he gives me `"SELECT * FROM users;"` which is not available in the database. or for example, it gives me this SQL response : `"SELECT id, name, login, active FROM res_users LIMIT 50"` which has the correct tables res\_users but res\_users doesn’t have the “name” field. thank you in advance

api

neutral

chatgpt

gpt-4

api

assistants

https://community.openai.com/t/654892

https://community.openai.com/p/894421

SQL Database

Embeddings (6)

Assistant with text-to-SQL Performance

2024-02-26T15:18:59.880Z

<p>Hi again -</p> <p>I think there is a lot of complexity here given the sheer number of tables involved and I think the instructions you have provided are not reflective of this complexity, which inevitably leads to incorrect Assistant responses.</p> <p>To <a class="mention" href="/u/somebodysysop">@SomebodySysop</a>’s point, the biggest challenge here will be to create a very clear picture of the landscape of tables and their fields. Even then, this will likely require a multi-step approach in order for the Assistant to generate a valid SQL query.</p> <p>I also still struggle a bit to understand the bigger picture. Is this a sales tool or an internal tool for users to questions about company policies? What’s the scope of questions that users can ask?</p>

5b48a0acbf

Hi again - I think there is a lot of complexity here given the sheer number of tables involved and I think the instructions you have provided are not reflective of this complexity, which inevitably leads to incorrect Assistant responses. To [@SomebodySysop](/u/somebodysysop)’s point, the biggest challenge here will be to create a very clear picture of the landscape of tables and their fields. Even then, this will likely require a multi-step approach in order for the Assistant to generate a valid SQL query. I also still struggle a bit to understand the bigger picture. Is this a sales tool or an internal tool for users to questions about company policies? What’s the scope of questions that users can ask?

api

negative

chatgpt

gpt-4

api

assistants

https://community.openai.com/t/654892

https://community.openai.com/p/894562

SQL Database

Embeddings (6)

Assistant with text-to-SQL Performance

2024-02-26T15:56:08.352Z

<p>Hi <a class="mention" href="/u/jr.2509">@jr.2509</a> ,<br> the Odoo framework has a complex database that manages users who use the platform additionally that odoo is based on modules each module handles a specific domain e.g.(account, sales, CRM, …) my idea is to create an assistant for each module, so I created the Sales assistant based on the tables of sales module,</p> <p>I chose this approach to not upload the entire schema to limit the context of assistant to just Sales<br> tables.</p> <p>and for the question that the client might ask for example :<br> <strong>Can you show me all the sales orders placed by a user_1?</strong><br> The SQL that must be generated is :</p> <pre><code class="lang-auto">SELECT * FROM sale_order WHERE partner_id = 44; </code></pre> <p>so, what are your suggestions?</p> <p>thanks in advance <img src="https://emoji.discourse-cdn.com/twitter/slight_smile.png?v=12" title=":slight_smile:" class="emoji" alt=":slight_smile:" loading="lazy" width="20" height="20"></p>

aea2b2b31c

Hi [@jr.2509](/u/jr.2509) , the Odoo framework has a complex database that manages users who use the platform additionally that odoo is based on modules each module handles a specific domain e.g.(account, sales, CRM, …) my idea is to create an assistant for each module, so I created the Sales assistant based on the tables of sales module, I chose this approach to not upload the entire schema to limit the context of assistant to just Sales tables. and for the question that the client might ask for example : **Can you show me all the sales orders placed by a user\_1?** The SQL that must be generated is : ``` SELECT * FROM sale_order WHERE partner_id = 44; ``` so, what are your suggestions? thanks in advance 

api

neutral

chatgpt

gpt-4

api

assistants

https://community.openai.com/t/654892

https://community.openai.com/p/894643

SQL Database

Embeddings (6)

Assistant with text-to-SQL Performance

2024-02-26T16:23:09.251Z

<p>Well, the starting point would be to create a very detailed overview of your different tables including their relationships.</p> <p>The set of tables you have shared by itself is not self-explanatory and would be difficult for a model to make sense of. You would need a clear and sufficiently specific description of each table along with further definitions of the table fields and - as per above - a clear overview of how tables are and fields are interrelated.<br> Getting this right, is going to be a significant effort.</p> <p>Incoming user requests would initially need to be evaluated to identify which table(s) house the information required to answer the question and based on that the model would then identify the query variables.</p>

5b48a0acbf

Well, the starting point would be to create a very detailed overview of your different tables including their relationships. The set of tables you have shared by itself is not self-explanatory and would be difficult for a model to make sense of. You would need a clear and sufficiently specific description of each table along with further definitions of the table fields and - as per above - a clear overview of how tables are and fields are interrelated. Getting this right, is going to be a significant effort. Incoming user requests would initially need to be evaluated to identify which table(s) house the information required to answer the question and based on that the model would then identify the query variables.

api

neutral

chatgpt

gpt-4

api

assistants

https://community.openai.com/t/654892

https://community.openai.com/p/894691

SQL Database

Embeddings (6)

Assistant with text-to-SQL Performance

2024-02-26T16:27:17.933Z

<p>Thanks a lot for your insights <img src="https://emoji.discourse-cdn.com/twitter/smiley.png?v=12" title=":smiley:" class="emoji" alt=":smiley:" loading="lazy" width="20" height="20">.</p>

aea2b2b31c

Thanks a lot for your insights .

api

positive

chatgpt

gpt-4

api

assistants

https://community.openai.com/t/654892

https://community.openai.com/p/894703

Emoji (7)

Emoji (8)

Assistant with text-to-SQL Performance

2024-02-26T23:16:28.562Z

<p>FWIW, here is a video about strategies for doing text to SQL. Since I can’t paste links, you can search YouTube for the title “Text2SQL: The Dream versus Reality - Laurel Orr | Stanford MLSys <span class="hashtag-raw">#89</span>” in channel “Stanford MLSys Seminars”.</p>

e1bd9aa6d7

FWIW, here is a video about strategies for doing text to SQL. Since I can’t paste links, you can search YouTube for the title “Text2SQL: The Dream versus Reality - Laurel Orr | Stanford MLSys #89” in channel “Stanford MLSys Seminars”.

api

neutral

chatgpt

gpt-4

api

assistants

https://community.openai.com/t/654892

https://community.openai.com/p/895301

SQL Database

Embeddings (6)

Assistant with text-to-SQL Performance

2024-02-27T01:53:41.798Z

<p>Hey y’all I am also trying to make a chatbot similar to this but I am very new to this and I have a vague idea on how to start. <a class="mention" href="/u/rigor08">@rigor08</a> how did you start off with this? My aim is to create a fullstack application with SpringBoot/Java, mySQL and React for frontend so that user can ask questions about soccer players as such. It will be a soccer database and a user can ask questions that get turned into SQL queries and those are executed similar to yours and show that data to the user. For example “Show me the player with the most goals in the last 3 games” is one of the prompts and it would find a sql query accordingly and show that data. Any help, criticism is greatly appreciated !!</p>

25649b1ef0

Hey y’all I am also trying to make a chatbot similar to this but I am very new to this and I have a vague idea on how to start. [@rigor08](/u/rigor08) how did you start off with this? My aim is to create a fullstack application with SpringBoot/Java, mySQL and React for frontend so that user can ask questions about soccer players as such. It will be a soccer database and a user can ask questions that get turned into SQL queries and those are executed similar to yours and show that data to the user. For example “Show me the player with the most goals in the last 3 games” is one of the prompts and it would find a sql query accordingly and show that data. Any help, criticism is greatly appreciated !!

api

neutral

chatgpt

gpt-4

api

assistants

https://community.openai.com/t/654892

https://community.openai.com/p/895588

Miscellaneous (9)

Embeddings (6)

Assistant with text-to-SQL Performance

2024-02-27T02:26:29.367Z

<aside class="quote no-group" data-username="omerk203" data-post="16" data-topic="654892"> <div class="title"> <div class="quote-controls"></div> <img loading="lazy" alt="" width="24" height="24" src="https://sea2.discourse-cdn.com/openai1/user_avatar/community.openai.com/omerk203/48/320972_2.png" class="avatar"> omerk203:</div> <blockquote> <p>Show me the player with the most goals in the last 3 games</p> </blockquote> </aside> <p>This is an easy one.</p> <ul> <li>OpenAI Text to SQL <ul> <li>Prompt Example - Natural Language to SQL</li> <li><a href="https://platform.openai.com/examples/default-sql-translate" rel="noopener nofollow ugc">https://platform.openai.com/examples/default-sql-translate</a></li> </ul> </li> </ul> <p>Start with the above. That’s the basics. Define your schema. But as has been stressed again and again above, you’ve got to do it in a way that clearly explains to the LLM how to respond to the questions it will get.</p> <ul> <li>OpenAI Functions calling the Poor Developers Vector Database <ul> <li><a href="https://blazorhelpwebsite.com/ViewBlogPost/6067" class="inline-onebox" rel="noopener nofollow ugc">OpenAI Functions Calling the Poor Developer's Vector Database</a></li> </ul> </li> <li>Private Chat with CSV Data (<a class="mention" href="/u/devgirl">@DevGirl</a> explanation): <ul> <li><a href="https://community.openai.com/t/private-chat-with-csv-data/620115/7" class="inline-onebox">Private Chat with CSV data - #7 by DevGirl</a></li> </ul> </li> <li>My Prompt Example: <ul> <li><a href="https://community.openai.com/t/how-to-apply-user-based-access-control-check-while-generating-sql-queries-using-openai-api-using-langchain/621409/4" class="inline-onebox">How to apply user based access control check while generating SQL queries using OpenAI API using langchain? - #4 by SomebodySysop</a></li> </ul> </li> </ul> <p>Look at the example from above:</p> <aside class="quote no-group" data-username="rigor08" data-post="12" data-topic="654892"> <div class="title"> <div class="quote-controls"></div> <img loading="lazy" alt="" width="24" height="24" src="https://sea2.discourse-cdn.com/openai1/user_avatar/community.openai.com/rigor08/48/320417_2.png" class="avatar"> rigor08:</div> <blockquote> <p>and for the question that the client might ask for example :<br> <strong>Can you show me all the sales orders placed by a user_1?</strong><br> The SQL that must be generated is :</p> <pre><code class="lang-auto">SELECT * FROM sale_order WHERE partner_id = 44; </code></pre> </blockquote> </aside> <p>Here is a case where, above and beyond the schema, you ALSO explain to the LLM:</p> <ul> <li>Queries with respect to users and sales orders will use the sale_order table</li> <li>The user id in the sales_order table is the partner_id field</li> </ul>

9716394c2d

 omerk203: > > Show me the player with the most goals in the last 3 games > > > This is an easy one. * OpenAI Text to SQL + Prompt Example - Natural Language to SQL + <https://platform.openai.com/examples/default-sql-translate> Start with the above. That’s the basics. Define your schema. But as has been stressed again and again above, you’ve got to do it in a way that clearly explains to the LLM how to respond to the questions it will get. * OpenAI Functions calling the Poor Developers Vector Database + [OpenAI Functions Calling the Poor Developer's Vector Database](https://blazorhelpwebsite.com/ViewBlogPost/6067) * Private Chat with CSV Data ([@DevGirl](/u/devgirl) explanation): + [Private Chat with CSV data - #7 by DevGirl](https://community.openai.com/t/private-chat-with-csv-data/620115/7) * My Prompt Example: + [How to apply user based access control check while generating SQL queries using OpenAI API using langchain? - #4 by SomebodySysop](https://community.openai.com/t/how-to-apply-user-based-access-control-check-while-generating-sql-queries-using-openai-api-using-langchain/621409/4) Look at the example from above:  rigor08: > > and for the question that the client might ask for example : > > **Can you show me all the sales orders placed by a user\_1?** > > The SQL that must be generated is : > > > > ``` > SELECT * FROM sale_order WHERE partner_id = 44; > > ``` > > Here is a case where, above and beyond the schema, you ALSO explain to the LLM: * Queries with respect to users and sales orders will use the sale\_order table * The user id in the sales\_order table is the partner\_id field

api

neutral

chatgpt

gpt-4

api

assistants

https://community.openai.com/t/654892

https://community.openai.com/p/895632

SQL Database

Embeddings (6)

Assistant Thread CreateMessage missing "Name" property

2024-02-27T02:00:52.759Z

<p>Chat completion allows user message to specify <code>Name</code>:</p> <p><a href="https://platform.openai.com/docs/api-reference/chat/create" rel="noopener nofollow ugc">API Reference - OpenAI API</a></p> <p>Assistant API - Create Message does not:</p> <p><a href="https://platform.openai.com/docs/api-reference/messages/createMessage" rel="noopener nofollow ugc">API Reference - OpenAI API</a></p> <p>This simple change would enable multi-user chat as well as other more subtle semantics.</p> <p>No reason this can’t align. <span class="mention">@OpenAI</span> - Please support user name for assistants.</p> <p>(Please upvote this thread by responding.)</p>

80813d35e2

Chat completion allows user message to specify `Name`: [API Reference - OpenAI API](https://platform.openai.com/docs/api-reference/chat/create) Assistant API - Create Message does not: [API Reference - OpenAI API](https://platform.openai.com/docs/api-reference/messages/createMessage) This simple change would enable multi-user chat as well as other more subtle semantics. No reason this can’t align. @OpenAI - Please support user name for assistants. (Please upvote this thread by responding.)

api

neutral

https://community.openai.com/t/656187

https://community.openai.com/p/895596

Chatbot (2)

Miscellaneous (27)

Trying to understand why I'm hitting token limit with API

2024-02-25T04:57:09.222Z

<p>Hi all - I’ve been reading past posts but I can’t quite grasp the answer.</p> <p>Right now I’m sending 22,891 tokens via API and asking for content of around 1900 to 3000 tokens back (around 1500 words +). I cannot get it to give me more than around 600 to 800 words max, and sometimes I just get an error. I assume all of this is because of the large prompts I’m using.</p> <p>I’m experimenting with the gpt-4-turbo-preview model and I thought I’d be able to get a 128k token window, but I also know it says about the 4096 token output…</p> <p>I am on Tier 2 currently.</p> <p>What am I missing? Is there anything I can do to make this work with the size of the tokens I’m sending? I know it’s large but surely it’s nowhere near as large as enterprise type companies are sending?</p> <p>I’d appreciate any help thank you!</p>

