diff --git a/router/README.md b/router/README.md
new file mode 100644
index 0000000..84ac0a8
--- /dev/null
+++ b/router/README.md
@@ -0,0 +1,40 @@
+# Router Module
+
+This directory contains scripts and utilities for routing user queries to the appropriate legal documents.
+
+## Overview
+
+- `query_router.py`: Main script for routing semantic queries to the appropriate collections using OpenAI's API.
+- `router_prompt.py`: Contains the prompt template used to determine relevant laws based on user queries.
+
+## Setup
+
+Refer to the main README for details on setting up the environment and dependencies using Poetry.
+
+### Environment Configuration
+
+Before running the scripts, ensure you have set the necessary environment variables in your `.env` file:
+```yaml
+OPENAI_API_KEY=
+```
+
+## Scripts
+
+- `query_router.py` - This script routes a semantic query to the appropriate collections using OpenAI's API.
+- `router_prompt.py` - This script contains the prompt template used to determine relevant laws based on user queries.
+
+## Usage
+
+To route a query and get the relevant laws, use the **semantic_query_router** function from `query_router.py`. Make sure to format your query and prompt correctly and provide the required API key in the .env file.
+
+```python
+from openai import OpenAI
+from router.query_router import semantic_query_router
+from router.router_prompt import ROUTER_PROMPT
+
+client = OpenAI(api_key='YOUR_OPENAI_API_KEY')
+
+query = "What are the conditions for terminating an employment contract?"
+response = semantic_query_router(client, query, ROUTER_PROMP)
+print(response)
+```
\ No newline at end of file
diff --git a/router/collection_centroids.json b/router/collection_centroids.json
deleted file mode 100644
index ee8c852..0000000
--- a/router/collection_centroids.json
+++ /dev/null
@@ -1,7692 +0,0 @@
-{
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- -0.003949127967773437,
- 0.03766617998789062,
- 0.022561988047773446,
- -0.012541947032890624,
- -0.01840111636542969,
- 0.009479620631093744,
- -0.014944382003515623,
- -0.016131899946617182,
- 0.017041058714660166,
- -0.010116272029062503,
- 0.007207765795671873,
- -0.020422802001113283,
- 0.015357530986523446,
- 0.02798127542265625,
- 0.02214191741701561,
- -0.0008622926781640626,
- 0.007434600871739843,
- -0.006559177211535159,
- 0.0035641961637421877,
- -0.009428011538585936,
- 0.0048546480850000006,
- 0.011619971709945314,
- 0.009649804699765618,
- -0.0016785368512890625,
- 0.007836762226097662,
- 0.03494093771093749,
- 0.0019977567628124996,
- 0.001347426200820312,
- 0.004381050907449218,
- 0.008591519130777346,
- 0.0026630002446093753,
- -0.004912667212578127,
- 0.0004699871279316408,
- -0.00939041298910156,
- -0.016050112575250006,
- -0.0052523126883984375,
- -0.011127158862105472,
- 0.019305278023281254,
- 0.017965682689804696,
- -0.00780626898951563,
- 0.0034548387083749997,
- 0.014250207085937494,
- -0.0035430361347515635,
- 0.0018707543213671866,
- 0.004162972887500001,
- 0.003387083043042579,
- -0.012662332113515619,
- -0.014768752472851558,
- -0.02740603597070312,
- -0.009712132611437503,
- -0.004180196381936329,
- 0.016907890950351577,
- -0.007955579812421882,
- 0.009532738729339841,
- 0.004837923314726565,
- -0.00989323250183593,
- 0.015016958530820317,
- 0.01578883888675782,
- -0.024779348274492186,
- 0.0244331908488828,
- 0.02274097113597656,
- -0.007638400372187498,
- 0.027180088020703132,
- -0.025684585125613273,
- -0.015066079199929692,
- 0.011447327845675781,
- -0.020315377228124997,
- 0.0012742214558593746,
- 0.021396482178671905,
- 0.008679716510757817,
- 0.008959503475683604,
- -0.003774268502734374,
- 0.0018165257732421858,
- 0.005719902068851563,
- -0.004054806729062499,
- -0.00817379893378906,
- 0.02798396401023437,
- -0.006345091628750001,
- 0.014660699204257815,
- -0.02693461845097657,
- 0.003517874380039063,
- -0.009048889528281251,
- 0.005000057698550779,
- 0.009136285388945313,
- 0.009011534357109378,
- -0.01347845586007813,
- 0.014437898097800784,
- 0.0031833894187343753,
- -0.005593769032460935,
- 0.0036563189155000026,
- 0.014031661375488278,
- 0.01926186501613282,
- 0.00038140250519531243,
- 0.007122492201765625,
- 0.03720169576523439,
- -0.00918398804083984,
- 0.0027441810610624997,
- 0.025455622854921874,
- -0.0266934724496875,
- -0.006295805205054687,
- -0.0017581763180859365,
- 0.006509286970376958,
