o i/@sdZddlmZddlZddlZddlZddlZddlmZm Z m Z m Z e e ZeddZedZedZed d Zd-ddZd.ddZd/ddZd0ddZ  d1d2d'd(Z  d1d2d)d*ZGd+d,d,ZdS)3u AI agent runner for post-call analysis. Loads ``business_agent_config`` records for a given bid from the database, builds prompts using the call transcript and speaker segments, calls the configured LLM (AWS Bedrock or Ollama), and returns an aggregated analysis dict. Each agent produces a JSON blob stored under its ``agent_name`` key. Two top-level shortcuts — ``quality_score`` and ``summary`` — are promoted from the first agent that emits them so the call records list can display scores without parsing per-agent JSON. ) annotationsN)AnyDictListOptional AWS_REGIONz us-east-1AWS_ACCESS_KEY_IDAWS_SECRET_ACCESS_KEYAWS_NOVA_MODELzamazon.nova-lite-v1:0 transcriptstrspeaker_segments List[Dict]returncCsl|r2g}|D]$}|d|dd}|dpd}|r*|d|d|q|r2d|S|p5dS) zOReturn a readable conversation string from speaker_segments, or raw transcript.rolespeakerSpeakertext[z]:  )getstripappendupperjoin)r r linessegrrr7/home/aiteam/pcaa-dev/dashboard-backend/agent_runner.py_format_transcript"s r templatecontextDict[str, Any]cCs2|D]\}}|d|dt|pd}q|S)zHReplace ``{key}`` placeholders in *template* with values from *context*.{}r)itemsreplacer )r!r"keyvaluerrr_render_template0sr*scoring_parametersc CsXdd|D}|s dSdg}tdd|D}|d|di}|D]}|d p,d }||g|q$|D]f\}}|d |d |D]V}|d pRd} |drZdnd} |d|dd| | d|ddd|dr|d|d|dr|d|d|dr|d|dqJq;|dd|S) zFormat a list of scoring parameter dicts into a structured prompt block. Only parameters with ``enabled != False`` are included. cSsg|] }|ddur|qS)enabledF)r.0prrr <sz3_build_scoring_parameters_block..rz SCORING PARAMETERS TO EVALUATE: css"|] }t|dp dVqdS) max_scorerN)intrr-rrr As z2_build_scoring_parameters_block..zTotal possible score: z points parameter_groupGeneralz []parameter_typeRequiredis_fatalu ⚠ FATALu • parameter_namez (u ) — max r1rz ptscheck_descriptionz Check: detailed_descriptionz Detail: sample_utterancesz Example: z For each parameter return: score (0 to max_score), evidence (exact quote), reasoning, and applicable (true/false). If a FATAL parameter scores 0, set overall quality_score to 0.r)sumrr setdefaultr&r) r+activertotalgroupsr/g group_nameparamsptypefatalrrr_build_scoring_parameters_block7s8.     rHrrc Cs>zt|WStyYnwdD]l\}}||}|dkr"qd}d}d}t||d|dD]M\}}|r;d}q2|dkrD|rDd}q2|d krM|sM| }|s||krX|d 7}q2||kr|d 8}|dkrzt|||d WSty~Ynwq2qtd |} | rz t| dWStyYdSwdS) z3Extract the first JSON object or array from *text*.))r$r%)rr6rFN)start\T"z \{[\s\S]+\}) jsonloadsr Exceptionfind enumerateresearchgroup) r start_charend_charidxdepthin_strescapeichmatchrrr _extract_json_sT          r_皙?model_id system_prompt user_message temperaturefloat max_tokensr2c Csddl}|jdtttd}dd|igdg}|rd|igng}|j|||||dd } | d i} | d id g} | D] } d| vrI| dSq=d S)zFInvoke an AWS Bedrock Converse-API model and return the text response.rNzbedrock-runtime) service_name region_nameaws_access_key_idaws_secret_access_keyuserrrcontent)re maxTokens)modelIdmessagessysteminferenceConfigoutputmessagernr)boto3client _AWS_REGION_AWS_KEY _AWS_SECRETconverser) rbrcrdrergrvrwrqrrresponsertrnblockrrr _call_bedrocks,  r~c Csnddl}tdd}|d|dd|dgd||d d }|j|d |d d }||diddS)z7Call a local Ollama model and return the response text.rNOLLAMA_BASE_URLzhttp://localhost:11434rrrmrlF)re num_predict)modelrqstreamoptionsz /api/chatx)rNtimeoutrurnr)requestsosgetenvpostraise_for_statusrNr) rbrcrdrergreq_libbase_urlpayloadresprrr _call_ollamas  rc@s*eZdZdZdddZ  ddddZdS) AgentRunnerzRun all enabled AI agents for a bid against a given call transcript. Parameters ---------- db_handler: Shared ``DatabaseHandler`` instance (used to load agent configs). rNonecCs ||_dS)N)_db)self db_handlerrrr__init__s zAgentRunner.__init__Nbidr callidr r Optional[List[Dict]] call_metadataOptional[Dict[str, Any]]r#c Cs|j|}|std||ggdSt||pg}|||d|p#i}ggd} d} d} |D]} | dds:q0| d} | dpDd }| d pMt}| d pTd }| d p[d}t| dpcd}t | dpld}g}| d}t |t rzt |}Wn tyi}Ynwt |tr|dpg}t|}i|d|i}t||}|rd|vr|d|}td||| ||t|zd|d krt|||||}n|dkrt|||||}ntd|t|}|durd|i}|| | <| d| t |tr'| sd|vrt|d| d<d} | s'd|vr't |d| d<d} Wq0tyM}ztjd||| |dd | d!| WYd}~q0d}~ww| S)"aRun all enabled agents and return an aggregated analysis dict. Returns ------- dict with keys: - ````: parsed JSON output for each agent - ``quality_score``: float, promoted from the first agent that emits it - ``summary``: str, promoted from the first agent that emits it - ``agents_run``: list of agent names that were executed - ``agents_failed``: list of agent names that raised an error z'[%s][%s] No agent configs found for bid) agents_run agents_failed)r rrF agent_enabledT agent_namemodel_providerbedrockrbrcruser_prompt_templatez {transcript}rer`rgraruntime_configr+z{scoring_parameters}z z9[%s][%s] Running agent '%s' via %s/%s (scoring_params=%d)ollamazUnknown model provider: N raw_outputr quality_scoresummaryz[%s][%s] Agent '%s' failed: %s)exc_infor)rget_agent_configsloggerinfor rlower_DEFAULT_BEDROCK_MODELrfr2 isinstancer rNrOrPdictrHr*lenr~r ValueErrorr_rerror)rrrr r rconfigs formattedr"resultquality_score_set summary_setcfgrproviderrbrc user_templaterergr+raw_cfg scoring_blockcontext_with_paramsrdraw_textparsedexcrrrruns               zAgentRunner.run)rr)NN) rr rr r r r rrrrr#)__name__ __module__ __qualname____doc__rrrrrrrs  r)r r r rrr )r!r r"r#rr )r+rrr )rr rr)r`ra) rbr rcr rdr rerfrgr2rr )r __future__rrNloggingrrStypingrrrr getLoggerrrrrxryrzrr r*rHr_r~rrrrrrs.          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