33faf25e39

Hi all - I’ve been reading past posts but I can’t quite grasp the answer. Right now I’m sending 22,891 tokens via API and asking for content of around 1900 to 3000 tokens back (around 1500 words +). I cannot get it to give me more than around 600 to 800 words max, and sometimes I just get an error. I assume all of this is because of the large prompts I’m using. I’m experimenting with the gpt-4-turbo-preview model and I thought I’d be able to get a 128k token window, but I also know it says about the 4096 token output… I am on Tier 2 currently. What am I missing? Is there anything I can do to make this work with the size of the tokens I’m sending? I know it’s large but surely it’s nowhere near as large as enterprise type companies are sending? I’d appreciate any help thank you!

api

negative

gpt-4

api

https://community.openai.com/t/653114

https://community.openai.com/p/891505

GPT Model

Tokenization (2)

Trying to understand why I'm hitting token limit with API

2024-02-25T05:04:13.587Z

<p>Hi <a class="mention" href="/u/mattrosine">@mattrosine</a></p> <p>It’d really help us help you if you could share the <code>stop_reason</code> from the response and the API call you’re making with all its hyper-parameters.</p> <p>Also the <code>usage</code> object within the response tells you the exact token count for input and output tokens.</p>

e5f957560a

Hi [@mattrosine](/u/mattrosine) It’d really help us help you if you could share the `stop_reason` from the response and the API call you’re making with all its hyper-parameters. Also the `usage` object within the response tells you the exact token count for input and output tokens.

api

positive

gpt-4

api

https://community.openai.com/t/653114

https://community.openai.com/p/891523

Valorant

Tokenization (2)

Trying to understand why I'm hitting token limit with API

2024-02-25T05:22:46.029Z

<p>is it possible the returned answer is just that long…?</p>

dd14d87a4d

is it possible the returned answer is just that long…?

api

neutral

gpt-4

api

https://community.openai.com/t/653114

https://community.openai.com/p/891546

Miscellaneous (17)

Emoji (8)

Trying to understand why I'm hitting token limit with API

2024-02-25T05:32:03.692Z

<aside class="quote no-group" data-username="mattrosine" data-post="1" data-topic="653114"> <div class="title"> <div class="quote-controls"></div> <img loading="lazy" alt="" width="24" height="24" src="https://avatars.discourse-cdn.com/v4/letter/m/71c47a/48.png" class="avatar"> mattrosine:</div> <blockquote> <p>I cannot get it to give me more than around 600 to 800 words max</p> </blockquote> </aside> <p>The limitation is because the model has intense training, or perhaps even some injected governor, that compels it to stop writing and wrap up its output. This is a preconceived planned notion also. Ask for 40 descriptions, and they will be cut to half the size of 20 descriptions. Make an infallible prompt that reproduces lines of input into processed lines of output, and you will just be cut off arbitrarily.</p> <p>22k tokens of input can be processed (and billed) almost instantly to a hidden state because of the attention masking techniques, but producing the following tokens takes computations, that apparently they don’t want you to even pay for. You don’t get a different model than one now extensively trained to make ChatGPT less expensive for OpenAI.</p>

b368833582

 mattrosine: > > I cannot get it to give me more than around 600 to 800 words max > > > The limitation is because the model has intense training, or perhaps even some injected governor, that compels it to stop writing and wrap up its output. This is a preconceived planned notion also. Ask for 40 descriptions, and they will be cut to half the size of 20 descriptions. Make an infallible prompt that reproduces lines of input into processed lines of output, and you will just be cut off arbitrarily. 22k tokens of input can be processed (and billed) almost instantly to a hidden state because of the attention masking techniques, but producing the following tokens takes computations, that apparently they don’t want you to even pay for. You don’t get a different model than one now extensively trained to make ChatGPT less expensive for OpenAI.

api

negative

gpt-4

api

https://community.openai.com/t/653114

https://community.openai.com/p/891555

Model Completion

Tokenization (2)

Trying to understand why I'm hitting token limit with API

2024-02-25T20:46:08.822Z

<p>Hey, thanks for the reply. Here is the error I get:</p> <blockquote> <p>Workflow error - The service Master OpenAI API - API Call just returned an error (HTTP 400). Please consult their documentation to ensure your call is setup properly. Raw error: { "error": { "message": "This model's maximum context length is 8192 tokens. However, your messages resulted in 22561 tokens. Please reduce the length of the messages.", "type": "invalid_request_error", "param": "messages", "code": "context_length_exceeded" } }</p> </blockquote> <p>I think the max context length of 8192 tokens is from when I was experimenting with one of the other models.</p> <p>Here is the API call:</p> <pre><code class="lang-auto">{ "model": "&lt;model&gt;", "messages": [ {"role": "system", "content": "&lt;system_prompt&gt;"}, {"role": "user", "content": "&lt;user_prompt1&gt;"}, {"role": "assistant", "content": "&lt;assistant_prompt1&gt;"}, {"role": "user", "content": "&lt;user_prompt1a&gt;"}, {"role": "user", "content": "&lt;user_prompt2&gt;"}, {"role": "assistant", "content": "&lt;assistant_prompt2&gt;"}, {"role": "user", "content": "&lt;user_prompt2a&gt;"}, {"role": "user", "content": "&lt;user_prompt3&gt;"}, {"role": "assistant", "content": "&lt;assistant_prompt3&gt;"}, {"role": "user", "content": "&lt;user_prompt3a&gt;"}, {"role": "user", "content": "&lt;user_prompt4&gt;"}, {"role": "assistant", "content": "&lt;assistant_prompt4&gt;"}, {"role": "user", "content": "&lt;user_prompt5&gt;"}, {"role": "assistant", "content": "&lt;assistant_prompt5&gt;"}, {"role": "user", "content": "&lt;user_prompt6&gt;"}, {"role": "assistant", "content": "&lt;assistant_prompt6&gt;"}, {"role": "user", "content": "&lt;prompt&gt;"} ], "temperature": 1.7, "max_tokens": 4096, "top_p": 0.5, "frequency_penalty": 1, "presence_penalty": 0.7 } </code></pre> <p>As I said, the API is working, I’m just not able to get anything more than max 800 words out of it.</p> <p>Does the above help at all?</p>

33faf25e39

Hey, thanks for the reply. Here is the error I get: > > Workflow error - The service Master OpenAI API - API Call just returned an error (HTTP 400). Please consult their documentation to ensure your call is setup properly. Raw error: { "error": { "message": "This model's maximum context length is 8192 tokens. However, your messages resulted in 22561 tokens. Please reduce the length of the messages.", "type": "invalid\_request\_error", "param": "messages", "code": "context\_length\_exceeded" } } > > > I think the max context length of 8192 tokens is from when I was experimenting with one of the other models. Here is the API call: ``` { "model": "<model>", "messages": [ {"role": "system", "content": "<system_prompt>"}, {"role": "user", "content": "<user_prompt1>"}, {"role": "assistant", "content": "<assistant_prompt1>"}, {"role": "user", "content": "<user_prompt1a>"}, {"role": "user", "content": "<user_prompt2>"}, {"role": "assistant", "content": "<assistant_prompt2>"}, {"role": "user", "content": "<user_prompt2a>"}, {"role": "user", "content": "<user_prompt3>"}, {"role": "assistant", "content": "<assistant_prompt3>"}, {"role": "user", "content": "<user_prompt3a>"}, {"role": "user", "content": "<user_prompt4>"}, {"role": "assistant", "content": "<assistant_prompt4>"}, {"role": "user", "content": "<user_prompt5>"}, {"role": "assistant", "content": "<assistant_prompt5>"}, {"role": "user", "content": "<user_prompt6>"}, {"role": "assistant", "content": "<assistant_prompt6>"}, {"role": "user", "content": "<prompt>"} ], "temperature": 1.7, "max_tokens": 4096, "top_p": 0.5, "frequency_penalty": 1, "presence_penalty": 0.7 } ``` As I said, the API is working, I’m just not able to get anything more than max 800 words out of it. Does the above help at all?

api

negative

gpt-4

api

https://community.openai.com/t/653114

https://community.openai.com/p/892803

Model Completion

Tokenization (2)

Trying to understand why I'm hitting token limit with API

2024-02-25T22:40:31.000Z

<p>Oh, that’s interesting. I hadn’t ever considered that the limit was optional… I can remove the line in the API call and I’ll be free of that restriction?</p> <p>When I’m using the Playground though, am I right in thinking the limit (the slider bar on the right) is not something you can remove…?</p>

33faf25e39

Oh, that’s interesting. I hadn’t ever considered that the limit was optional… I can remove the line in the API call and I’ll be free of that restriction? When I’m using the Playground though, am I right in thinking the limit (the slider bar on the right) is not something you can remove…?

api

neutral

gpt-4

api

https://community.openai.com/t/653114

https://community.openai.com/p/892963

Playground

API (2)

Trying to understand why I'm hitting token limit with API

2024-02-25T22:53:49.355Z

<p>I think so.</p> <p>If I remember correctly, you can top up this value to 128,000 in the Playground.</p> <p>It may also be because this model was trained to refuse generating more tokens after 1000. This is not related to the max output it has, but the internal training (this is what _j mentioned).</p>

4180cf6cce

I think so. If I remember correctly, you can top up this value to 128,000 in the Playground. It may also be because this model was trained to refuse generating more tokens after 1000. This is not related to the max output it has, but the internal training (this is what \_j mentioned).

api

neutral

gpt-4

api

https://community.openai.com/t/653114

https://community.openai.com/p/892976

Tokens

Tokenization (2)

Trying to understand why I'm hitting token limit with API

2024-02-26T16:14:03.319Z

<p>You don’t need to set the <code>max_tokens</code> on chat models because they are designed to avail all the remaining context length other than that consumed by input tokens.</p> <p>We really can’t ask the the model to reply in a certain amount of tokens especially if the number is large.</p> <p>I also noticed that you’re using unusually high value for <code>temperature</code>.</p> <p>Apart from that, I find the number of messages quite high given that this is the first API call. Additionally there are non successive messages with the same role - which although isn’t wrong but wasn’t likely the pattern the model was trained on.</p> <p>I’d recommend reducing the number of messages by sending the system prompt followed by a user-assistant grounding pair(if you have any) which is just a one shot example consisting of a user message and expected model response. Then simply add the user message you want the model to process.</p>

e5f957560a

You don’t need to set the `max_tokens` on chat models because they are designed to avail all the remaining context length other than that consumed by input tokens. We really can’t ask the the model to reply in a certain amount of tokens especially if the number is large. I also noticed that you’re using unusually high value for `temperature`. Apart from that, I find the number of messages quite high given that this is the first API call. Additionally there are non successive messages with the same role - which although isn’t wrong but wasn’t likely the pattern the model was trained on. I’d recommend reducing the number of messages by sending the system prompt followed by a user-assistant grounding pair(if you have any) which is just a one shot example consisting of a user message and expected model response. Then simply add the user message you want the model to process.

api

neutral

gpt-4

api

https://community.openai.com/t/653114

https://community.openai.com/p/894678

Booking

Tokenization (2)

Trying to understand why I'm hitting token limit with API

2024-02-27T02:12:36.543Z

<aside class="quote no-group" data-username="sps" data-post="9" data-topic="653114"> <div class="title"> <div class="quote-controls"></div> <img loading="lazy" alt="" width="24" height="24" src="https://sea2.discourse-cdn.com/openai1/user_avatar/community.openai.com/sps/48/7736_2.png" class="avatar"> sps:</div> <blockquote> <p>You don’t need to set the <code>max_tokens</code> on chat models because they are designed to avail all the remaining context length other than that consumed by input tokens.</p> </blockquote> </aside> <p>So if I don’t need to set the ‘max_tokens’, what do I do with that line in the API call? If I remove it, the API breaks. If I set it to be higher than what the max is supposed to be (4096 in this example), it doesn’t work.</p> <aside class="quote no-group" data-username="sps" data-post="9" data-topic="653114"> <div class="title"> <div class="quote-controls"></div> <img loading="lazy" alt="" width="24" height="24" src="https://sea2.discourse-cdn.com/openai1/user_avatar/community.openai.com/sps/48/7736_2.png" class="avatar"> sps:</div> <blockquote> <p>I also noticed that you’re using unusually high value for <code>temperature</code>.</p> </blockquote> </aside> <p>The tool I’m building is a copywriting/text generative platform, and the high temperature value is what I’ve found in my experiments to give the best results for my use case.</p> <aside class="quote no-group" data-username="sps" data-post="9" data-topic="653114"> <div class="title"> <div class="quote-controls"></div> <img loading="lazy" alt="" width="24" height="24" src="https://sea2.discourse-cdn.com/openai1/user_avatar/community.openai.com/sps/48/7736_2.png" class="avatar"> sps:</div> <blockquote> <p>I’d recommend reducing the number of messages by sending the system prompt followed by a user-assistant grounding pair(if you have any) which is just a one shot example consisting of a user message and expected model response. Then simply add the user message you want the model to process.</p> </blockquote> </aside> <p>This wouldn’t allow me to achieve the same results that I’ve been able to achieve in the Playground . My whole strategy relies on few-shot prompting with a long system instruction including several long content examples, then a series of assistant/user prompts which I have written.</p> <p>I think what I struggle to understand, as a relative beginner and someone who is desperately trying to learn, is why would the Playground allow me to mix the different assistant/user prompts if it won’t work in practical use?</p>

33faf25e39

 sps: > > You don’t need to set the `max_tokens` on chat models because they are designed to avail all the remaining context length other than that consumed by input tokens. > > > So if I don’t need to set the ‘max\_tokens’, what do I do with that line in the API call? If I remove it, the API breaks. If I set it to be higher than what the max is supposed to be (4096 in this example), it doesn’t work.  sps: > > I also noticed that you’re using unusually high value for `temperature`. > > > The tool I’m building is a copywriting/text generative platform, and the high temperature value is what I’ve found in my experiments to give the best results for my use case.  sps: > > I’d recommend reducing the number of messages by sending the system prompt followed by a user-assistant grounding pair(if you have any) which is just a one shot example consisting of a user message and expected model response. Then simply add the user message you want the model to process. > > > This wouldn’t allow me to achieve the same results that I’ve been able to achieve in the Playground . My whole strategy relies on few-shot prompting with a long system instruction including several long content examples, then a series of assistant/user prompts which I have written. I think what I struggle to understand, as a relative beginner and someone who is desperately trying to learn, is why would the Playground allow me to mix the different assistant/user prompts if it won’t work in practical use?