- 0.0018265677752734358,
- -0.015645206555468753,
- 0.0010994621933593744,
- 0.01780034897097656,
- -0.005542860695152344,
- -0.013608229061523433,
- 0.018796027519062505,
- -0.010807874053691406,
- -0.006406312150515622,
- -0.0004602978575023439,
- 0.002568250449570312,
- 0.01673808414429689,
- -0.02086130606472657,
- 0.003999285643878904,
- -0.009930727786517579,
- -0.010098818474816416,
- 0.0034328602209843734,
- 0.02286010482593749,
- -0.015356466133125,
- -0.004493441818437499,
- 0.017525820043156247,
- 0.026122144906171877,
- -0.00457880121967968,
- 0.008750743813335933,
- -0.010517507725042967
- ]
-}
\ No newline at end of file
diff --git a/router/query_router.py b/router/query_router.py
index e83edc3..df9a86a 100644
--- a/router/query_router.py
+++ b/router/query_router.py
@@ -1,26 +1,10 @@
import json
-from typing import Dict, List
+from typing import List
-import numpy as np
from langfuse.decorators import observe
from openai import OpenAI
-def rout_query(centroids: Dict[str, List], query_embedding: List) -> str:
- centroids = list(centroids.items())
- centroids_np = np.array([value for key, value in centroids])
- query_np = np.array(query_embedding)
-
- norm_query = np.linalg.norm(query_np)
- norm_centroids = np.linalg.norm(centroids_np, axis=1)
-
- cosine_similarities = np.dot(centroids_np, query_np) / (norm_centroids * norm_query)
- max_index = np.argmax(cosine_similarities)
-
- collection = centroids[max_index][0]
- return collection
-
-
@observe()
def semantic_query_router(
client: OpenAI,
@@ -29,11 +13,26 @@ def semantic_query_router(
temperature: float,
model: str = "gpt-3.5-turbo",
) -> List[str]:
+ """
+ Routes a semantic query to the appropriate collections using OpenAI's API.
+
+ Args:
+ client (OpenAI): The OpenAI client instance.
+ query (str): The query string to be routed.
+ prompt (str): The prompt template to be used for the query.
+ temperature (float): The temperature setting for the model's response.
+ model (str, optional): The model to be used. Defaults to "gpt-3.5-turbo".
+
+ Returns:
+ List[str]: A list of collections that are relevant to the query.
+ """
+ # Create the completion request to the OpenAI API
response = client.chat.completions.create(
model=model,
response_format={"type": "json_object"},
messages=[{"role": "system", "content": prompt.format(query=query)}],
temperature=temperature,
)
+ # Parse the response to extract the collections
collections = json.loads(response.choices[0].message.content)["response"]
return collections
diff --git a/router/router-dev.ipynb b/router/router-dev.ipynb
deleted file mode 100644
index 7df493e..0000000
--- a/router/router-dev.ipynb
+++ /dev/null
@@ -1,545 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "import json\n",
- "import os\n",
- "from pathlib import Path\n",
- "from typing import Dict, List\n",
- "\n",
- "import numpy as np\n",
- "from loguru import logger\n",
- "from tqdm.auto import tqdm"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Calculate centroids for all collections"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {},
- "outputs": [],
- "source": [
- "embeddings_dir = Path(\"../database/embeddings\")\n",
- "embeddings_paths = [\n",
- " path for path in list(embeddings_dir.iterdir()) if path.suffix == \".jsonl\"\n",
- "]\n",
- "if not embeddings_dir.exists():\n",
- " logger.error(f\"Embeddings dir not found: {embeddings_dir}.\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [],
- "source": [
- "def load_embeddings(path: Path) -> List:\n",
- " if not path.exists():\n",
- " logger.error(f\"File does not exist: {path}.\")\n",
- "\n",
- " with open(path, \"r\", encoding=\"utf-8\") as file:\n",
- " embeddings = []\n",
- " for line in file:\n",
- " embeddings.append(json.loads(line)[1][\"data\"][0][\"embedding\"])\n",
- "\n",
- " return embeddings"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 34,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "7d0747d4d8964a32aefa93fcef21da77",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Calculating centroids: 0%| | 0/5 [00:00 str:\n",
- " centroids = list(centroids.items())\n",
- " centroids_np = np.array([value for key, value in centroids])\n",
- " query_np = np.array(query_embedding)\n",
- "\n",
- " norm_query = np.linalg.norm(query_np)\n",
- " norm_centroids = np.linalg.norm(centroids_np, axis=1)\n",
- "\n",
- " cosine_similarities = np.dot(centroids_np, query_np) / (norm_centroids * norm_query)\n",
