From 29ff81f994d3867110cb49e7d0c24b7425a6f95b Mon Sep 17 00:00:00 2001 From: PaulKalho Date: Tue, 20 Jan 2026 16:00:58 -0600 Subject: [PATCH 1/4] feat: draft structure --- algorithms/explanations.py | 101 +++++++++++++++++++++++++++++++++++++ main.py | 57 +++++++++++++++++++++ 2 files changed, 158 insertions(+) create mode 100644 algorithms/explanations.py diff --git a/algorithms/explanations.py b/algorithms/explanations.py new file mode 100644 index 0000000..ad5af6a --- /dev/null +++ b/algorithms/explanations.py @@ -0,0 +1,101 @@ +import logging +import requests +import pandas as pd + +logger = logging.getLogger(__name__) + + +class TopicExplainer: + def __init__( + self, + model_name: str = "llama3.1", + ollama_url: str = "http://localhost:1234", + timeout: str = 60 + ): + self.model_name = model_name + self.ollama_url = ollama_url + self.timeout = timeout + + def explain_topics( + self, + topic_terms: pd.DataFrame, + search_query: str, + source: str, + created_at: str + ): + logger.info("Generating topic explainations...") + + rows = [] + + for topic_id, group in topic_terms.groupby("topic_id"): + terms = ( + group + .sort_values("weight", ascending=False) + .tolist() + ) + + prompt = self._build_promt( + topic_id=topic_id, + terms=terms, + search_query=search_query, + source=source, + created_at=created_at + ) + + description = self._call_ollama(prompt) + + rows.append({ + "topic_id": topic_id, + "description": description + }) + + df = pd.DataFrame(rows) + logger.info( + f"Generated explainations for {len(df)} topics" + ) + + def _build_prompt( + self, + topic_id: int, + terms: list[str], + search_query: str, + source: str, + created_at: str + ): + terms_str = ", ".join(terms) + + date_info = f"Creation date: {created_at}" + + return f""" + Context: + The documents come from {source} + + Search Query: + \"{search_query}\"{date_info} + + Topic ID: {topic_id} + + Top keywords for this topic: + {terms_str} + + Task: + Describe in 1-2 concise sentences what this topic represents. + Focus on the research subfield or thematic area. + Do not list the keywords explicitly. + """ + + def _call_ollama(self, prompt: str) -> str: + logger.debug("Calling Ollama...") + + response = requests.post( + f"{self.ollama_url}/api/generate", + json={ + "model": self.model_name, + "prompt": prompt, + "stream": False + }, + timeout=self.timeout, + ) + + response.raise_for_status() + return response.json()["response"].strip() diff --git a/main.py b/main.py index 5dba9f5..c3e574e 100644 --- a/main.py +++ b/main.py @@ -14,6 +14,8 @@ from algorithms.models import PreprocessedDocument from algorithms.vectorizer import NLPVectorizer +from algorithms.explanations import TopicExplainer + logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' @@ -45,6 +47,33 @@ class LDATopicModeling(EnvSettings): topic_term: TopicTermsOutput +class TopicTermsInput(PostgresSettings, InputSettings): + __identifier__ = "topic_terms_input" + + +class QueryInformationInput(PostgresSettings, InputSettings): + """ + Our TopicExplaination needs some kind of information about the actual + query executed,this query information includes the query and the source + + Looking like: query, source + """ + __identifier__ = "query_information_input" + + +class ExplanationsOutput(PostgresSettings, OutputSettings): + __identifier__ = "ExplanationsOutput" + + +class TopicExplanation(EnvSettings): + MODEL_NAME: str = "llama3.1" + + topic_terms: TopicTermsInput + query_information: QueryInformationInput + + explanations_output: ExplanationsOutput + + def _make_engine(settings: PostgresSettings): return create_engine( f"postgresql+psycopg2://{settings.PG_USER}:{settings.PG_PASS}" @@ -103,3 +132,31 @@ def lda_topic_modeling(settings): # TODO: Use Spark Integration here write_df_to_postgres(doc_topics, settings.doc_topic) write_df_to_postgres(topic_terms, settings.topic_term) + + +@entrypoint(TopicExplanation) +def topic_explaination(settings): + logger.info("Starting topic explaination...") + + logging.info("Querying topic terms from db...") + topic_terms = read_table_from_postgres(settings.topic_terms) + + logging.info("Querying query information from db...") + query_information = read_table_from_postgres(settings.query_information) + + metadata = query_information.iloc[0] + + explainer = TopicExplainer( + model_name=settings.MODEL_NAME, + ) + + explainations = explainer.explain_topics( + topic_terms=topic_terms, + search_query=metadata["query"], + source=metadata["source"], + created_at=metadata["created_at"] + ) + + write_df_to_postgres(explainations, settings.explanations_output) + + logging.info("Topic explanation block finished.") From 04cd36760b89d977c032bcf6de7eee4ba5625b27 Mon Sep 17 00:00:00 2001 From: Paul Kalhorn Date: Wed, 4 Feb 2026 18:26:22 -0500 Subject: [PATCH 2/4] feat: add ollama for explanation --- .github/workflows/ci.yaml | 2 + .gitignore | 2 + algorithms/explanations.py | 80 ++++++------ main.py | 8 +- requirements.txt | 1 + test/files/bike_norm.sql | 175 ++++++++++++++++++++++++++ test/files/doc_topic.sql | 179 +++++++++++++++++++++++++++ test/files/topic_terms.sql | 95 ++++++++++++++ test/test_explaination_entrypoint.py | 140 +++++++++++++++++++++ 9 files changed, 642 insertions(+), 40 deletions(-) create mode 100644 test/files/bike_norm.sql create mode 100644 test/files/doc_topic.sql create mode 100644 test/files/topic_terms.sql create mode 100644 test/test_explaination_entrypoint.py diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index a713081..ac2ff03 100644 --- a/.github/workflows/ci.yaml +++ b/.github/workflows/ci.yaml @@ -91,6 +91,8 @@ jobs: pip install -r requirements.txt - name: Run Tests + env: + OLLAMA_API_KEY: ${{ secrets.OLLAMA_API_KEY }} run: pytest -vv build: diff --git a/.gitignore b/.gitignore index e15106e..60a7879 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,5 @@ +.env + # Byte-compiled / optimized / DLL files __pycache__/ *.py[codz] diff --git a/algorithms/explanations.py b/algorithms/explanations.py index ad5af6a..1cc7b19 100644 --- a/algorithms/explanations.py +++ b/algorithms/explanations.py @@ -1,6 +1,6 @@ import logging -import requests import pandas as pd +from ollama import Client logger = logging.getLogger(__name__) @@ -8,51 +8,60 @@ class TopicExplainer: def __init__( self, - model_name: str = "llama3.1", - ollama_url: str = "http://localhost:1234", - timeout: str = 60 + api_key: str, + model_name: str = "gpt-oss:120b", + timeout: int = 60, ): self.model_name = model_name - self.ollama_url = ollama_url self.timeout = timeout + self.client = Client( + host="https://ollama.com", + headers={ + "Authorization": "Bearer " + api_key + }, + timeout=self.timeout, + ) + def explain_topics( self, topic_terms: pd.DataFrame, search_query: str, source: str, - created_at: str - ): - logger.info("Generating topic explainations...") + created_at: str, + ) -> pd.DataFrame: + logger.info("Generating topic explanations...") rows = [] for topic_id, group in topic_terms.groupby("topic_id"): terms = ( - group - .sort_values("weight", ascending=False) + group.sort_values("weight", ascending=False)["term"] + .astype(str) .tolist() ) - prompt = self._build_promt( + prompt = self._build_prompt( topic_id=topic_id, terms=terms, search_query=search_query, source=source, - created_at=created_at + created_at=created_at, ) description = self._call_ollama(prompt) - rows.append({ - "topic_id": topic_id, - "description": description - }) + rows.append( + { + "topic_id": topic_id, + "description": description, + } + ) df = pd.DataFrame(rows) - logger.info( - f"Generated explainations for {len(df)} topics" - ) + + logger.info(f"Generated explanations for {len(df)} topics") + return df def _build_prompt( self, @@ -60,10 +69,9 @@ def _build_prompt( terms: list[str], search_query: str, source: str, - created_at: str - ): + created_at: str, + ) -> str: terms_str = ", ".join(terms) - date_info = f"Creation date: {created_at}" return f""" @@ -71,7 +79,8 @@ def _build_prompt( The documents come from {source} Search Query: - \"{search_query}\"{date_info} + "{search_query}" + {date_info} Topic ID: {topic_id} @@ -79,23 +88,20 @@ def _build_prompt( {terms_str} Task: - Describe in 1-2 concise sentences what this topic represents. + Describe in 1–2 concise sentences what this topic represents. Focus on the research subfield or thematic area. Do not list the keywords explicitly. - """ + """.strip() def _call_ollama(self, prompt: str) -> str: - logger.debug("Calling Ollama...") - - response = requests.post( - f"{self.ollama_url}/api/generate", - json={ - "model": self.model_name, - "prompt": prompt, - "stream": False - }, - timeout=self.timeout, + logger.debug("Calling Ollama Cloud...") + + response = self.client.chat( + model=self.model_name, + messages=[ + {"role": "user", "content": prompt} + ], + stream=False, ) - response.raise_for_status() - return response.json()["response"].strip() + return response["message"]["content"].strip() diff --git a/main.py b/main.py index c3e574e..55bba35 100644 --- a/main.py +++ b/main.py @@ -62,11 +62,12 @@ class QueryInformationInput(PostgresSettings, InputSettings): class ExplanationsOutput(PostgresSettings, OutputSettings): - __identifier__ = "ExplanationsOutput" + __identifier__ = "explanations_output" class TopicExplanation(EnvSettings): - MODEL_NAME: str = "llama3.1" + MODEL_NAME: str = "gpt-oss:120b" + OLLAMA_API_KEY: str = "" topic_terms: TopicTermsInput query_information: QueryInformationInput @@ -90,7 +91,7 @@ def write_df_to_postgres(df, settings: PostgresSettings): def read_table_from_postgres(settings: PostgresSettings) -> pd.DataFrame: engine = _make_engine(settings) - query = f"SELECT * FROM {settings.DB_TABLE} ORDER BY doc_id;" + query = f"SELECT * FROM {settings.DB_TABLE};" return pd.read_sql(query, engine) @@ -148,6 +149,7 @@ def topic_explaination(settings): explainer = TopicExplainer( model_name=settings.MODEL_NAME, + api_key=settings.OLLAMA_API_KEY ) explainations = explainer.explain_topics( diff --git a/requirements.txt b/requirements.txt index b6ac3b1..8137824 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,3 +6,4 @@ pandas==2.3.2 SQLAlchemy==2.0.43 psycopg2-binary==2.9.10 pytest==9.0.1 +ollama==0.6.1 diff --git a/test/files/bike_norm.sql b/test/files/bike_norm.sql new file mode 100644 index 0000000..1fb4ad0 --- /dev/null +++ b/test/files/bike_norm.sql @@ -0,0 +1,175 @@ +-- +-- PostgreSQL database dump +-- + +-- Dumped from database version 18.1 (Debian 18.1-1.pgdg13+2) +-- Dumped by pg_dump version 18.1 (Debian 18.1-1.pgdg13+2) + +SET statement_timeout = 0; +SET lock_timeout = 0; +SET idle_in_transaction_session_timeout = 0; +SET client_encoding = 'UTF8'; +SET standard_conforming_strings = on; +SELECT pg_catalog.set_config('search_path', '', false); +SET check_function_bodies = false; +SET xmloption = content; +SET client_min_messages = warning; +SET row_security = off; + +SET default_tablespace = ''; + +SET default_table_access_method = heap; + +-- +-- Name: table_d8b2b796-9ae8-4f35-a1a2-e2ede35f0d5a; Type: TABLE; Schema: public; Owner: postgres +-- + +CREATE TABLE public."norm_cycling" ( + doc_id text, + tokens text +); + + +ALTER TABLE public."norm_cycling" OWNER TO postgres; + +-- +-- Data for Name: table_d8b2b796-9ae8-4f35-a1a2-e2ede35f0d5a; Type: TABLE DATA; Schema: public; Owner: postgres +-- + 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0.00037361140489369885, 0.0003735139222401872); +INSERT INTO public."doc_topic" VALUES ('WOS:000687957200005', 0.00033984977523531695, 0.9986403006500136, 0.00033973565329728663, 0.00034023002971421943, 0.000339883891739605); +INSERT INTO public."doc_topic" VALUES ('WOS:000687957400013', 0.9983242799312161, 0.00042007269916033234, 0.00041819142380144803, 0.00041885146724039983, 0.00041860447858176996); +INSERT INTO public."doc_topic" VALUES ('WOS:000689769600001', 0.0005728086054445161, 0.0005737630350673716, 0.0005721596998717182, 0.00057275697344437, 0.997708511686172); +INSERT INTO public."doc_topic" VALUES ('WOS:000690627900001', 0.0007707318980670847, 0.9969192573588098, 0.0007694752597519384, 0.0007689486283030255, 0.0007715868550681984); +INSERT INTO public."doc_topic" VALUES ('WOS:000704580700009', 0.00044066437397143624, 0.9982378376255882, 0.0004403784231473873, 0.0004402857536894737, 0.00044083382360377146); +INSERT INTO public."doc_topic" VALUES ('WOS:000717543100275', 0.0006567162451100517, 0.9973731630608147, 0.000656632793895441, 0.0006557154244552083, 0.0006577724757247359); +INSERT INTO public."doc_topic" VALUES ('WOS:000729240600028', 0.0003493970044737166, 0.00034944716863126945, 0.00034872842512823754, 0.9986035200797264, 0.00034890732204048296); +INSERT INTO public."doc_topic" VALUES ('WOS:000738774600027', 0.9977627727692078, 0.0005619380421203955, 0.0005575550563798194, 0.0005576050792481938, 