api

neutral

gpt-4

api

https://community.openai.com/t/653114

https://community.openai.com/p/895610

Model Completion

Tokenization (2)

Assistant API: Empty message is generated with Code Interpreter tool for no reason

2024-02-27T01:40:28.516Z

<p>Current when working with Assistant API plus code interpreter. There is always an unexpected empty message in the message list when code interpreter tool is called.</p> <p>It is quiet easy to reproduce in the playground as below screenshot. msg_JXx38gqU1DUl8KMxjAlYvCLO is clearly redundant.<br> <div class="lightbox-wrapper"><a class="lightbox" href="https://global.discourse-cdn.com/openai1/original/4X/1/a/d/1adfd4976d92d99ebefb397f399c317ebba88660.jpeg" data-download-href="/uploads/short-url/3PJY3N4CEYbQeAjPkWoJCaokBxK.jpeg?dl=1" title="image" rel="noopener nofollow ugc"><img src="https://global.discourse-cdn.com/openai1/optimized/4X/1/a/d/1adfd4976d92d99ebefb397f399c317ebba88660_2_690x356.jpeg" alt="image" data-base62-sha1="3PJY3N4CEYbQeAjPkWoJCaokBxK" width="690" height="356" srcset="https://global.discourse-cdn.com/openai1/optimized/4X/1/a/d/1adfd4976d92d99ebefb397f399c317ebba88660_2_690x356.jpeg, https://global.discourse-cdn.com/openai1/optimized/4X/1/a/d/1adfd4976d92d99ebefb397f399c317ebba88660_2_1035x534.jpeg 1.5x, https://global.discourse-cdn.com/openai1/optimized/4X/1/a/d/1adfd4976d92d99ebefb397f399c317ebba88660_2_1380x712.jpeg 2x" data-dominant-color="F7F2F3"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">image</span><span class="informations">2434×1256 285 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p> <p><strong>There was no such redundant empty message weeks ago.</strong> I assume it should be a bug because of recent changes?</p>

88e0d30eea

Current when working with Assistant API plus code interpreter. There is always an unexpected empty message in the message list when code interpreter tool is called. It is quiet easy to reproduce in the playground as below screenshot. msg\_JXx38gqU1DUl8KMxjAlYvCLO is clearly redundant. [image2434×1256 285 KB](https://global.discourse-cdn.com/openai1/original/4X/1/a/d/1adfd4976d92d99ebefb397f399c317ebba88660.jpeg "image") **There was no such redundant empty message weeks ago.** I assume it should be a bug because of recent changes?

api/bugs

negative

assistants-api

code-interpreter

https://community.openai.com/t/656170

https://community.openai.com/p/895571

Miscellaneous (6)

Miscellaneous (27)

Assistant API: Empty message is generated with Code Interpreter tool for no reason

2024-02-27T01:44:25.539Z

<p>Hi whung,</p> <p>I think there might me some error behaviour in Assistant API. Not sure if the issue will be solved by when though.</p>

fa3b50a2a8

Hi whung, I think there might me some error behaviour in Assistant API. Not sure if the issue will be solved by when though.

api/bugs

negative

assistants-api

code-interpreter

https://community.openai.com/t/656170

https://community.openai.com/p/895577

Assistants, API, Platform

Embeddings (6)

Assistants API Function Tools

2024-02-24T21:06:15.764Z

<p>Hopefully I haven’t missed something here, but I’m struggling to get my assistant to properly call it’s function. The function should be used whenever the assistant gets an image as part of the message. If I give the assistant just text it works fine, but if I give it an image and text it hallucinates my entire input.<br> some examples of the createMessage functions I’ve tried:<br> V1:</p> <pre><code class="lang-auto">const createMessage = async (threadId, userMessage) =&gt; { console.log('create message triggered') try { if (userMessage.image) { console.log(userMessage.image.split(',')[0]) console.log('create message file:', userMessage.file) await fetch(`https://api.openai.com/v1/threads/${threadId}/messages`, { method: "POST", headers: { "Content-Type": "application/json", 'Authorization': `Bearer APIKEY`, 'OpenAI-Beta': 'assistants=v1', }, body: JSON.stringify({ role: "user", content: userMessage, tool: "analyzeImage" }) }); console.log('User message sent to API contains image:',); } </code></pre> <p>V2:</p> <pre><code class="lang-auto">const createMessage = async (threadId, userMessage) =&gt; { console.log('create message triggered') try { if (userMessage.image) { console.log(userMessage.image.split(',')[0]) console.log('create message file:', userMessage.file) await fetch(`https://api.openai.com/v1/threads/${threadId}/messages`, { method: "POST", headers: { "Content-Type": "application/json", 'Authorization': `Bearer APIKEY`, 'OpenAI-Beta': 'assistants=v1', }, body: JSON.stringify({ role: "user", inputs: { text: userMessage.text, // Pass along any user-entered text data: { file: userMessage.file, image: userMessage.image.split(",")[1] // Split the string and take only the base64 part } }, tool: "analyzeImage" }) }); console.log('User message sent to API contains image:',); } </code></pre> <p>The function tool is called analyzeImage</p> <pre><code class="lang-auto"> "name": "analyzeImage", "parameters": { "type": "object", "properties": { "image": { "type": "string", "contentMediaType": "image/jpeg", "description": "The base64-encoded string of the image." } }, "required": [ "image", ] }, </code></pre> <p>I saw an example from Geoligard where he uses an actual file url, but since this is being done on mobile I was trying to use just the base64 version of the image. Any input as to why I’m getting the hallucinations and it almost never triggers the function would be appreciated.</p>

6f019b1cf4

Hopefully I haven’t missed something here, but I’m struggling to get my assistant to properly call it’s function. The function should be used whenever the assistant gets an image as part of the message. If I give the assistant just text it works fine, but if I give it an image and text it hallucinates my entire input. some examples of the createMessage functions I’ve tried: V1: ``` const createMessage = async (threadId, userMessage) => { console.log('create message triggered') try { if (userMessage.image) { console.log(userMessage.image.split(',')[0]) console.log('create message file:', userMessage.file) await fetch(`https://api.openai.com/v1/threads/${threadId}/messages`, { method: "POST", headers: { "Content-Type": "application/json", 'Authorization': `Bearer APIKEY`, 'OpenAI-Beta': 'assistants=v1', }, body: JSON.stringify({ role: "user", content: userMessage, tool: "analyzeImage" }) }); console.log('User message sent to API contains image:',); } ``` V2: ``` const createMessage = async (threadId, userMessage) => { console.log('create message triggered') try { if (userMessage.image) { console.log(userMessage.image.split(',')[0]) console.log('create message file:', userMessage.file) await fetch(`https://api.openai.com/v1/threads/${threadId}/messages`, { method: "POST", headers: { "Content-Type": "application/json", 'Authorization': `Bearer APIKEY`, 'OpenAI-Beta': 'assistants=v1', }, body: JSON.stringify({ role: "user", inputs: { text: userMessage.text, // Pass along any user-entered text data: { file: userMessage.file, image: userMessage.image.split(",")[1] // Split the string and take only the base64 part } }, tool: "analyzeImage" }) }); console.log('User message sent to API contains image:',); } ``` The function tool is called analyzeImage ``` "name": "analyzeImage", "parameters": { "type": "object", "properties": { "image": { "type": "string", "contentMediaType": "image/jpeg", "description": "The base64-encoded string of the image." } }, "required": [ "image", ] }, ``` I saw an example from Geoligard where he uses an actual file url, but since this is being done on mobile I was trying to use just the base64 version of the image. Any input as to why I’m getting the hallucinations and it almost never triggers the function would be appreciated.

api

neutral

assistants-api

assistants

https://community.openai.com/t/652735

https://community.openai.com/p/890941

Functions (2)

Embeddings (6)

Assistants API Function Tools

2024-02-24T22:07:48.254Z

<p>Hello and welcome to the community!</p> <p>So, I think the big misconception here is this:</p> <aside class="quote no-group" data-username="cdunn" data-post="1" data-topic="652735"> <div class="title"> <div class="quote-controls"></div> <img loading="lazy" alt="" width="24" height="24" src="https://avatars.discourse-cdn.com/v4/letter/c/e68b1a/48.png" class="avatar"> cdunn:</div> <blockquote> <p>I saw an example from Geoligard where he uses an actual file url, but since this is being done on mobile I was trying to use just the base64 version of the image.</p> </blockquote> </aside> <p>The file URL they were using is likely there for a reason. The major flaw here is that there is an assumption the AI will automatically be able to decode the string. It cannot decode strings automatically; you would need to either handle the encoding/decoding yourself, or surgically manipulate the tokenizer.</p> <p>If you are passing it a description of an image as this:</p> <pre><code class="lang-auto">aGVsbG8gd29ybGQh </code></pre> <p>It is either going to be interpreted as a weird token it’s never seen before and hallucinate like crazy, or think it’s junk and ignore this entirely (if it even can).<br> It needs to be fed interpretable data. file URLs work because the AI is already well trained on interpreting such URLs. It is <em>not</em> trained to be a base64 decoder.</p> <p>What you are better off doing is instead of encoding it as base64, encode an image description as a <strong>vector embedding</strong> inside a db. When the function is called, you can replace that embedding to inject the actual textual description of the image.</p>

de1ce23d32

Hello and welcome to the community! So, I think the big misconception here is this:  cdunn: > > I saw an example from Geoligard where he uses an actual file url, but since this is being done on mobile I was trying to use just the base64 version of the image. > > > The file URL they were using is likely there for a reason. The major flaw here is that there is an assumption the AI will automatically be able to decode the string. It cannot decode strings automatically; you would need to either handle the encoding/decoding yourself, or surgically manipulate the tokenizer. If you are passing it a description of an image as this: ``` aGVsbG8gd29ybGQh ``` It is either going to be interpreted as a weird token it’s never seen before and hallucinate like crazy, or think it’s junk and ignore this entirely (if it even can). It needs to be fed interpretable data. file URLs work because the AI is already well trained on interpreting such URLs. It is *not* trained to be a base64 decoder. What you are better off doing is instead of encoding it as base64, encode an image description as a **vector embedding** inside a db. When the function is called, you can replace that embedding to inject the actual textual description of the image.

api

neutral

assistants-api

assistants

https://community.openai.com/t/652735

https://community.openai.com/p/891022

Image Processing

Miscellaneous (27)

Assistants API Function Tools

2024-02-24T22:42:36.860Z

<p>Thanks for the input. Your explanation does explain why when adding an image which is just a super long string of random characters the ai hallucinates. I thought I had seen somewhere that base64 was an acceptable way to send the files, but maybe not. Do you have any thoughts on the format of the body being sent? V1 vs V2? does adding that “tool” property do anything? I’ll try doing a file url and seeing what happens. Thanks again for help so far!</p>

6f019b1cf4

Thanks for the input. Your explanation does explain why when adding an image which is just a super long string of random characters the ai hallucinates. I thought I had seen somewhere that base64 was an acceptable way to send the files, but maybe not. Do you have any thoughts on the format of the body being sent? V1 vs V2? does adding that “tool” property do anything? I’ll try doing a file url and seeing what happens. Thanks again for help so far!

api

neutral

assistants-api

assistants

https://community.openai.com/t/652735

https://community.openai.com/p/891070

Image Processing

Miscellaneous (27)

Assistants API Function Tools

2024-02-24T23:00:15.731Z

<p>Okay, so I digged for a minute, and I think I might be seeing where the confusion is.</p> <p>I’m looking at this for reference:</p> <p><a href="https://platform.openai.com/docs/guides/vision" class="onebox" target="_blank" rel="noopener">https://platform.openai.com/docs/guides/vision</a></p> <p>Here’s the gist:</p> <ul> <li>you were probably looking at how they used GPT-4 Vision, which <em>can</em> be passed a <strong>decoded</strong> base64 string. See here:</li> </ul> <pre><code class="lang-auto"># Function to encode the image def encode_image(image_path): with open(image_path, "rb") as image_file: return base64.b64encode(image_file.read()).decode('utf-8') </code></pre> <ul> <li>gpt-4-v can <strong>only</strong> work with chat <strong>completions</strong>, not the <strong>assistants</strong> API.</li> </ul> <p>Does this mean you can’t build your tool? No. but what it <em>does</em> mean is that you are likely going to have to call a chat completion API with vision separately in order to retrieve the description result of the image that you could use to pass into the assistant’s context/query.</p> <p>Keep in mind the tool property is <em>required</em> if you’re trying to perform any kind of function call with the model. Everything else is up to you. There’s not as much advice I can give on the version differences, because of the problem above. I also personally prefer this kind of work be done on a backend instead of JS, but again, that’s a “me” thing and you are more than okay to continue as-is.</p>

de1ce23d32

Okay, so I digged for a minute, and I think I might be seeing where the confusion is. I’m looking at this for reference: <https://platform.openai.com/docs/guides/vision> Here’s the gist: * you were probably looking at how they used GPT-4 Vision, which *can* be passed a **decoded** base64 string. See here: ``` # Function to encode the image def encode_image(image_path): with open(image_path, "rb") as image_file: return base64.b64encode(image_file.read()).decode('utf-8') ``` * gpt-4-v can **only** work with chat **completions**, not the **assistants** API. Does this mean you can’t build your tool? No. but what it *does* mean is that you are likely going to have to call a chat completion API with vision separately in order to retrieve the description result of the image that you could use to pass into the assistant’s context/query. Keep in mind the tool property is *required* if you’re trying to perform any kind of function call with the model. Everything else is up to you. There’s not as much advice I can give on the version differences, because of the problem above. I also personally prefer this kind of work be done on a backend instead of JS, but again, that’s a “me” thing and you are more than okay to continue as-is.

api

neutral

assistants-api

assistants

https://community.openai.com/t/652735

https://community.openai.com/p/891100

Image Processing

Miscellaneous (27)