- " max_index = np.argmax(cosine_similarities)\n",
- "\n",
- " collection = centroids[max_index][0]\n",
- " return collection"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 47,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "'porodicni_zakon'"
- ]
- },
- "execution_count": 47,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "rout_query(\n",
- " centroids=collection_centroids,\n",
- " query_embedding=collection_centroids[\"porodicni_zakon\"],\n",
- ")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Query Router with Qdrant"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Load centroids"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [],
- "source": [
- "centroid_path = Path(\"./collection_centroids.json\")\n",
- "with open(centroid_path, \"r\", encoding=\"utf-8\") as file:\n",
- " centroids = json.loads(file.read())"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Set path to be able to load Qdrant Utils from database folder"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "logger.info(f\"Current working directory: {os.getcwd()}\")\n",
- "os.chdir(\"../\")\n",
- "logger.info(f\"Changed working directory: {os.getcwd()}\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [],
- "source": [
- "from openai import OpenAI\n",
- "from qdrant_client import QdrantClient\n",
- "from qdrant_client.http.models import Distance, PointStruct\n",
- "from tqdm.auto import tqdm\n",
- "\n",
- "from database.utils import (\n",
- " create_collection,\n",
- " delete_collection,\n",
- " embed_text,\n",
- " get_collection_info,\n",
- " get_count,\n",
- " search,\n",
- " upsert,\n",
- ")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "qdrant_client = QdrantClient(\n",
- " url=os.environ[\"QDRANT_CLUSTER_URL\"],\n",
- " api_key=os.environ[\"QDRANT_API_KEY\"],\n",
- ")\n",
- "\n",
- "openai_client = OpenAI(api_key=os.environ[\"OPENAI_API_KEY\"])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Create data points for Qdrant"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "points = []\n",
- "\n",
- "for id, (law_title, centroid) in enumerate(centroids.items()):\n",
- " points.append(\n",
- " PointStruct(\n",
- " id=id,\n",
- " vector=centroid,\n",
- " payload={\"law_title\": law_title},\n",
- " )\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "We are using EUCLID distance because it is much more intuitive and better to set threshold between vectors for real distance metric.
\n",
- "COSINE metric is not really distance, it is an angle between vectors and it can be harder to interpret and set threshold value."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\u001b[32m2024-04-26 22:40:22.450\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36mdatabase.utils\u001b[0m:\u001b[36mcreate_collection\u001b[0m:\u001b[36m31\u001b[0m - \u001b[1mCreating collection: router with vector size: 1536.\u001b[0m\n"
- ]
- }
- ],
- "source": [
- "collection_name = \"router\"\n",
- "create_collection(client=qdrant_client, name=collection_name, distance=Distance.EUCLID)\n",
- "upsert(client=qdrant_client, collection=collection_name, points=points)\n",
- "if get_count(client=qdrant_client, collection=collection_name) != len(\n",
- " centroids.items()\n",
- "):\n",
- " logger.error(\"Router is missing centroids.\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'status': ,\n",
- " 'optimizer_status': ,\n",
- " 'vectors_count': 5,\n",
- " 'indexed_vectors_count': 0,\n",
- " 'points_count': 5,\n",
- " 'segments_count': 2,\n",
- " 'config': {'params': {'vectors': {'size': 1536,\n",
- " 'distance': ,\n",
- " 'hnsw_config': None,\n",
- " 'quantization_config': None,\n",
- " 'on_disk': None},\n",
- " 'shard_number': 1,\n",
- " 'sharding_method': None,\n",
- " 'replication_factor': 1,\n",
- " 'write_consistency_factor': 1,\n",
- " 'read_fan_out_factor': None,\n",
- " 'on_disk_payload': True,\n",
- " 'sparse_vectors': None},\n",
- " 'hnsw_config': {'m': 16,\n",
- " 'ef_construct': 100,\n",
- " 'full_scan_threshold': 10000,\n",
- " 'max_indexing_threads': 0,\n",
- " 'on_disk': False,\n",
- " 'payload_m': None},\n",
- " 'optimizer_config': {'deleted_threshold': 0.2,\n",
- " 'vacuum_min_vector_number': 1000,\n",
- " 'default_segment_number': 0,\n",
- " 'max_segment_size': None,\n",
- " 'memmap_threshold': None,\n",
- " 'indexing_threshold': 20000,\n",
- " 'flush_interval_sec': 5,\n",