0.0005601290530437149); +INSERT INTO public."doc_topic" VALUES ('WOS:000780465400001', 0.000633073182131689, 0.0006353408648429043, 0.0006340678771105023, 0.0006343586169902721, 0.9974631594589247); +INSERT INTO public."doc_topic" VALUES ('WOS:000782455800001', 0.9981389978339676, 0.0004649626978680381, 0.00046451372004349537, 0.00046703967248582705, 0.00046448607563494916); +INSERT INTO public."doc_topic" VALUES ('WOS:000788123600002', 0.00034421275659556034, 0.0003449222627765249, 0.0003435724908055071, 0.9986238618448711, 0.0003434306449514246); 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0.0006464622465191008, 0.0006440963229286101, 0.9974226260679713); +INSERT INTO public."doc_topic" VALUES ('WOS:000893530400001', 0.0008347825443775074, 0.9966649623987414, 0.0008331202694836724, 0.0008341005855372808, 0.000833034201860309); +INSERT INTO public."doc_topic" VALUES ('WOS:000908382900005', 0.00043541368295700714, 0.00043552322174372315, 0.9982597455987652, 0.00043470064469161205, 0.0004346168518424776); +INSERT INTO public."doc_topic" VALUES ('WOS:000938162600001', 0.0005399375322718005, 0.9978415299446174, 0.0005393950063162767, 0.0005395571426808528, 0.0005395803741135878); +INSERT INTO public."doc_topic" VALUES ('WOS:000980597900001', 0.0007795587647653054, 0.9968846849748592, 0.0007777255793177154, 0.0007799613950047837, 0.0007780692860532595); +INSERT INTO public."doc_topic" VALUES ('WOS:000992960800001', 0.00039910945987729334, 0.0003987922446158395, 0.9984049798567777, 0.0003984934637994227, 0.0003986249749298322); +INSERT INTO public."doc_topic" VALUES ('WOS:001003375300001', 0.9984738666788316, 0.000382041641891359, 0.00038082685925636177, 0.0003827266642912504, 0.00038053815572945926); +INSERT INTO public."doc_topic" VALUES ('WOS:001018532600001', 0.0004435087177113452, 0.9982266490506383, 0.00044352597429734913, 0.00044296720685744296, 0.0004433490504955606); +INSERT INTO public."doc_topic" VALUES ('WOS:001022960300087', 0.0005312407635364054, 0.9978790306042333, 0.000529827215029666, 0.0005298356845855064, 0.0005300657326152293); +INSERT INTO public."doc_topic" VALUES ('WOS:001037681000001', 0.0004311914437947468, 0.9982743879605056, 0.000431612646463423, 0.00043107813065743045, 0.0004317298185788365); +INSERT INTO public."doc_topic" VALUES ('WOS:001069745000069', 0.0006746468423624905, 0.9973002091839284, 0.0006735541626464054, 0.0006751116987413509, 0.000676478112321493); +INSERT INTO public."doc_topic" VALUES ('WOS:001074639605055', 0.0007037862983621546, 0.9971862445728011, 0.0007029434807479838, 0.0007026448049737081, 0.0007043808431150122); +INSERT INTO public."doc_topic" VALUES ('WOS:001076120700001', 0.0003742460753544139, 0.0003757097697626854, 0.9985003312696491, 0.0003745699403400461, 0.0003751429448936522); +INSERT INTO public."doc_topic" VALUES ('WOS:001112198500001', 0.0009693677967760789, 0.0009716968007679106, 0.0009675446040330337, 0.0009671846101136369, 0.9961242061883093); +INSERT INTO public."doc_topic" VALUES ('WOS:001146958600005', 0.9972303640586183, 0.0006930571867646102, 0.0006894775561906072, 0.0006909294200371198, 0.0006961717783893197); +INSERT INTO public."doc_topic" VALUES ('WOS:001155897400007', 0.9979127742378212, 0.0005217439884720234, 0.0005217707801452451, 0.0005223161354560858, 0.0005213948581052885); +INSERT INTO public."doc_topic" VALUES ('WOS:001176385100001', 0.0005182035262846831, 0.9979270007906242, 0.0005182508987865474, 0.0005181893985253034, 0.0005183553857792388); +INSERT INTO public."doc_topic" VALUES ('WOS:001177433400106', 0.00031631622640477473, 0.0003165250475832452, 0.00031562965484411887, 0.9987356053500172, 0.0003159237211506326); +INSERT INTO public."doc_topic" VALUES ('WOS:001205972500026', 0.9987376135931388, 0.00031513274510133385, 0.00031514195332365175, 0.0003173004271012199, 0.00031481128133497736); +INSERT INTO public."doc_topic" VALUES ('WOS:001252759400003', 0.0008584783206544239, 0.0008697564406687388, 0.0008586705536272735, 0.0008580968023732638, 0.9965549978826763); +INSERT INTO public."doc_topic" VALUES ('WOS:001255413500001', 0.0005902302422863398, 0.0005897087428101981, 0.997643307750513, 0.0005882705907714255, 0.0005884826736188646); +INSERT INTO public."doc_topic" VALUES ('WOS:001270098100001', 