Assistants API Function Tools

2024-02-26T00:02:17.699Z

<p><div class="lightbox-wrapper"><a class="lightbox" href="https://global.discourse-cdn.com/openai1/original/4X/f/7/d/f7d31d215ce8010883c3ab7a7f9c40a250e1c86c.png" data-download-href="/uploads/short-url/zmmf9vU75Ir0miaOdgqlJt48UyM.png?dl=1" title="Screen Shot 2024-02-25 at 6.47.25 PM" rel="noopener nofollow ugc"><img src="https://global.discourse-cdn.com/openai1/original/4X/f/7/d/f7d31d215ce8010883c3ab7a7f9c40a250e1c86c.png" alt="Screen Shot 2024-02-25 at 6.47.25 PM" data-base62-sha1="zmmf9vU75Ir0miaOdgqlJt48UyM" width="690" height="305" data-dominant-color="181818"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">Screen Shot 2024-02-25 at 6.47.25 PM</span><span class="informations">893×396 26.6 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div><br> Macha I appreciate all your help yesterday. So I tried uploading a url you can see the url and run in the image above. The code for upload is below and I tried adjusting the function tool to match. But once again I’m back in a spot where it won’t trigger the requires action to make use of the function tool which, yes does call the Vision api separately to then return the description of the image back to the assistant. It’s also still hallucinating when there is an image even being passed as a url. Any thoughts? I’m wondering whether I could add another property that would trigger it. Like a containsImage property that’s true/false see if that might trigger the function.</p> <pre><code class="lang-auto"> body: JSON.stringify({ role: "user", text: userMessage.text, image_url: userMessage.image_url, }) </code></pre> <p>function tool:</p> <pre><code class="lang-auto"> "name": "analyzeImage", "parameters": { "type": "object", "properties": { "image_url": { "type": "string", "contentMediaType": "image/jpeg", "description": "The url of the image sent by the user." }, "text": { "type": "string", "description": "Message the user sent along with the image, without the URL itself" } }, "required": [ "image_url", "text" ] }, </code></pre>

6f019b1cf4

[Screen Shot 2024-02-25 at 6.47.25 PM893×396 26.6 KB](https://global.discourse-cdn.com/openai1/original/4X/f/7/d/f7d31d215ce8010883c3ab7a7f9c40a250e1c86c.png "Screen Shot 2024-02-25 at 6.47.25 PM") Macha I appreciate all your help yesterday. So I tried uploading a url you can see the url and run in the image above. The code for upload is below and I tried adjusting the function tool to match. But once again I’m back in a spot where it won’t trigger the requires action to make use of the function tool which, yes does call the Vision api separately to then return the description of the image back to the assistant. It’s also still hallucinating when there is an image even being passed as a url. Any thoughts? I’m wondering whether I could add another property that would trigger it. Like a containsImage property that’s true/false see if that might trigger the function. ``` body: JSON.stringify({ role: "user", text: userMessage.text, image_url: userMessage.image_url, }) ``` function tool: ``` "name": "analyzeImage", "parameters": { "type": "object", "properties": { "image_url": { "type": "string", "contentMediaType": "image/jpeg", "description": "The url of the image sent by the user." }, "text": { "type": "string", "description": "Message the user sent along with the image, without the URL itself" } }, "required": [ "image_url", "text" ] }, ```

api

neutral

assistants-api

assistants

https://community.openai.com/t/652735

https://community.openai.com/p/893074

Image Processing

Miscellaneous (27)

Assistants API Function Tools

2024-02-26T01:05:48.787Z

<p>Of course! Always happy to help!</p> <p>Can we see the updated script again in full? Usually in these situations, when I’m reviewing my own code, I like to “pretend” I’m the computer and go through the logic flow of the code step-by-step to make sure it’s flowing the right way.</p> <p>Also, I can’t tell from this function call that gpt-4-v is being called and used properly. My thinking was more…ensure the tool is a <em>wrapper</em> for the gpt-4-v call, but I don’t necessarily see evidence of that here. Then again, at this point, it’s becoming more of a “how might I find a hacky workaround to retrieve vision data via assistant API call”.</p>

de1ce23d32

Of course! Always happy to help! Can we see the updated script again in full? Usually in these situations, when I’m reviewing my own code, I like to “pretend” I’m the computer and go through the logic flow of the code step-by-step to make sure it’s flowing the right way. Also, I can’t tell from this function call that gpt-4-v is being called and used properly. My thinking was more…ensure the tool is a *wrapper* for the gpt-4-v call, but I don’t necessarily see evidence of that here. Then again, at this point, it’s becoming more of a “how might I find a hacky workaround to retrieve vision data via assistant API call”.

api

positive

assistants-api

assistants

https://community.openai.com/t/652735

https://community.openai.com/p/893158

Functions

GPTs (4)

Assistants API Function Tools

2024-02-26T02:00:02.209Z

<p>Macha you’re very kind to keep digging on this.<br> So below is my create message</p> <pre><code class="lang-auto"> const createMessage = async (threadId, userMessage) =&gt; { console.log('create message triggered') try { if (userMessage.image_url) { console.log('create message file:', userMessage.image_url) await fetch(`https://api.openai.com/v1/threads/${threadId}/messages`, { method: "POST", headers: { "Content-Type": "application/json", 'Authorization': `Bearer APIKEY`, 'OpenAI-Beta': 'assistants=v1', }, body: JSON.stringify({ role: "user", text: userMessage.text, image_url: userMessage.image_url, containsImage: true, }) }); console.log('User message sent to API contains image:',); } else { await fetch(`https://api.openai.com/v1/threads/${threadId}/messages`, { method: "POST", headers: { "Content-Type": "application/json", 'Authorization': `Bearer APIKEY`, 'OpenAI-Beta': 'assistants=v1', }, body: JSON.stringify({ role: "user", content: userMessage.text }) }); console.log('User message sent to API does not have an image:',); } } catch (error) { console.error("Error sending user message:", error); } }; </code></pre> <p>Here is my check run that it should pick up the requires action</p> <pre><code class="lang-auto"> const checkRun = async (threadId, runId) =&gt; { try { let completed = false; let retries = 0; const maxRetries = 120; // Maximum retries before giving up const timeoutMs = 1200000; // Maximum time to wait in milliseconds (e.g., 30 seconds) const startTime = Date.now(); while (!completed &amp;&amp; retries &lt; maxRetries &amp;&amp; !awaitingResults) { retries++; setTyping(true) const statusCheckResponse = await fetch( `https://api.openai.com/v1/threads/${threadId}/runs/${runId}`, { method: "GET", headers: { "Content-Type": "application/json", 'Authorization': `Bearer APIKEY`, 'OpenAI-Beta': 'assistants=v1', }, } ); if (!statusCheckResponse.ok) { console.error("API request failed at check status:", statusCheckResponse.statusText); break; // Exit the loop if the status check request fails } const runResponse = await statusCheckResponse.json(); switch (runResponse.status) { case 'completed': completed = true; console.log('Run completed, fetching messages...'); try { // Try-catch block specifically for fetchMessages await fetchMessages(threadId); } catch (error) { console.error('Error fetching messages:', error); } finally { setAwaitingResponse(false); } break; case 'in_progress': console.log('The run is currently in progress.'); break; case 'requires_action': console.log('requires action'); // Access the array of required tool outputs const toolOutputs = await runResponse.required_action.submit_tool_outputs?.tool_calls; console.log('Tool id from requires action ', toolOutputs); const toolId = toolOutputs[0].id if (toolOutputs &amp;&amp; toolOutputs.length &gt; 0) { setAwaitingResults(true); try { console.log('Tool id is : ', toolOutputs[0].id) console.log('Tool image is: ', toolOutputs[0].image_base64) await analyzeImage(threadId, runId, toolId, toolId, toolOutputs) } catch (error) { console.error('Error processing tool output:', error); setAwaitingResults(false); } } else { console.log('No tool outputs to process'); } break; case 'failed': case 'cancelled': completed = true; console.log(`The run has ${runResponse.status}.`); break; default: console.log(`Unknown status: ${runResponse.status}`); break; } // If not completed, wait for 1 second before the next poll if (!completed) { if ((Date.now() - startTime) &gt; timeoutMs) { console.log('Operation timed out.'); break; } await new Promise(resolve =&gt; setTimeout(resolve, 1000)); } } if (!completed) { console.log(`Run did not complete after ${maxRetries} retries or timed out.`); // Perform any necessary cleanup or notification } } catch (error) { console.error("Error checking run:", error); console.error("Response Status:", error.response.status); console.error("Response Text:", await error.response.text()); } finally { setAwaitingResponse(false); // This should be the way to update your loading state } }; </code></pre> <p>Here is my vision api call</p> <pre><code class="lang-auto">const analyzeImage = async (threadId, runId, toolId, toolOutputs) =&gt; { try { console.log('Starting to analyze image'); console.log('tool outputs inside analyze image: ',toolOutputs) console.log('Image is: ', toolOutputs[0].image_base64) const startTime = performance.now(); const response = await fetch("https://api.openai.com/v1/chat/completions", { method: "POST", headers: { "Content-Type": "application/json", 'Authorization': `Bearer APIKEY`, }, body: JSON.stringify({ model: "gpt-4-vision-preview", messages: [ { role: "system", content: "You are a helpful assistant." }, { role: "user", content: [ { type: "text", text: "Custom Instructions" }, { type: "image_url", image_url: { "url": "hard coded test image", 'detail': 'auto' }, }, ], } ], max_tokens: 2500 }) }); console.log('waiting on response from vision api'); if (response.ok) { const endTime = performance.now(); const data = await response.json(); console.log("main response:", data.choices[0].message.content); const elapsedTime = endTime - startTime; console.log(`myFunction took ${elapsedTime} milliseconds to execute.`); const results = data.choices[0].message.content; await sendBack(results, threadId, runId, toolId); } else { console.error("Request failed with status:", response.status); } } catch (error) { console.log('Failed to analyze image: ', error); } }; export { analyzeImage } </code></pre>

6f019b1cf4

Macha you’re very kind to keep digging on this. So below is my create message ``` const createMessage = async (threadId, userMessage) => { console.log('create message triggered') try { if (userMessage.image_url) { console.log('create message file:', userMessage.image_url) await fetch(`https://api.openai.com/v1/threads/${threadId}/messages`, { method: "POST", headers: { "Content-Type": "application/json", 'Authorization': `Bearer APIKEY`, 'OpenAI-Beta': 'assistants=v1', }, body: JSON.stringify({ role: "user", text: userMessage.text, image_url: userMessage.image_url, containsImage: true, }) }); console.log('User message sent to API contains image:',); } else { await fetch(`https://api.openai.com/v1/threads/${threadId}/messages`, { method: "POST", headers: { "Content-Type": "application/json", 'Authorization': `Bearer APIKEY`, 'OpenAI-Beta': 'assistants=v1', }, body: JSON.stringify({ role: "user", content: userMessage.text }) }); console.log('User message sent to API does not have an image:',); } } catch (error) { console.error("Error sending user message:", error); } }; ``` Here is my check run that it should pick up the requires action ``` const checkRun = async (threadId, runId) => { try { let completed = false; let retries = 0; const maxRetries = 120; // Maximum retries before giving up const timeoutMs = 1200000; // Maximum time to wait in milliseconds (e.g., 30 seconds) const startTime = Date.now(); while (!completed && retries < maxRetries && !awaitingResults) { retries++; setTyping(true) const statusCheckResponse = await fetch( `https://api.openai.com/v1/threads/${threadId}/runs/${runId}`, { method: "GET", headers: { "Content-Type": "application/json", 'Authorization': `Bearer APIKEY`, 'OpenAI-Beta': 'assistants=v1', }, } ); if (!statusCheckResponse.ok) { console.error("API request failed at check status:", statusCheckResponse.statusText); break; // Exit the loop if the status check request fails } const runResponse = await statusCheckResponse.json(); switch (runResponse.status) { case 'completed': completed = true; console.log('Run completed, fetching messages...'); try { // Try-catch block specifically for fetchMessages await fetchMessages(threadId); } catch (error) { console.error('Error fetching messages:', error); } finally { setAwaitingResponse(false); } break; case 'in_progress': console.log('The run is currently in progress.'); break; case 'requires_action': console.log('requires action'); // Access the array of required tool outputs const toolOutputs = await runResponse.required_action.submit_tool_outputs?.tool_calls; console.log('Tool id from requires action ', toolOutputs); const toolId = toolOutputs[0].id if (toolOutputs && toolOutputs.length > 0) { setAwaitingResults(true); try { console.log('Tool id is : ', toolOutputs[0].id) console.log('Tool image is: ', toolOutputs[0].image_base64) await analyzeImage(threadId, runId, toolId, toolId, toolOutputs) } catch (error) { console.error('Error processing tool output:', error); setAwaitingResults(false); } } else { console.log('No tool outputs to process'); } break; case 'failed': case 'cancelled': completed = true; console.log(`The run has ${runResponse.status}.`); break; default: console.log(`Unknown status: ${runResponse.status}`); break; } // If not completed, wait for 1 second before the next poll if (!completed) { if ((Date.now() - startTime) > timeoutMs) { console.log('Operation timed out.'); break; } await new Promise(resolve => setTimeout(resolve, 1000)); } } if (!completed) { console.log(`Run did not complete after ${maxRetries} retries or timed out.`); // Perform any necessary cleanup or notification } } catch (error) { console.error("Error checking run:", error); console.error("Response Status:", error.response.status); console.error("Response Text:", await error.response.text()); } finally { setAwaitingResponse(false); // This should be the way to update your loading state } }; ``` Here is my vision api call ``` const analyzeImage = async (threadId, runId, toolId, toolOutputs) => { try { console.log('Starting to analyze image'); console.log('tool outputs inside analyze image: ',toolOutputs) console.log('Image is: ', toolOutputs[0].image_base64) const startTime = performance.now(); const response = await fetch("https://api.openai.com/v1/chat/completions", { method: "POST", headers: { "Content-Type": "application/json", 'Authorization': `Bearer APIKEY`, }, body: JSON.stringify({ model: "gpt-4-vision-preview", messages: [ { role: "system", content: "You are a helpful assistant." }, { role: "user", content: [ { type: "text", text: "Custom Instructions" }, { type: "image_url", image_url: { "url": "hard coded test image", 'detail': 'auto' }, }, ], } ], max_tokens: 2500 }) }); console.log('waiting on response from vision api'); if (response.ok) { const endTime = performance.now(); const data = await response.json(); console.log("main response:", data.choices[0].message.content); const elapsedTime = endTime - startTime; console.log(`myFunction took ${elapsedTime} milliseconds to execute.`); const results = data.choices[0].message.content; await sendBack(results, threadId, runId, toolId); } else { console.error("Request failed with status:", response.status); } } catch (error) { console.log('Failed to analyze image: ', error); } }; export { analyzeImage } ```

api

positive

assistants-api

assistants

https://community.openai.com/t/652735

https://community.openai.com/p/893217

Error Handling

Embeddings (6)

Assistants API Function Tools

2024-02-26T23:56:04.269Z

<p>Alright, haha this is definitely giving me a run for my money, but I like challenges!</p> <p>So, looking at your previous message,</p> <aside class="quote no-group" data-username="cdunn" data-post="5" data-topic="652735"> <div class="title"> <div class="quote-controls"></div> <img loading="lazy" alt="" width="24" height="24" src="https://avatars.discourse-cdn.com/v4/letter/c/e68b1a/48.png" class="avatar"> cdunn:</div> <blockquote> <p><div class="lightbox-wrapper"><a class="lightbox" href="https://global.discourse-cdn.com/openai1/original/4X/f/7/d/f7d31d215ce8010883c3ab7a7f9c40a250e1c86c.png" data-download-href="/uploads/short-url/zmmf9vU75Ir0miaOdgqlJt48UyM.png?dl=1" title="Screen Shot 2024-02-25 at 6.47.25 PM"><img src="https://global.discourse-cdn.com/openai1/original/4X/f/7/d/f7d31d215ce8010883c3ab7a7f9c40a250e1c86c.png" alt="Screen Shot 2024-02-25 at 6.47.25 PM" data-base62-sha1="zmmf9vU75Ir0miaOdgqlJt48UyM" width="690" height="305" data-dominant-color="181818"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">Screen Shot 2024-02-25 at 6.47.25 PM</span><span class="informations">893×396 26.6 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p> </blockquote> </aside> <p>(btw thank god you have logs, I’m terrible at that)<br> from this, it looks like it <em>is</em> successfully calling this part of your if statement:</p> <aside class="quote no-group" data-username="cdunn" data-post="7" data-topic="652735"> <div class="title"> <div class="quote-controls"></div> <img loading="lazy" alt="" width="24" height="24" src="https://avatars.discourse-cdn.com/v4/letter/c/e68b1a/48.png" class="avatar"> cdunn:</div> <blockquote> <pre><code class="lang-auto">if (userMessage.image_url) { console.log('create message file:', userMessage.image_url) await fetch(`https://api.openai.com/v1/threads/${threadId}/messages`, { method: "POST", headers: { "Content-Type": "application/json", 'Authorization': `Bearer APIKEY`, 'OpenAI-Beta': 'assistants=v1', }, body: JSON.stringify({ role: "user", text: userMessage.text, image_url: userMessage.image_url, containsImage: true, }) }); console.log('User message sent to API contains image:',); </code></pre> </blockquote> </aside> <p>Then, it goes to this part of the code:</p> <aside class="quote no-group" data-username="cdunn" data-post="7" data-topic="652735"> <div class="title"> <div class="quote-controls"></div> <img loading="lazy" alt="" width="24" height="24" src="https://avatars.discourse-cdn.com/v4/letter/c/e68b1a/48.png" class="avatar"> cdunn:</div> <blockquote> <pre><code class="lang-auto">switch (runResponse.status) { case 'completed': completed = true; console.log('Run completed, fetching messages...'); try { // Try-catch block specifically for fetchMessages await fetchMessages(threadId); } catch (error) { console.error('Error fetching messages:', error); } finally { setAwaitingResponse(false); } break; case 'in_progress': console.log('The run is currently in progress.'); break; </code></pre> </blockquote> </aside> <p>Notice how the rest of your switch statement is not in my quote.</p> <p>So, the code looks right, but it does seem to be ignoring the function call.</p> <p>After looking at the actual assistant’s custom tool call schema seen <a href="https://platform.openai.com/docs/assistants/tools/function-calling">here</a>:</p> <pre><code class="lang-auto">assistant = client.beta.assistants.create( instructions="You are a weather bot. Use the provided functions to answer questions.", model="gpt-4-turbo-preview", tools=[{ "type": "function", "function": { "name": "getCurrentWeather", "description": "Get the weather in location", "parameters": { "type": "object", "properties": { "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"}, "unit": {"type": "string", "enum": ["c", "f"]} }, "required": ["location"] } } }, { "type": "function", "function": { "name": "getNickname", "description": "Get the nickname of a city", "parameters": { "type": "object", "properties": { "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"}, }, "required": ["location"] } } }] ) </code></pre> <p>This leads me to conclude your tool definition may not be right. Like this:</p> <aside class="quote no-group" data-username="cdunn" data-post="5" data-topic="652735"> <div class="title"> <div class="quote-controls"></div> <img loading="lazy" alt="" width="24" height="24" src="https://avatars.discourse-cdn.com/v4/letter/c/e68b1a/48.png" class="avatar"> cdunn:</div> <blockquote> <p><code>"type": "object",</code></p> </blockquote> </aside> <p>It should be “function”. You need to match the function call definition properly, otherwise it wouldn’t be able to properly identify it as a tool it has available to use.</p> <p>Fix this, and it should be able to start picking up the tool. The rest of the flow looks fine, it’s just getting the AI to pick up the tool that’s the problem.</p>

de1ce23d32

Alright, haha this is definitely giving me a run for my money, but I like challenges! So, looking at your previous message,  cdunn: > > [Screen Shot 2024-02-25 at 6.47.25 PM893×396 26.6 KB](https://global.discourse-cdn.com/openai1/original/4X/f/7/d/f7d31d215ce8010883c3ab7a7f9c40a250e1c86c.png "Screen Shot 2024-02-25 at 6.47.25 PM") > > > (btw thank god you have logs, I’m terrible at that) from this, it looks like it *is* successfully calling this part of your if statement:  cdunn: > > > ``` > if (userMessage.image_url) { > console.log('create message file:', userMessage.image_url) > await fetch(`https://api.openai.com/v1/threads/${threadId}/messages`, { > method: "POST", > headers: { > "Content-Type": "application/json", > 'Authorization': `Bearer APIKEY`, > 'OpenAI-Beta': 'assistants=v1', > }, > body: JSON.stringify({ > role: "user", > text: userMessage.text, > image_url: userMessage.image_url, > containsImage: true, > > }) > }); > console.log('User message sent to API contains image:',); > > ``` > > Then, it goes to this part of the code:  cdunn: > > > ``` > switch (runResponse.status) { > case 'completed': > completed = true; > console.log('Run completed, fetching messages...'); > try { // Try-catch block specifically for fetchMessages > await fetchMessages(threadId); > } catch (error) { > console.error('Error fetching messages:', error); > } finally { > setAwaitingResponse(false); > } > break; > case 'in_progress': > console.log('The run is currently in progress.'); > break; > > ``` > > Notice how the rest of your switch statement is not in my quote. So, the code looks right, but it does seem to be ignoring the function call. After looking at the actual assistant’s custom tool call schema seen [here](https://platform.openai.com/docs/assistants/tools/function-calling): ``` assistant = client.beta.assistants.create( instructions="You are a weather bot. Use the provided functions to answer questions.", model="gpt-4-turbo-preview", tools=[{ "type": "function", "function": { "name": "getCurrentWeather", "description": "Get the weather in location", "parameters": { "type": "object", "properties": { "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"}, "unit": {"type": "string", "enum": ["c", "f"]} }, "required": ["location"] } } }, { "type": "function", "function": { "name": "getNickname", "description": "Get the nickname of a city", "parameters": { "type": "object", "properties": { "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"}, }, "required": ["location"] } } }] ) ``` This leads me to conclude your tool definition may not be right. Like this:  cdunn: > > `"type": "object",` > > > It should be “function”. You need to match the function call definition properly, otherwise it wouldn’t be able to properly identify it as a tool it has available to use. Fix this, and it should be able to start picking up the tool. The rest of the flow looks fine, it’s just getting the AI to pick up the tool that’s the problem.

api

positive

assistants-api

assistants

https://community.openai.com/t/652735

https://community.openai.com/p/895367

Assistant

Miscellaneous (27)

Assistants API Function Tools

2024-02-27T01:42:18.083Z

<p>Macha, You might be onto something here. Here is the documentation version:</p> <pre><code class="lang-auto">Defining functions First, define your functions when creating an Assistant: curl https://api.openai.com/v1/assistants \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "OpenAI-Beta: assistants=v1" \ -d '{ "instructions": "You are a weather bot. Use the provided functions to answer questions.", "tools": [{ "type": "function", "function": { "name": "getCurrentWeather", "description": "Get the weather in location", "parameters": { "type": "object", "properties": { "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"}, "unit": {"type": "string", "enum": ["c", "f"]} }, "required": ["location"] } } }, </code></pre> <p>Ok so I modify this for my use which looks something like this:</p> <pre><code class="lang-auto">{ "type": "function", "function": { "name": "analyzeImage", "description": "custom description.", "parameters": { "type": "object", "properties": { "image_url": { "type": "string", "contentMediaType": "image/jpeg", "description": "The url of the image sent by the user. Starts with https://" } }, "required": ["image_url"] } } } </code></pre> <p>It then says I need to give the function a name so I put “name”: above the type and click save. When I reopen it to examine it it shows this:</p> <pre><code class="lang-auto">{ "name": "analyzeImage", "parameters": { "type": "object", "properties": {}, "required": [] } } </code></pre> <p>I think you’re on the right track here, but the documentation seems to be a little off. I’ll keep exploring this.</p>

6f019b1cf4

Macha, You might be onto something here. Here is the documentation version: ``` Defining functions First, define your functions when creating an Assistant: curl https://api.openai.com/v1/assistants \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $OPENAI_API_KEY" \ -H "OpenAI-Beta: assistants=v1" \ -d '{ "instructions": "You are a weather bot. Use the provided functions to answer questions.", "tools": [{ "type": "function", "function": { "name": "getCurrentWeather", "description": "Get the weather in location", "parameters": { "type": "object", "properties": { "location": {"type": "string", "description": "The city and state e.g. San Francisco, CA"}, "unit": {"type": "string", "enum": ["c", "f"]} }, "required": ["location"] } } }, ``` Ok so I modify this for my use which looks something like this: ``` { "type": "function", "function": { "name": "analyzeImage", "description": "custom description.", "parameters": { "type": "object", "properties": { "image_url": { "type": "string", "contentMediaType": "image/jpeg", "description": "The url of the image sent by the user. Starts with https://" } }, "required": ["image_url"] } } } ``` It then says I need to give the function a name so I put “name”: above the type and click save. When I reopen it to examine it it shows this: ``` { "name": "analyzeImage", "parameters": { "type": "object", "properties": {}, "required": [] } } ``` I think you’re on the right track here, but the documentation seems to be a little off. I’ll keep exploring this.

api

neutral

assistants-api

assistants

https://community.openai.com/t/652735

https://community.openai.com/p/895574

Functions (2)

Embeddings (6)

Is there a way to retrieve an assistant by Name instead of ID?

2024-02-26T23:57:55.366Z

<p>Because i’m trying something stateless, I’d love to be able programmatically name an Assistant and retrieve it later.</p> <p>It would also be nice if I could do this with a thread.</p> <p>Anyone have a clue?</p> <p>Sorry if this is an easy question i’m a noob</p>

f1827cbe5a

Because i’m trying something stateless, I’d love to be able programmatically name an Assistant and retrieve it later. It would also be nice if I could do this with a thread. Anyone have a clue? Sorry if this is an easy question i’m a noob

api

positive

https://community.openai.com/t/656066

https://community.openai.com/p/895374

File Management (3)

Embeddings (6)

Is there a way to retrieve an assistant by Name instead of ID?

2024-02-27T00:31:48.086Z

<p>Welcome to the community!</p> <p>How many assistants do you have?</p> <p>you could just list them <a href="https://platform.openai.com/docs/api-reference/assistants/listAssistants">https://platform.openai.com/docs/api-reference/assistants/listAssistants</a>, filter by name, and then retrieve whatever, no?</p> <p><img src="https://emoji.discourse-cdn.com/twitter/thinking.png?v=12" title=":thinking:" class="emoji only-emoji" alt=":thinking:" loading="lazy" width="20" height="20"></p> <p>If you’re using node or something (anything other than PHP really), you can memoize that information to speed things up.</p>

69bb4acfca

Welcome to the community! How many assistants do you have? you could just list them <https://platform.openai.com/docs/api-reference/assistants/listAssistants>, filter by name, and then retrieve whatever, no?  If you’re using node or something (anything other than PHP really), you can memoize that information to speed things up.

api

positive

https://community.openai.com/t/656066

https://community.openai.com/p/895452

Assistants, API, Platform

Embeddings (6)

Is there a way to retrieve an assistant by Name instead of ID?