0.00043156670269473945, 0.00043275138628968946, 0.0004305874827520741, 0.9982743734960892, 0.0004307209321743306); +INSERT INTO public."doc_topic" VALUES ('WOS:001303819200009', 0.0005068015965888083, 0.0005066499366782828, 0.0005057371133771735, 0.9979735994187633, 0.0005072119345924458); +INSERT INTO public."doc_topic" VALUES ('WOS:001316434100001', 0.00051996534397116, 0.0005238041592509783, 0.0005189253409553119, 0.9979184075317579, 0.0005188976240646335); +INSERT INTO public."doc_topic" VALUES ('WOS:001327918900019', 0.0008051066706979815, 0.9967709727853464, 0.0008066758190061648, 0.0008061503110782631, 0.0008110944138711537); +INSERT INTO public."doc_topic" VALUES ('WOS:001349394400001', 0.9979319072883605, 0.0005174629995212979, 0.0005181511651428957, 0.0005157304726173185, 0.0005167480743578922); +INSERT INTO public."doc_topic" VALUES ('WOS:001349946700074', 0.99841842213596, 0.0003955634041568681, 0.000394800044350908, 0.000395958907452258, 0.000395255508080088); +INSERT INTO public."doc_topic" VALUES ('WOS:001351523600001', 0.0004412348737845116, 0.0004422674165432014, 0.0004401190818919693, 0.00044027275824893626, 0.9982361058695314); +INSERT INTO public."doc_topic" VALUES ('WOS:001353944800001', 0.9988326708140921, 0.00029261935750683987, 0.0002917168327757019, 0.0002911943249840231, 0.0002917986706413048); +INSERT INTO public."doc_topic" VALUES ('WOS:001442947000198', 0.0005732037987763153, 0.9977104684166478, 0.0005730274420476835, 0.0005715418124196162, 0.0005717585301087685); +INSERT INTO public."doc_topic" VALUES ('WOS:001448013800110', 0.000980622329329711, 0.9960741711621783, 0.0009827124340972384, 0.000980807669532051, 0.0009816864048626274); +INSERT INTO public."doc_topic" VALUES ('WOS:001454278600154', 0.0006618819709988469, 0.0006629945524191933, 0.0006613539246383314, 0.9973514997084877, 0.0006622698434559336); +INSERT INTO public."doc_topic" VALUES ('WOS:001527154600002', 0.00036290478153040543, 0.00036381317741415443, 0.9985476142983435, 0.00036260482069049146, 0.00036306292202150097); +INSERT INTO public."doc_topic" VALUES ('WOS:001572953600062', 0.996781921248001, 0.0008077889280924505, 0.0008037708093763882, 0.000802940114637681, 0.0008035788998924225); +INSERT INTO public."doc_topic" VALUES ('WOS:001576294100050', 0.0005638381300448241, 0.0005684117483276834, 0.0005632593629826239, 0.9977396139534445, 0.0005648768052003453); +INSERT INTO public."doc_topic" VALUES ('WOS:001578728400001', 0.0005965193410946579, 0.9976139190321371, 0.000596099420933269, 0.0005961775079301374, 0.000597284697904917); +INSERT INTO public."doc_topic" VALUES ('WOS:001594525800110', 0.9982859938088139, 0.0004297884238751402, 0.00042779772523375044, 0.00042799616714427107, 0.00042842387493309894); +INSERT INTO public."doc_topic" VALUES ('WOS:001598864600001', 0.00047495600526016693, 0.00047747267204777024, 0.9980971395009065, 0.00047451207132135286, 0.00047591975046434734); +INSERT INTO public."doc_topic" VALUES ('WOS:001640856600001', 0.0003970431541177392, 0.0003952419839691485, 0.0003947970851556119, 0.9984183718408065, 0.0003945459359510019); +INSERT INTO public."doc_topic" VALUES ('WOS:A1987F823600019', 0.0005906757815970422, 0.0005889625233856038, 0.9976381189783688, 0.0005949821808083233, 0.00058726053584023); +INSERT INTO public."doc_topic" VALUES ('WOS:A1991FB61800007', 0.0020192794300937372, 0.002012726624130039, 0.9919381928762588, 0.0020186047811472326, 0.002011196288370117); +INSERT INTO public."doc_topic" VALUES ('WOS:A1996UL07400005', 0.9966573077776837, 0.0008360131646617899, 0.0008338354909534446, 0.0008380990903296164, 0.0008347444763716404); + + +-- +-- PostgreSQL database dump complete +-- + diff --git a/test/files/topic_terms.sql b/test/files/topic_terms.sql new file mode 100644 index 0000000..763ed6d --- /dev/null +++ b/test/files/topic_terms.sql @@ -0,0 +1,95 @@ +-- +-- PostgreSQL database dump +-- + +-- Dumped from database version 18.1 (Debian 18.1-1.pgdg13+2) +-- Dumped by pg_dump version 18.1 (Debian 18.1-1.pgdg13+2) + +SET statement_timeout = 0; +SET lock_timeout = 0; +SET idle_in_transaction_session_timeout = 0; +SET client_encoding = 'UTF8'; +SET standard_conforming_strings = on; +SELECT pg_catalog.set_config('search_path', '', false); +SET check_function_bodies = false; +SET xmloption = content; +SET client_min_messages = warning; +SET row_security = off; + +SET default_tablespace = ''; + +SET default_table_access_method = heap; + +-- +-- Name: table_topic_term_b6f63c93-7ed0-4320-9957-dbd3ec690624; Type: TABLE; Schema: public; Owner: postgres +-- + +CREATE TABLE public."topic_terms" ( + topic_id bigint, + term text, + weight double precision +); + + +ALTER TABLE public."topic_terms" OWNER TO postgres; + +-- +-- Data for Name: table_topic_term_b6f63c93-7ed0-4320-9957-dbd3ec690624; Type: TABLE DATA; Schema: public; Owner: postgres +-- + +INSERT INTO public."topic_terms" VALUES (0, 'bike', 59.1918460791726); +INSERT INTO public."topic_terms" VALUES (0, 'accident', 53.20056393232391); +INSERT INTO public."topic_terms" VALUES (0, 'road', 50.48598957803687); +INSERT INTO public."topic_terms" VALUES (0, 'car', 45.94439645952022); +INSERT INTO public."topic_terms" VALUES (0, 'patient', 44.20207675328453); +INSERT INTO public."topic_terms" VALUES (0, 'safety', 42.201067986182665); +INSERT INTO public."topic_terms" VALUES (0, 'injury', 39.199803727883875); +INSERT INTO public."topic_terms" VALUES (0, 'study', 36.58007376096543); +INSERT INTO public."topic_terms" VALUES (0, 'cyclist', 32.505037925761734); +INSERT INTO public."topic_terms" VALUES (0, 'factor', 29.200734580467568); +INSERT INTO public."topic_terms" VALUES (1, 'car', 87.45624160202024); +INSERT INTO public."topic_terms" VALUES (1, 'bike', 83.20743256160291); +INSERT INTO public."topic_terms" VALUES (1, 'crash', 75.20185154992718); +INSERT INTO public."topic_terms" VALUES (1, 'bicycle', 73.02224809650083); +INSERT INTO public."topic_terms" VALUES (1, 'vehicle', 70.41451826492317); +INSERT INTO public."topic_terms" VALUES (1, 'road', 65.91519655863446); +INSERT INTO public."topic_terms" VALUES (1, 'accident', 53.199110287474916); +INSERT INTO public."topic_terms" VALUES (1, 'traffic', 44.200033079638864); +INSERT INTO public."topic_terms" VALUES (1, 'datum', 43.20065093486357); +INSERT INTO public."topic_terms" VALUES (1, 'cyclist', 40.895451225540576); +INSERT INTO public."topic_terms" VALUES (2, 'accident', 52.20255139113972); +INSERT INTO public."topic_terms" VALUES (2, 'traffic', 31.201392651780292); +INSERT INTO public."topic_terms" VALUES (2, 'safety', 30.201099323866146); +INSERT INTO public."topic_terms" VALUES (2, 'car', 30.20023290922999); +INSERT INTO public."topic_terms" VALUES (2, 'study', 27.20079045712977); +INSERT INTO public."topic_terms" VALUES (2, 'bicycle', 27.20068525242122); +INSERT INTO public."topic_terms" VALUES (2, 'behavior', 25.201600668144895); +INSERT INTO public."topic_terms" VALUES (2, 'cyclist', 22.200778603045276); +INSERT INTO public."topic_terms" VALUES (2, 'model', 20.20093139254029); +INSERT INTO public."topic_terms" VALUES (2, 'factor', 20.200886378333102); +INSERT INTO public."topic_terms" VALUES (3, 'injury', 82.20278429960825); +INSERT INTO public."topic_terms" VALUES (3, 'bike', 42.19901662997125); +INSERT INTO public."topic_terms" VALUES (3, 'child', 39.20171975517025); +INSERT INTO public."topic_terms" VALUES (3, 'accident', 39.20007478047872); +INSERT INTO public."topic_terms" VALUES (3, 'car', 39.1996141202332); +INSERT INTO public."topic_terms" VALUES (3, 'risk', 36.20035598056616); +INSERT INTO public."topic_terms" VALUES (3, 'crash', 33.2005715769374); +INSERT INTO public."topic_terms" VALUES (3, 'school', 33.20041352502453); +INSERT INTO public."topic_terms" VALUES (3, 'year', 32.20110140561873); +INSERT INTO public."topic_terms" VALUES (3, 'study', 31.199843028446885); +INSERT INTO public."topic_terms" VALUES (4, 'bike', 90.20366648216675); +INSERT INTO public."topic_terms" VALUES (4, 'bicycle', 39.200579142722354); +INSERT INTO public."topic_terms" VALUES (4, 'vehicle', 37.20105190777457); +INSERT INTO public."topic_terms" VALUES (4, 'speed', 36.20171810689791); +INSERT INTO public."topic_terms" VALUES (4, 'car', 34.199514908977534); +INSERT INTO public."topic_terms" VALUES (4, 'study', 31.200051732299197); +INSERT INTO public."topic_terms" VALUES (4, 'road', 29.199793490140163); +INSERT INTO public."topic_terms" VALUES (4, 'risk', 27.2001862345917); +INSERT INTO public."topic_terms" VALUES (4, 'use', 25.200710833296004); +INSERT INTO public."topic_terms" VALUES (4, 'driver', 25.20051046618428); + + +-- +-- PostgreSQL database dump complete +-- + diff --git a/test/test_explaination_entrypoint.py b/test/test_explaination_entrypoint.py new file mode 100644 index 0000000..9f09b3d --- /dev/null +++ b/test/test_explaination_entrypoint.py @@ -0,0 +1,140 @@ +import os +import pytest +import psycopg2 +import time +import pandas as pd +import re + +from pathlib import Path +from main import topic_explaination + + +@pytest.fixture(scope="session") +def postgres_conn(): + """Wait until postgres is ready, then yield a live connection.""" + for _ in range(30): + try: + conn = psycopg2.connect( + host="127.0.0.1", + port=5432, + user="postgres", + password="postgres", + database="postgres" + ) + conn.autocommit = True + yield conn + conn.close() + return + except Exception: + time.sleep(1) + raise RuntimeError("Postgres did not start") + + +def test_topic_explanation_with_real_ollama(postgres_conn): + if not os.environ.get("OLLAMA_API_KEY"): + pytest.skip("OLLAMA_API_KEY not set") + + topic_terms_table = "topic_terms" + doc_topic_table = "doc_topic" + norm_cycling_table = "norm_cycling" + query_information_table = "query_information" + + explanations_output_table = "topic_explanations" + + sql_topic_terms = Path(__file__).parent / "files" / "topic_terms.sql" + sql_doc_topic = Path(__file__).parent / "files" / "doc_topic.sql" + sql_norm_cycling = Path(__file__).parent / "files" / "bike_norm.sql" + + with open(sql_topic_terms, "r") as f: + sql_topic_terms = f.read() + + with open(sql_doc_topic, "r") as f: + sql_doc_topic = f.read() + + with open(sql_norm_cycling, "r") as f: + sql_norm_cycling = f.read() + + cur = postgres_conn.cursor() + cur.execute(f"DROP TABLE IF EXISTS {query_information_table};") + cur.execute(f"DROP TABLE IF EXISTS {topic_terms_table};") + cur.execute(f"DROP TABLE IF EXISTS {doc_topic_table};") + cur.execute(f"DROP TABLE IF EXISTS {norm_cycling_table};") + cur.execute(f"DROP TABLE IF EXISTS {explanations_output_table};") + + cur.execute(sql_topic_terms) + cur.execute(sql_doc_topic) + cur.execute(sql_norm_cycling) + + # Manually create query information, TODO: Use real export here + cur.execute(f""" + CREATE TABLE public.{query_information_table} ( + query TEXT, + source TEXT, + created_at TEXT + ); + """) + + cur.execute(f""" + INSERT INTO public.{query_information_table} VALUES + ('ALL=(bike accident)', 'Web Of Science', '2024-01-01'); + """) + + # ------------------------------------------------------------------ + # Environment + # ------------------------------------------------------------------ + + env = { + "topic_terms_input_PG_HOST": "127.0.0.1", + "topic_terms_input_PG_PORT": "5432", + "topic_terms_input_PG_USER": "postgres", + "topic_terms_input_PG_PASS": "postgres", + "topic_terms_input_DB_TABLE": topic_terms_table, + + "query_information_input_PG_HOST": "127.0.0.1", + "query_information_input_PG_PORT": "5432", + "query_information_input_PG_USER": "postgres", + "query_information_input_PG_PASS": "postgres", + "query_information_input_DB_TABLE": query_information_table, + + "explanations_output_PG_HOST": "127.0.0.1", + "explanations_output_PG_PORT": "5432", + "explanations_output_PG_USER": "postgres", + "explanations_output_PG_PASS": "postgres", + "explanations_output_DB_TABLE": explanations_output_table, + + "MODEL_NAME": "gpt-oss:120b", + "API_KEY": os.environ.get("OLLAMA_API_KEY") + } + + for k, v in env.items(): + os.environ[k] = v + + # ------------------------------------------------------------------ + # Run entrypoint + # ------------------------------------------------------------------ + + topic_explaination() + + # ------------------------------------------------------------------ + # Validate results + # ------------------------------------------------------------------ + + cur.execute( + f"SELECT * FROM public.{explanations_output_table} ORDER BY topic_id;") + results = pd.DataFrame( + cur.fetchall(), + columns=[desc[0] for desc in cur.description] + ) + + assert len(results) == 5 + assert "topic_id" in results.columns + assert "description" in results.columns + + # Must not be empty + assert results["description"].str.len().min() > 10 + + for desc in results["description"]: + assert isinstance(desc, str) + assert len(desc) > 20 + sentences = re.findall(r"[A-Z][^.?!]