2024-02-27T01:30:56.309Z

<p>I use the name in my code only as well.<br> Here’s how I do it:</p> <pre><code class="lang-auto">def getAssistant(**kwargs) -&gt; object: ''' Get an OpenAI Assistant object to run a task with parameters: Name - the name of the assistant or id / assistant_id - the id of the assistant when called with no parameters it will return a list of all assistants returns: the OpenAI assistant object ''' client = OpenAI( api_key=.OPENAI_API_KEY) if len(kwargs.items())==0: return client.beta.assistants.list() for key, value in kwargs.items(): if key == 'name' or key == 'Name' or key == 'assistantName': Name = value aa = client.beta.assistants.list() assistant = None for a in aa.data: if a.name == Name: assistant = a break elif key == 'assistant_id' or key=="id": assistant = client.beta.assistants.retrieve(assistant_id=value) break return assistant </code></pre> <p>If you’re doing Python and Django you might be interested in cheking out my library (where this code is from)</p><aside class="onebox allowlistedgeneric" data-onebox-src="https://pypi.org/project/django-openai-assistant/"> <header class="source"> <img src="https://global.discourse-cdn.com/openai1/original/4X/9/a/4/9a452346fd233f9dfe09f625aea9d7d9404375a9.png" class="site-icon" data-dominant-color="F4F4F2" width="32" height="30"> <a href="https://pypi.org/project/django-openai-assistant/" target="_blank" rel="noopener nofollow ugc">PyPI</a> </header> <article class="onebox-body"> <img width="300" height="300" src="https://global.discourse-cdn.com/openai1/original/3X/6/f/6f97026709e67b2111b465be6427519ead928642.webp" class="thumbnail onebox-avatar" data-dominant-color="EAEBE9"> <h3><a href="https://pypi.org/project/django-openai-assistant/" target="_blank" rel="noopener nofollow ugc">django-openai-assistant</a></h3> <p>Django OpenAI Assistant</p> </article> <div class="onebox-metadata"> </div> <div style="clear: both"></div> </aside>

59f4c2f25d

I use the name in my code only as well. Here’s how I do it: ``` def getAssistant(**kwargs) -> object: ''' Get an OpenAI Assistant object to run a task with parameters: Name - the name of the assistant or id / assistant_id - the id of the assistant when called with no parameters it will return a list of all assistants returns: the OpenAI assistant object ''' client = OpenAI( api_key=.OPENAI_API_KEY) if len(kwargs.items())==0: return client.beta.assistants.list() for key, value in kwargs.items(): if key == 'name' or key == 'Name' or key == 'assistantName': Name = value aa = client.beta.assistants.list() assistant = None for a in aa.data: if a.name == Name: assistant = a break elif key == 'assistant_id' or key=="id": assistant = client.beta.assistants.retrieve(assistant_id=value) break return assistant ``` If you’re doing Python and Django you might be interested in cheking out my library (where this code is from)  [PyPI](https://pypi.org/project/django-openai-assistant/)  ### [django-openai-assistant](https://pypi.org/project/django-openai-assistant/) Django OpenAI Assistant

api

neutral

https://community.openai.com/t/656066

https://community.openai.com/p/895557

Assistant

Miscellaneous (27)

Multiple copies of a clone made in platform

2024-02-26T21:51:00.170Z

<p>hi,</p> <p>got a few days automagical additional clones when in clone one assistant in the online interface.</p> <p><div class="lightbox-wrapper"><a class="lightbox" href="https://global.discourse-cdn.com/openai1/original/4X/5/4/e/54eea77b761914d76b9601a354e11400f9b6fd37.png" data-download-href="/uploads/short-url/c7lq9yOGoYkNkjYV5LXjZEAT5bN.png?dl=1" title="image" rel="noopener nofollow ugc"><img src="https://global.discourse-cdn.com/openai1/optimized/4X/5/4/e/54eea77b761914d76b9601a354e11400f9b6fd37_2_418x500.png" alt="image" data-base62-sha1="c7lq9yOGoYkNkjYV5LXjZEAT5bN" width="418" height="500" srcset="https://global.discourse-cdn.com/openai1/optimized/4X/5/4/e/54eea77b761914d76b9601a354e11400f9b6fd37_2_418x500.png, https://global.discourse-cdn.com/openai1/optimized/4X/5/4/e/54eea77b761914d76b9601a354e11400f9b6fd37_2_627x750.png 1.5x, https://global.discourse-cdn.com/openai1/original/4X/5/4/e/54eea77b761914d76b9601a354e11400f9b6fd37.png 2x" data-dominant-color="FAFAFB"><div class="meta"><svg class="fa d-icon d-icon-far-image svg-icon" aria-hidden="true"><use href="#far-image"></use></svg><span class="filename">image</span><span class="informations">693×827 46 KB</span><svg class="fa d-icon d-icon-discourse-expand svg-icon" aria-hidden="true"><use href="#discourse-expand"></use></svg></div></a></div></p>

270db946c4

hi, got a few days automagical additional clones when in clone one assistant in the online interface. [image693×827 46 KB](https://global.discourse-cdn.com/openai1/original/4X/5/4/e/54eea77b761914d76b9601a354e11400f9b6fd37.png "image")

api

neutral

assistants-api

bug

https://community.openai.com/t/655948

https://community.openai.com/p/895200

Optimized Images

Miscellaneous (27)

Sentiment and clarity analysis of customer service calls using GPT 3.5 Turbo

2024-02-26T20:00:55.686Z

<p>I am building a web app for a customer service company and would like to use gpt-3.5-turbo to do basic sentiment analyses on the conversations.</p> <p>The basic idea is i want to label each line of the transcription with either a Positive , Negative or Neutral label , and a Clarity label either Clear or Unclear based on whether the customer understood the request.</p> <p>My text is in Arabic and if formatted as follows<br> [hh:mm:ss] Speaker(Agent/Customer) : Content</p> <p>I pass the whole content of the transcription in one go to the API with a this prompt as initialization<br> prompt : Your job is to extract the timestamp at the start each line then read the content of the line and based of the sentiment expressed in the content of the line add to it one of the following tags {Positive} , {Negative} and {Neutral} label each line based on the sentiment expressed in the line additionally include a {Unclear} label if the text in the line is unclear or was not understood by the customer or Include {Clear} label if the conversation the sentence was understood and was clear include only the timestamp, sentiment and clarity for that line as follows make sure to analyze the sentiment and clarity for every line in the input and every timestamp don’t skip anything in the input</p> <p>My problem is it has a problem identifying clarity especially when the context of the misunderstanding comes more than 2 time blocks away , also it choice of sentiment label is sometime random how would i solve this?</p> <p>Also would passing the input line by line instead of one big block help at all and would it cost more?</p> <p>I am fairly new in using Openai’s API, so any guidance would be greatly appreciated.</p>

48baaf4a14

I am building a web app for a customer service company and would like to use gpt-3.5-turbo to do basic sentiment analyses on the conversations. The basic idea is i want to label each line of the transcription with either a Positive , Negative or Neutral label , and a Clarity label either Clear or Unclear based on whether the customer understood the request. My text is in Arabic and if formatted as follows [hh:mm:ss] Speaker(Agent/Customer) : Content I pass the whole content of the transcription in one go to the API with a this prompt as initialization prompt : Your job is to extract the timestamp at the start each line then read the content of the line and based of the sentiment expressed in the content of the line add to it one of the following tags {Positive} , {Negative} and {Neutral} label each line based on the sentiment expressed in the line additionally include a {Unclear} label if the text in the line is unclear or was not understood by the customer or Include {Clear} label if the conversation the sentence was understood and was clear include only the timestamp, sentiment and clarity for that line as follows make sure to analyze the sentiment and clarity for every line in the input and every timestamp don’t skip anything in the input My problem is it has a problem identifying clarity especially when the context of the misunderstanding comes more than 2 time blocks away , also it choice of sentiment label is sometime random how would i solve this? Also would passing the input line by line instead of one big block help at all and would it cost more? I am fairly new in using Openai’s API, so any guidance would be greatly appreciated.

api

neutral

api

gpt-35-turbo

https://community.openai.com/t/655833

https://community.openai.com/p/895062

AI Assistants

Embeddings (6)

Sentiment and clarity analysis of customer service calls using GPT 3.5 Turbo

2024-02-26T23:52:22.534Z

<p>Yes, the higher number of elements you include, especially if labeled in an unconventional way, the more the AI will have incoherent answers that are essentially fabrications.</p> <p>To start, ensure you use the API parameter top_p:0.001 so that a top-choice is actually given instead of a random answer.</p> <hr> <p>I made a classifier with a clearly-labeled input:</p> <p><code>{1: "I am building a web app for a customer service company and would like to use gpt-3.5-turbo to do basic sentiment analyses on the conversations." , 2: "The basic idea is i want to label each line of the transcription with either a Positive , Negative or Neutral label , and a Clarity label either Clear or Unclear based on whether the customer understood the request." , 3: "My text is in Arabic and if formatted as follows</code></p> <p>Then secondly a system prompt with the goal of making sure each response is itself labeled, has a text excerpt for reference, and only when what is actually being scored is reiterated do we have the AI generate the token:</p> <pre><code class="lang-auto">For each input item, output continuous integer range(1,10) where: 1: very confused and angry, up to 10: confident, assured, correct Output format: [{ 1: five words of text (elided), assuredness: score },{ 2: five words... </code></pre> <p>Then get the AI’s answer (what it’s testing for here is something that also is hard to judge)</p> <pre><code class="lang-auto"> [{ 1: "Using GPT-3.5-turbo for sentiment analysis", assuredness: 7 },{ 2: "Labeling transcription lines with sentiment and clarity", assuredness: 6 },{ 3: "Arabic text format for transcription", assuredness: 8 },{ 4: "Passing transcription content to API", assuredness: 7 },{ 5: "Challenges with clarity and sentiment labeling", assuredness: 5 },{ 6: "Effect of passing input line by line", assuredness: 6 }] </code></pre> <p>You then have list, dictionaries of answers that can be parsed directly for the keys (pick the key not “assuredness” to get number), and values.</p> <p>By having a clear focus on matching what is currently being output in reference to the original item, a processor with multiple items can do a better job.</p>

b368833582

Yes, the higher number of elements you include, especially if labeled in an unconventional way, the more the AI will have incoherent answers that are essentially fabrications. To start, ensure you use the API parameter top\_p:0.001 so that a top-choice is actually given instead of a random answer. --- I made a classifier with a clearly-labeled input: `{1: "I am building a web app for a customer service company and would like to use gpt-3.5-turbo to do basic sentiment analyses on the conversations." , 2: "The basic idea is i want to label each line of the transcription with either a Positive , Negative or Neutral label , and a Clarity label either Clear or Unclear based on whether the customer understood the request." , 3: "My text is in Arabic and if formatted as follows` Then secondly a system prompt with the goal of making sure each response is itself labeled, has a text excerpt for reference, and only when what is actually being scored is reiterated do we have the AI generate the token: ``` For each input item, output continuous integer range(1,10) where: 1: very confused and angry, up to 10: confident, assured, correct Output format: [{ 1: five words of text (elided), assuredness: score },{ 2: five words... ``` Then get the AI’s answer (what it’s testing for here is something that also is hard to judge) ``` [{ 1: "Using GPT-3.5-turbo for sentiment analysis", assuredness: 7 },{ 2: "Labeling transcription lines with sentiment and clarity", assuredness: 6 },{ 3: "Arabic text format for transcription", assuredness: 8 },{ 4: "Passing transcription content to API", assuredness: 7 },{ 5: "Challenges with clarity and sentiment labeling", assuredness: 5 },{ 6: "Effect of passing input line by line", assuredness: 6 }] ``` You then have list, dictionaries of answers that can be parsed directly for the keys (pick the key not “assuredness” to get number), and values. By having a clear focus on matching what is currently being output in reference to the original item, a processor with multiple items can do a better job.

api

neutral

api

gpt-35-turbo

https://community.openai.com/t/655833

https://community.openai.com/p/895362

Writing Prompts

Artificial Intelligence

Can we get Sky's voice on the TTS API?

2024-02-20T22:30:12.570Z

<p>The voices on the TTS API are pretty good, but the voices on the ChatGPT app just feel more interesting and emotive. Especially Sky. I think I may have a small crush on Her…</p> <p>Let’s bring these voices to the API too!</p>

c242226e21

The voices on the TTS API are pretty good, but the voices on the ChatGPT app just feel more interesting and emotive. Especially Sky. I think I may have a small crush on Her… Let’s bring these voices to the API too!

api

positive

tts

https://community.openai.com/t/644230

https://community.openai.com/p/879898

Speech Recognition

Artificial Intelligence

Can we get Sky's voice on the TTS API?

2024-02-21T21:31:24.251Z

<p>Thinking about it more, the ChatGPT and API voices are probably completely different models. ChatGPT TTS supports streaming, after all, while the API does not. A streaming TTS API would be really cool too!</p>

c242226e21

Thinking about it more, the ChatGPT and API voices are probably completely different models. ChatGPT TTS supports streaming, after all, while the API does not. A streaming TTS API would be really cool too!

api

positive

tts

https://community.openai.com/t/644230

https://community.openai.com/p/883138

Streaming

Embeddings (6)

Can we get Sky's voice on the TTS API?

2024-02-21T22:28:54.138Z

<p>It would be nice to get the Sky voice via the API. I haven’t personally seen anything that would suggest that the ChatGPT and API models are different from one another though. Also, I believe that the API does support streaming and from my tests it has allowed me to listen to the audio prior to the full download being finished. Here is the link to that part of the documentation if it helps: <a href="https://platform.openai.com/docs/guides/text-to-speech/streaming-real-time-audio" rel="noopener nofollow ugc">https://platform.openai.com/docs/guides/text-to-speech/streaming-real-time-audio</a></p>

5a9eb34450

It would be nice to get the Sky voice via the API. I haven’t personally seen anything that would suggest that the ChatGPT and API models are different from one another though. Also, I believe that the API does support streaming and from my tests it has allowed me to listen to the audio prior to the full download being finished. Here is the link to that part of the documentation if it helps: <https://platform.openai.com/docs/guides/text-to-speech/streaming-real-time-audio>

api

neutral

tts

https://community.openai.com/t/644230

https://community.openai.com/p/883234

Speech Recognition

Artificial Intelligence

Can we get Sky's voice on the TTS API?

2024-02-22T20:49:12.562Z

<p>Hey, you’re right!</p> <p>Though I found that the code on that page doesn’t actually work, it results in a DeprecationError. I was able to get streaming to work by calling the REST API directly, using <a href="https://community.openai.com/t/streaming-from-text-to-speech-api/493784/20">code from this thread</a>. That’s probably a better place to continue the discussion on streaming.</p>

c242226e21

Hey, you’re right! Though I found that the code on that page doesn’t actually work, it results in a DeprecationError. I was able to get streaming to work by calling the REST API directly, using [code from this thread](https://community.openai.com/t/streaming-from-text-to-speech-api/493784/20). That’s probably a better place to continue the discussion on streaming.

api

neutral

tts

https://community.openai.com/t/644230

https://community.openai.com/p/885994

Streaming

Embeddings (6)

Can we get Sky's voice on the TTS API?