*[.?!]", desc) + assert len(sentences) <= 2 From 8260b853db0ccd359277e68993dac1908a19845b Mon Sep 17 00:00:00 2001 From: Paul Kalhorn Date: Wed, 4 Feb 2026 18:37:41 -0500 Subject: [PATCH 3/4] fix: update cbc --- cbc.yaml | 38 ++++++++++++++++++++++++++++++++++++-- 1 file changed, 36 insertions(+), 2 deletions(-) diff --git a/cbc.yaml b/cbc.yaml index a98f0ce..48ce14f 100644 --- a/cbc.yaml +++ b/cbc.yaml @@ -1,9 +1,9 @@ -author: Paul Kalhorn +author: Paul Kalhorn description: Compute Block that offers topic modeling algorithms docker_image: ghcr.io/rwth-time/topic-modeling/topic-modeling entrypoints: lda_topic_modeling: - description: Sklearn LDA Topic Modeling + description: Sklearn LDA Topic Modeling envs: LEARNING_METHOD: batch MAX_ITER: 10 @@ -38,4 +38,38 @@ entrypoints: top_terms_per_topic_PG_USER: null description: A table that lists most likely terms for a topic type: pg_table + topic_explaination: + description: + envs: + MODEL_NAME: gpt-oss:120b + OLLAMA_API_KEY: '' + inputs: + query_information: + config: + query_information_input_DB_TABLE: null + query_information_input_PG_HOST: null + query_information_input_PG_PASS: null + query_information_input_PG_PORT: null + query_information_input_PG_USER: null + description: Information of the query used, must contain query, source, created_at + type: pg_table + topic_terms: + config: + topic_terms_input_DB_TABLE: null + topic_terms_input_PG_HOST: null + topic_terms_input_PG_PASS: null + topic_terms_input_PG_PORT: null + topic_terms_input_PG_USER: null + description: A table that lists most likely terms for a topic + type: pg_table + outputs: + explanations_output: + config: + explanations_output_DB_TABLE: null + explanations_output_PG_HOST: null + explanations_output_PG_PASS: null + explanations_output_PG_PORT: null + explanations_output_PG_USER: null + description: Output of the generated explanations, contains topic_id and description + type: pg_table name: Topic-Modeling From 7e0b7c543c6cd8f1a7816d4910a2a4908ed20fb2 Mon Sep 17 00:00:00 2001 From: Paul Kalhorn Date: Fri, 6 Feb 2026 10:55:34 -0500 Subject: [PATCH 4/4] fix: typo --- cbc.yaml | 4 ++-- main.py | 4 ++-- ...laination_entrypoint.py => test_explanation_entrypoint.py} | 4 ++-- 3 files changed, 6 insertions(+), 6 deletions(-) rename test/{test_explaination_entrypoint.py => test_explanation_entrypoint.py} (98%) diff --git a/cbc.yaml b/cbc.yaml index 48ce14f..7a61d14 100644 --- a/cbc.yaml +++ b/cbc.yaml @@ -38,8 +38,8 @@ entrypoints: top_terms_per_topic_PG_USER: null description: A table that lists most likely terms for a topic type: pg_table - topic_explaination: - description: + topic_explanation: + description: Explains the topics using the query and top terms envs: MODEL_NAME: gpt-oss:120b OLLAMA_API_KEY: '' diff --git a/main.py b/main.py index 1b464ab..04ae7c5 100644 --- a/main.py +++ b/main.py @@ -56,7 +56,7 @@ class QueryInformationInput(PostgresSettings, InputSettings): Our TopicExplaination needs some kind of information about the actual query executed,this query information includes the query and the source - Looking like: query, source + Looking like: query, source, created_at """ __identifier__ = "query_information_input" @@ -142,7 +142,7 @@ def lda_topic_modeling(settings): @entrypoint(TopicExplanation) -def topic_explaination(settings): +def topic_explanation(settings): logger.info("Starting topic explaination...") logging.info("Querying topic terms from db...") diff --git a/test/test_explaination_entrypoint.py b/test/test_explanation_entrypoint.py similarity index 98% rename from test/test_explaination_entrypoint.py rename to test/test_explanation_entrypoint.py index 9f09b3d..930e7b5 100644 --- a/test/test_explaination_entrypoint.py +++ b/test/test_explanation_entrypoint.py @@ -6,7 +6,7 @@ import re from pathlib import Path -from main import topic_explaination +from main import topic_explanation @pytest.fixture(scope="session") @@ -113,7 +113,7 @@ def test_topic_explanation_with_real_ollama(postgres_conn): # Run entrypoint # ------------------------------------------------------------------ - topic_explaination() + topic_explanation() # ------------------------------------------------------------------ # Validate results