2024-02-26T23:37:44.046Z

<p>I agree it would great! The voices in the GPT app are much better. It would be great to be able to use them in our applications!</p>

50a9e27879

I agree it would great! The voices in the GPT app are much better. It would be great to be able to use them in our applications!

api

positive

tts

https://community.openai.com/t/644230

https://community.openai.com/p/895335

Speech Recognition

Artificial Intelligence

Need Help Understanding ASSISTANT API Pricing for GPT-4 Turbo and File Storage

2023-12-29T12:53:19.324Z

<p>Hi everyone,</p> <p>I’m trying to get a clear understanding of the ASSISTANT API pricing, specifically with my current setup. Here’s what I have:</p> <ul> <li>A single assistant</li> <li>Model: gpt-4-turbo-preview-1106</li> <li>4k tokens for input instruction only</li> <li>Outputs average around 200 tokens</li> <li>20 files totaling slightly less than 0.01GB each</li> </ul> <p>After running about 10 messages in a single thread with the same assistant, I noticed I was charged $3. I’m trying to break down the costs and would appreciate some insights.</p> <p>Here’s my understanding of the pricing model:</p> <p>dailyCost = fileCosts + modelCosts</p> <p>fileCosts = fileSize * $0.20</p> <p>modelCosts = (kTokens * $0.01) + (200Tokens * $0.03) // for input + output</p> <p>Additionally, I came across two points regarding the retrieval tool pricing that I’m struggling to comprehend:</p> <ol> <li>The Pricing page states: “Retrieval $0.20 / GB / assistant / day (free until 01/12/2024).” Does this mean my current costs are entirely dependent on model costs?</li> <li>More intriguingly, there’s a statement: “In addition, files attached to messages are charged on a per-assistant basis if the messages are part of a run where the retrieval tool is enabled. For example, running an assistant with retrieval enabled on a thread with 10 messages, each with 1 unique file (10 total unique files), will incur a per-GB per-day charge on all 10 files (in addition to any files attached to the assistant itself).”</li> </ol> <p>Regarding point number 2, I’m not sure what this implies and how it could affect me given my setup.</p> <p>Could anyone provide some clarity on this? Thanks in advance!</p>

26e4547713

Hi everyone, I’m trying to get a clear understanding of the ASSISTANT API pricing, specifically with my current setup. Here’s what I have: * A single assistant * Model: gpt-4-turbo-preview-1106 * 4k tokens for input instruction only * Outputs average around 200 tokens * 20 files totaling slightly less than 0.01GB each After running about 10 messages in a single thread with the same assistant, I noticed I was charged $3. I’m trying to break down the costs and would appreciate some insights. Here’s my understanding of the pricing model: dailyCost = fileCosts + modelCosts fileCosts = fileSize \* $0.20 modelCosts = (kTokens \* $0.01) + (200Tokens \* $0.03) // for input + output Additionally, I came across two points regarding the retrieval tool pricing that I’m struggling to comprehend: 1. The Pricing page states: “Retrieval $0.20 / GB / assistant / day (free until 01/12/2024).” Does this mean my current costs are entirely dependent on model costs? 2. More intriguingly, there’s a statement: “In addition, files attached to messages are charged on a per-assistant basis if the messages are part of a run where the retrieval tool is enabled. For example, running an assistant with retrieval enabled on a thread with 10 messages, each with 1 unique file (10 total unique files), will incur a per-GB per-day charge on all 10 files (in addition to any files attached to the assistant itself).” Regarding point number 2, I’m not sure what this implies and how it could affect me given my setup. Could anyone provide some clarity on this? Thanks in advance!

api

neutral

https://community.openai.com/t/575105

https://community.openai.com/p/777759

Pricing (2)

Tokenization (2)

Need Help Understanding ASSISTANT API Pricing for GPT-4 Turbo and File Storage

2023-12-29T13:21:59.012Z

<p>You are way off base with the calculation of your “model costs” price, which simply cannot be made because the internals are autonomous, iterative, undocumented.</p> <p>gpt-4-turbo = <code>gpt-4-1106-preview</code>: 128k token context length, max 4k output (AI resists output over 1k)</p> <p>Then read:</p> <blockquote> <p>Threads don’t have a size limit. You can add as many Messages as you want to a Thread. The Assistant will ensure that requests to the model fit within the maximum context window,<br> …<br> As the run progresses, the assistant appends Messages to the thread with the <code>role="assistant"</code>. The Assistant will also automatically decide what previous Messages to include in the context window for the model. This has both an impact on pricing as well as model performance<br> …<br> Retrieval currently optimizes for quality by adding all relevant content to the context of model calls.<br> …</p> <p>Note that you are not charged based on the size of the files you upload via the Files API but rather based on which files you attach to a specific Assistant or Message that get indexed.</p> </blockquote> <p>The retrieval is also based on iterative browsing function tool calls: part of the documentation disguises this, part of the documentation points out the requirement for models that emit parallel tool calls to use this.</p> <p>Essentially, there is no predicting and no controlling the usage and costs. The AI might internally get stuck in a loop of calling with errors while loaded with 100k of context tokens. You can at best estimate the minimum you will spend, but not the maximum.</p>

b368833582

You are way off base with the calculation of your “model costs” price, which simply cannot be made because the internals are autonomous, iterative, undocumented. gpt-4-turbo = `gpt-4-1106-preview`: 128k token context length, max 4k output (AI resists output over 1k) Then read: > > Threads don’t have a size limit. You can add as many Messages as you want to a Thread. The Assistant will ensure that requests to the model fit within the maximum context window, > > … > > As the run progresses, the assistant appends Messages to the thread with the `role="assistant"`. The Assistant will also automatically decide what previous Messages to include in the context window for the model. This has both an impact on pricing as well as model performance > > … > > Retrieval currently optimizes for quality by adding all relevant content to the context of model calls. > > … > > > Note that you are not charged based on the size of the files you upload via the Files API but rather based on which files you attach to a specific Assistant or Message that get indexed. > > > The retrieval is also based on iterative browsing function tool calls: part of the documentation disguises this, part of the documentation points out the requirement for models that emit parallel tool calls to use this. Essentially, there is no predicting and no controlling the usage and costs. The AI might internally get stuck in a loop of calling with errors while loaded with 100k of context tokens. You can at best estimate the minimum you will spend, but not the maximum.

api

neutral

https://community.openai.com/t/575105

https://community.openai.com/p/777787

Pricing (2)

Tokenization (2)

Need Help Understanding ASSISTANT API Pricing for GPT-4 Turbo and File Storage

2023-12-29T13:47:55.183Z

<aside class="quote no-group" data-username="_j" data-post="2" data-topic="575105"> <div class="title"> <div class="quote-controls"></div> <img loading="lazy" alt="" width="24" height="24" src="https://sea2.discourse-cdn.com/openai1/user_avatar/community.openai.com/_j/48/277961_2.png" class="avatar"> _j:</div> <blockquote> <p>You can at best estimate the minimum you will spend, but not the maximum.</p> </blockquote> </aside> <p>Understood. So, regarding minimum costs, is given calculation right?</p>

26e4547713

 \_j: > > You can at best estimate the minimum you will spend, but not the maximum. > > > Understood. So, regarding minimum costs, is given calculation right?

api

neutral

https://community.openai.com/t/575105

https://community.openai.com/p/777819

Pricing

Payment (4)

Need Help Understanding ASSISTANT API Pricing for GPT-4 Turbo and File Storage

2023-12-29T13:59:25.954Z

<p>If you disable the unpredictability, which is disabling code interpreter, disabling retrieval, not using tools, then…</p> <p>the amount of input should be the tokens of the current user message — PLUS all the previous user messages and assistant responses in a thread, up to the model context length limit. (edit: plus the instructions, including unseen instructions, also included each run)</p> <p>Assistants does not have a parameter where you can specify the maximum number of chat turns or chat tokens from thread conversation history to pass to a new AI model call. The maximum is the model used.</p> <p>So then input cost per question depends on how long you let a user engage in the same thread before they are forced to start anew.</p> <hr> <p>The file costs are GB/day - multiplied by the number of assistants files are attached to. So if you make five assistants with different instructions, they each get a daily bill for the same connected file. OpenAI hasn’t clarified the partial use of GB storage, but it seems that it might start at $0.20.</p> <p>Code interpreter is billed by how many threads are left with code interpreter sessions open. Again the price page is absent further clarification. One can find that the unengaged chat session’s code interpreter should stop counting after an hour, so a day of a thread unused (unless it is close to date change) should not be billed.</p> <p>All in all, with reports of “I tried it again, got hit with $70” or “we were billed $200 in a day”… it is something to watch and wait for improvement in documentation and function.</p>

b368833582

If you disable the unpredictability, which is disabling code interpreter, disabling retrieval, not using tools, then… the amount of input should be the tokens of the current user message — PLUS all the previous user messages and assistant responses in a thread, up to the model context length limit. (edit: plus the instructions, including unseen instructions, also included each run) Assistants does not have a parameter where you can specify the maximum number of chat turns or chat tokens from thread conversation history to pass to a new AI model call. The maximum is the model used. So then input cost per question depends on how long you let a user engage in the same thread before they are forced to start anew. --- The file costs are GB/day - multiplied by the number of assistants files are attached to. So if you make five assistants with different instructions, they each get a daily bill for the same connected file. OpenAI hasn’t clarified the partial use of GB storage, but it seems that it might start at $0.20. Code interpreter is billed by how many threads are left with code interpreter sessions open. Again the price page is absent further clarification. One can find that the unengaged chat session’s code interpreter should stop counting after an hour, so a day of a thread unused (unless it is close to date change) should not be billed. All in all, with reports of “I tried it again, got hit with $70” or “we were billed $200 in a day”… it is something to watch and wait for improvement in documentation and function.

api

neutral

https://community.openai.com/t/575105

https://community.openai.com/p/777832

Model Completion

Tokenization (2)

Need Help Understanding ASSISTANT API Pricing for GPT-4 Turbo and File Storage

2024-01-03T19:49:40.001Z

<p>Clear enough! Thank you very much for your time!</p>

26e4547713

Clear enough! Thank you very much for your time!

api

positive

https://community.openai.com/t/575105

https://community.openai.com/p/783011

Women's Feedback

Emoji (8)

Need Help Understanding ASSISTANT API Pricing for GPT-4 Turbo and File Storage

2024-01-25T12:26:59.025Z

<p>Hi all,</p> <p>Has there been any further clarification on these costs since the last post on this thread?</p> <p>I’m trying to come up with a commercial pricing model for around 100 clients requiring their own custom built Assistant, but I have no idea how to ensure our ongoing operational costs are covered. We can’t price this on a per usage basis to our clients so this is not an option.</p> <p>Any advice or feedback on this would be greatly appreciated.</p> <p>Many thanks</p>

bb2b7ffd15

Hi all, Has there been any further clarification on these costs since the last post on this thread? I’m trying to come up with a commercial pricing model for around 100 clients requiring their own custom built Assistant, but I have no idea how to ensure our ongoing operational costs are covered. We can’t price this on a per usage basis to our clients so this is not an option. Any advice or feedback on this would be greatly appreciated. Many thanks

api

negative

https://community.openai.com/t/575105

https://community.openai.com/p/818780

Pricing

Payment (4)

Need Help Understanding ASSISTANT API Pricing for GPT-4 Turbo and File Storage

2024-01-25T12:41:59.604Z

<p>Someone that wants to complain about their limit of 40 messages within three hours could easily rack up more than $40 in billing when the assistant has multiple features enabled, such as retreval documents which have the 128k gpt-4-1106 context length loaded up, with then the agent making many internal iterative calls per input.</p> <p>If you do not charge per usage, you will be creating an application that is the best value for those that exploit your pricing model and the lack of budget configurability or transparency of assistants.</p>

b368833582

Someone that wants to complain about their limit of 40 messages within three hours could easily rack up more than $40 in billing when the assistant has multiple features enabled, such as retreval documents which have the 128k gpt-4-1106 context length loaded up, with then the agent making many internal iterative calls per input. If you do not charge per usage, you will be creating an application that is the best value for those that exploit your pricing model and the lack of budget configurability or transparency of assistants.

api

neutral

https://community.openai.com/t/575105

https://community.openai.com/p/818800

Pricing

Payment (4)

Need Help Understanding ASSISTANT API Pricing for GPT-4 Turbo and File Storage

2024-02-26T22:49:10.909Z

<p>Guys has anyone got any updates on this? I really need some help making a pricing model using Assistants. I understand the unpredictability but I can make some estimates I guess right?</p>

f13e6fcd2a

Guys has anyone got any updates on this? I really need some help making a pricing model using Assistants. I understand the unpredictability but I can make some estimates I guess right?

api

neutral

https://community.openai.com/t/575105

https://community.openai.com/p/895271

Pricing

Payment (4)

Need Help Understanding ASSISTANT API Pricing for GPT-4 Turbo and File Storage

2024-02-26T22:49:47.443Z

<p>Hi mate, did you ever manage to figure this out?</p>

f13e6fcd2a

Hi mate, did you ever manage to figure this out?

api

neutral

https://community.openai.com/t/575105

https://community.openai.com/p/895273

Headache

Emoji (8)

Bug in gpt-4-0125-preview

2024-02-22T08:54:12.027Z

<p>We use gpt-4-0125-preview with several functions and retrieval. When we load more than 4096 tokens for retrieval and functions, the model starts lying, inventing information and calling functions without asking the user for information, but simply substituting fictitious values. Although the documentation says that you can provide 128000 tokens as an input, and you can receive up to 4096 tokens as a response. But in fact, it doesn’t work that way.</p>

e0155d2092

We use gpt-4-0125-preview with several functions and retrieval. When we load more than 4096 tokens for retrieval and functions, the model starts lying, inventing information and calling functions without asking the user for information, but simply substituting fictitious values. Although the documentation says that you can provide 128000 tokens as an input, and you can receive up to 4096 tokens as a response. But in fact, it doesn’t work that way.

api/feedback

neutral

chatgpt

gpt-4

api

assistants-api

https://community.openai.com/t/647661

https://community.openai.com/p/884390

GPT Model

Tokenization (2)

Bug in gpt-4-0125-preview

2024-02-26T11:05:29.134Z

<p><a class="mention" href="/u/logankilpatrick">@logankilpatrick</a> help please with this problem.</p>

e0155d2092

[@logankilpatrick](/u/logankilpatrick) help please with this problem.

api/feedback

neutral

chatgpt

gpt-4

api

assistants-api

https://community.openai.com/t/647661

https://community.openai.com/p/894076

Headache

Emoji (8)

Bug in gpt-4-0125-preview

2024-02-26T19:33:56.676Z

<aside class="quote no-group" data-username="vitaliyishkevychdev" data-post="1" data-topic="647661"> <div class="title"> <div class="quote-controls"></div> <img loading="lazy" alt="" width="24" height="24" src="https://sea2.discourse-cdn.com/openai1/user_avatar/community.openai.com/vitaliyishkevychdev/48/151373_2.png" class="avatar"> vitaliyishkevychdev:</div> <blockquote> <p>the model starts lying, inventing information and calling functions without asking the user for information, but simply substituting fictitious values.</p> </blockquote> </aside> <p>This phenomenon is known as hallucination.<br> It seems that using the Assistant function on GPT-4-turbo is particularly prone to hallucinations.</p> <p>The Assistant is still in beta and is not yet official. So, all feedback is valuable!</p>

ed0e16a3dd

 vitaliyishkevychdev: > > the model starts lying, inventing information and calling functions without asking the user for information, but simply substituting fictitious values. > > > This phenomenon is known as hallucination. It seems that using the Assistant function on GPT-4-turbo is particularly prone to hallucinations. The Assistant is still in beta and is not yet official. So, all feedback is valuable!

api/feedback

neutral

chatgpt

gpt-4

api

assistants-api

https://community.openai.com/t/647661

https://community.openai.com/p/895028

Miscellaneous (12)

Artificial Intelligence

Bug in gpt-4-0125-preview

2024-02-26T19:46:34.569Z

<p>It is clear. It’s just strange that he starts hallucinating exactly at the mark of 4096 tokens. It seems that he has 4096 tokens, not 128,000 tokens.</p>

e0155d2092

It is clear. It’s just strange that he starts hallucinating exactly at the mark of 4096 tokens. It seems that he has 4096 tokens, not 128,000 tokens.

api/feedback

neutral

chatgpt

gpt-4

api

assistants-api

https://community.openai.com/t/647661

https://community.openai.com/p/895047

Tokens

Tokenization (2)

Bug in gpt-4-0125-preview

2024-02-26T21:00:03.246Z

<p>In general, language models that include GPT-4 Turbo are more prone to hallucination when the model is given longer contexts, due to its limited attention.</p> <p>I also know about this very well from using the assistant feature, and seeing how GPT-4 Turbo starts creating implausible stories when the context gets longer.</p> <p>I have managed to get around this by instructing GPT-4 Turbo with the assistant function to use the code interpreter and store only the necessary part of a long sentence in a Python dictionary or list and use it as needed, instead of using the long context😅</p> <p>Although GPT-4 Turbo can handle 128,000 tokens (input and output combined), I think it’s best to know there are limits to its attention, and use it wisely, without expecting to use the whole context length(this also saves on token fees)<img src="https://emoji.discourse-cdn.com/twitter/slightly_smiling_face.png?v=12" title=":slightly_smiling_face:" class="emoji" alt=":slightly_smiling_face:" loading="lazy" width="20" height="20">.</p>

ed0e16a3dd

In general, language models that include GPT-4 Turbo are more prone to hallucination when the model is given longer contexts, due to its limited attention. I also know about this very well from using the assistant feature, and seeing how GPT-4 Turbo starts creating implausible stories when the context gets longer. I have managed to get around this by instructing GPT-4 Turbo with the assistant function to use the code interpreter and store only the necessary part of a long sentence in a Python dictionary or list and use it as needed, instead of using the long context😅 Although GPT-4 Turbo can handle 128,000 tokens (input and output combined), I think it’s best to know there are limits to its attention, and use it wisely, without expecting to use the whole context length(this also saves on token fees).

api/feedback

neutral

chatgpt

gpt-4

api

assistants-api

https://community.openai.com/t/647661

https://community.openai.com/p/895134

Campaign

GPTs (4)

Image Request to DALL-E Failing With Server Disconnected

2024-02-26T16:21:45.544Z

<p>Hey All,</p> <p>I have suddenly encountered an issue with my API request to DALL-E suddenly throwing a “Server Disconnected” when requesting anything.</p> <p>It was working find maybe 2 days ago and since then it constantly disconnects</p> <pre><code class="lang-auto">async def generate_image(userInput): logger = logging.getLogger(__name__) logger.debug("Generating image with input: %s", userInput) headers = { 'Content-Type': 'application/json', 'Authorization': f'Bearer {OPENAI_KEY}' } data = { 'model': 'dall-e-3', 'prompt': userInput, 'size': '1024x1024', 'quality': 'hd', 'n': 1 } try: async with aiohttp.ClientSession() as session: async with session.post('[https call here to/v1/images/generations]', headers=headers, json=data) as resp: response_data = await resp.json() logger.debug("Response data: %s", response_data) if resp.status == 200: image_url = response_data['data'][0]['url'] logger.info("Image URL: %s", image_url) return image_url else: logger.error("Failed to generate image. Status: %s, Data: %s", resp.status, response_data) return None except Exception as e: logger.exception("An error occurred while generating the image: %s", e) return None </code></pre> <p>and the logging returned from making the subsequent request:</p> <pre><code class="lang-auto">&gt; Request: Flower &gt; DEBUG:NetworkAdapters.OpenAINetworkAdapter:Generating image with input: Flower &gt; ERROR:NetworkAdapters.OpenAINetworkAdapter:An error occurred while generating the image: Server disconnected &gt; Traceback (most recent call last): &gt; File C:\Users\...\NetworkAdapters\OpenAINetworkAdapter.py", line 109, in generate_image &gt; async with session.post('https://api.openai.com/v1/images/generations', headers=headers, json=data) as resp: &gt; File C:\Users\...\Python\Lib\site-packages\aiohttp\client.py", line 1141, in __aenter__ &gt; self._resp = await self._coro &gt; ^^^^^^^^^^^^^^^^ &gt; File "C:\Users\...\Python\Lib\site-packages\aiohttp\client.py", line 560, in _request &gt; await resp.start(conn) &gt; File "C:\Users\...\Python\Lib\site-packages\aiohttp\client_reqrep.py", line 899, in start &gt; message, payload = await protocol.read() # type: ignore[union-attr] &gt; ^^^^^^^^^^^^^^^^^^^^^ &gt; File "C:\Users\...\Python\Lib\site-packages\aiohttp\streams.py", line 616, in read &gt; await self._waiter &gt; aiohttp.client_exceptions.ServerDisconnectedError: Server disconnected &gt; Response: None </code></pre> <p>I’m posting this mostly because I am totally baffled as to what changed. I have a request using slash commands (fails) and prefix commands (fails) both of which use the request above and fails with <code>server disconnected</code> everytime</p>

4b54f84862

Hey All, I have suddenly encountered an issue with my API request to DALL-E suddenly throwing a “Server Disconnected” when requesting anything. It was working find maybe 2 days ago and since then it constantly disconnects ``` async def generate_image(userInput): logger = logging.getLogger(__name__) logger.debug("Generating image with input: %s", userInput) headers = { 'Content-Type': 'application/json', 'Authorization': f'Bearer {OPENAI_KEY}' } data = { 'model': 'dall-e-3', 'prompt': userInput, 'size': '1024x1024', 'quality': 'hd', 'n': 1 } try: async with aiohttp.ClientSession() as session: async with session.post('[https call here to/v1/images/generations]', headers=headers, json=data) as resp: response_data = await resp.json() logger.debug("Response data: %s", response_data) if resp.status == 200: image_url = response_data['data'][0]['url'] logger.info("Image URL: %s", image_url) return image_url else: logger.error("Failed to generate image. Status: %s, Data: %s", resp.status, response_data) return None except Exception as e: logger.exception("An error occurred while generating the image: %s", e) return None ``` and the logging returned from making the subsequent request: ``` > Request: Flower > DEBUG:NetworkAdapters.OpenAINetworkAdapter:Generating image with input: Flower > ERROR:NetworkAdapters.OpenAINetworkAdapter:An error occurred while generating the image: Server disconnected > Traceback (most recent call last): > File C:\Users\...\NetworkAdapters\OpenAINetworkAdapter.py", line 109, in generate_image > async with session.post('https://api.openai.com/v1/images/generations', headers=headers, json=data) as resp: > File C:\Users\...\Python\Lib\site-packages\aiohttp\client.py", line 1141, in __aenter__ > self._resp = await self._coro > ^^^^^^^^^^^^^^^^ > File "C:\Users\...\Python\Lib\site-packages\aiohttp\client.py", line 560, in _request > await resp.start(conn) > File "C:\Users\...\Python\Lib\site-packages\aiohttp\client_reqrep.py", line 899, in start > message, payload = await protocol.read() # type: ignore[union-attr] > ^^^^^^^^^^^^^^^^^^^^^ > File "C:\Users\...\Python\Lib\site-packages\aiohttp\streams.py", line 616, in read > await self._waiter > aiohttp.client_exceptions.ServerDisconnectedError: Server disconnected > Response: None ``` I’m posting this mostly because I am totally baffled as to what changed. I have a request using slash commands (fails) and prefix commands (fails) both of which use the request above and fails with `server disconnected` everytime

api

neutral

chatgpt

dalle3

https://community.openai.com/t/655555

https://community.openai.com/p/894690

Python3 Packages

Miscellaneous (27)

Image Request to DALL-E Failing With Server Disconnected

2024-02-26T20:35:31.994Z

<p>After hours of debugging this issue, I have found two potential solutions to this problem.</p> <p>In VS code I had a library <code>asyncio</code> I removed as it claimed the library was unused. However I think it’s necessary to use the <code>aiottp</code> library. I can only assume <code>asyncio</code> is used by <code>aiohttp</code>.</p> <p>Interestingly enough I realized connecting to a VPN caused a very similar issue in terms of “server disconnect” - So ensure you are not connected to VPN unless purposefully doing so.</p>

4b54f84862

After hours of debugging this issue, I have found two potential solutions to this problem. In VS code I had a library `asyncio` I removed as it claimed the library was unused. However I think it’s necessary to use the `aiottp` library. I can only assume `asyncio` is used by `aiohttp`. Interestingly enough I realized connecting to a VPN caused a very similar issue in terms of “server disconnect” - So ensure you are not connected to VPN unless purposefully doing so.

api

neutral

chatgpt

dalle3

https://community.openai.com/t/655555

https://community.openai.com/p/895100

CORS

API (2)

Need help with GPT 4 batching using Langchain wrapper

2024-02-26T20:21:31.899Z

<p>hey guys, I am working with langchain wrapper to query GPT 4, now because of the cost incurred, I want to implement batching.<br> However, I don’t know the right implementation for the same,</p> <p>My approach: I created a prompt template, let’s say <code>data_template</code> and then did <code>data_template = data_template * 10</code> (to process 10 records at a time).</p> <p>Following which data_creation_prompt looks like this,</p> <pre><code class="lang-auto">data_creation_prompt = PromptTemplate( input_variables=["term", "synonym_list"], partial_variables={"format_instructions": variation_parser.get_format_instructions(),}, template=data_creation_template ) </code></pre> <p>a) is this the correct approach ?<br> b) If yes, what I observed doing this way - I get processing time of 60s/it around 96hrs for 5-6k records. When I did only passed one record at a time, it was 2s/it. Has anyone observed such diff between completion time 5hrs v 96hrs in processing these many records w/o and with batching respectively ?<br> c) If no, may someone please let me know what’s the right way of batching…</p>

573da82797

hey guys, I am working with langchain wrapper to query GPT 4, now because of the cost incurred, I want to implement batching. However, I don’t know the right implementation for the same, My approach: I created a prompt template, let’s say `data_template` and then did `data_template = data_template * 10` (to process 10 records at a time). Following which data\_creation\_prompt looks like this, ``` data_creation_prompt = PromptTemplate( input_variables=["term", "synonym_list"], partial_variables={"format_instructions": variation_parser.get_format_instructions(),}, template=data_creation_template ) ``` a) is this the correct approach ? b) If yes, what I observed doing this way - I get processing time of 60s/it around 96hrs for 5-6k records. When I did only passed one record at a time, it was 2s/it. Has anyone observed such diff between completion time 5hrs v 96hrs in processing these many records w/o and with batching respectively ? c) If no, may someone please let me know what’s the right way of batching…

api

neutral

langchain

gpt4

batching

https://community.openai.com/t/655852

https://community.openai.com/p/895083

Parallel Methods

Embeddings (6)

Where can I find the syntax of the function template/schema for Assistants?

2024-02-26T18:12:06.443Z

<p>I currently use functions in Assistants. However, to be able to use functions from libraries I wrote without making them incompatible with existing code (Python), I added a keyword parameter (key=default value) to all functions called by the Assistants in case they need to return text (instead of numeric values or dicts, etc.) whenever they are used within an Assistant. If the parameter is not defined in the function call, everything works as before. If the parameter is defined as “mode=‘chatgpt’”, the function returns text. Therefore, an Assistant must always include in the returned parameters, awaiting to receive the tool_output, a constant value “mode=‘chatgpt’”. I don’t know how to proceed.</p> <p>to be short:</p> <p>This is the function def i want to describe in a function template:</p> <p><code>def get_moons_in_year(d, n: int, t: str, mode='code') -&gt; List[Tuple[ephem.Date, str]]:</code></p> <p>I need the Assistante returned :</p> <p><code>get_moons_in_year("d":"2024-02-3","n":1,"t":"prossima","mode=chatgpt"})</code></p> <p>the assistant instead returns always as following:</p> <p><code>get_moons_in_year("d":"2024-02-23","n":1,"t":"prossima"})</code></p> <p>Thank you in advance</p>

a5b08ac443

I currently use functions in Assistants. However, to be able to use functions from libraries I wrote without making them incompatible with existing code (Python), I added a keyword parameter (key=default value) to all functions called by the Assistants in case they need to return text (instead of numeric values or dicts, etc.) whenever they are used within an Assistant. If the parameter is not defined in the function call, everything works as before. If the parameter is defined as “mode=‘chatgpt’”, the function returns text. Therefore, an Assistant must always include in the returned parameters, awaiting to receive the tool\_output, a constant value “mode=‘chatgpt’”. I don’t know how to proceed. to be short: This is the function def i want to describe in a function template: `def get_moons_in_year(d, n: int, t: str, mode='code') -> List[Tuple[ephem.Date, str]]:` I need the Assistante returned : `get_moons_in_year("d":"2024-02-3","n":1,"t":"prossima","mode=chatgpt"})` the assistant instead returns always as following: `get_moons_in_year("d":"2024-02-23","n":1,"t":"prossima"})` Thank you in advance

api

neutral