- " 'max_optimization_threads': None},\n",
- " 'wal_config': {'wal_capacity_mb': 32, 'wal_segments_ahead': 0},\n",
- " 'quantization_config': None},\n",
- " 'payload_schema': {}}"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "get_collection_info(client=qdrant_client, collection=collection_name)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Search router"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "def query_router_qdrant(\n",
- " query_embedding: List, collection_name: str = \"router\", threshold: float = 0.2\n",
- ") -> str:\n",
- " response = search(\n",
- " client=qdrant_client, collection=collection_name, query_vector=query_embedding\n",
- " )\n",
- " collection = response\n",
- " return collection"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[ScoredPoint(id=1, version=0, score=0.8214849, payload={'law_title': 'zakon_o_radu'}, vector=None, shard_key=None),\n",
- " ScoredPoint(id=0, version=0, score=0.84445184, payload={'law_title': 'porodicni_zakon'}, vector=None, shard_key=None),\n",
- " ScoredPoint(id=4, version=0, score=0.85869735, payload={'law_title': 'zakon-o-porezu-na-dohodak-gradjana'}, vector=None, shard_key=None),\n",
- " ScoredPoint(id=3, version=0, score=0.90343463, payload={'law_title': 'zakon_o_zastiti_potrosaca'}, vector=None, shard_key=None),\n",
- " ScoredPoint(id=2, version=0, score=0.9431141, payload={'law_title': 'zakon_o_zastiti_podataka_o_licnosti'}, vector=None, shard_key=None)]"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# query = \"da li sajt sme da mi salje newsletter ako se nisam prijavila?\"\n",
- "query = \"koliko godisnjih dana imam po zakonu?\"\n",
- "query_embedding = (\n",
- " embed_text(client=openai_client, text=query, model=\"text-embedding-3-small\")\n",
- " .data[0]\n",
- " .embedding\n",
- ")\n",
- "query_router_qdrant(query_embedding)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Semantic router using GPT"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [],
- "source": [
- "prompt = \"\"\"\n",
- "Tvoj zadatak je da na osnovu datog pitanja korisnika odlucis koji zakon ili zakoni su potrebni da bi se odgovorilo na korisnikovo pitanje.\n",
- "Ponudjeni zakoni i njihova objasnjenja su sledeci:\n",
- "- zakon_o_radu\n",
- " - Zakon o radu Republike Srbije reguliše radne odnose između zaposlenih i poslodavaca. Definiše prava i obaveze obe strane, uključujući radno vreme, odmore i druga odsustva i uslove za otkaz ugovora o radu. Zakon takođe obuhvata pravila vezana za ugovore o radu, minimalnu zaradu, kao i mere zaštite na radu. Osim toga, predviđa mehanizme za rešavanje radnih sporova.\n",
- "- zakon_o_porezu_na_dohodak_gradjana\n",
- " - Zakon o porezu na dohodak građana reguliše način oporezivanja porezom na dohodak građana, u šta spadaju zarada, prihodi od samostalnih delatnosti, prihodi od kapitala, nepokretnosti i slično. Zakon detaljno opisuje koji sve prihodi su oporezivi ovim porezom, kao i poreske stope, osnovice, određena poreska oslobođenja i olakšice za određene kategorije građana.\n",
- "- zakon_o_zastiti_podataka_o_licnosti\n",
- " - Zakon o zaštiti podataka o ličnosti štiti prava građana na privatnost njihovih ličnih podataka. Obavezuje sve organizacije koje obrađuju lične podatke da to čine transparentno, zakonito i u skladu sa definisanim svrhama. Zakon definiše prava lica na pristup, ispravku, brisanje i prenos svojih podataka o ličnosti. Takođe, ustanovljava Poverenika za informacije od javnog značaja i zaštitu podataka o ličnosti kao regulatorno telo koje nadzire primenu zakona.\n",
- "- zakon_o_zastiti_potrosaca\n",
- " - Zakon o zaštiti potrošača osigurava da potrošači u Srbiji imaju prava na sigurnost i kvalitet proizvoda i usluga. Zakon propisuje obaveze trgovaca u pogledu pravilnog informisanja potrošača o proizvodima, uslugama, cenama i pravu na reklamaciju. Takođe, uključuje prava potrošača na odustanak od kupovine unutar određenog roka i prava u slučaju neispravnosti proizvoda. \n",
- "- porodicni_zakon\n",
- " - Porodični zakon reguliše pravne odnose unutar porodice, uključujući brak, roditeljstvo, starateljstvo, hraniteljstvo i usvojenje. Zakon definiše prava i obaveze bračnih partnera, kao i prava dece i roditeljske odgovornosti. Takođe se bavi pitanjima nasleđivanja i alimentacije. \n",
- "\n",
- "**FORMAT ODGOVORA:**\n",
- "- Odgovor vratiti u JSON formatu.\n",
- "- Odgovor treba da sadrzi samo JSON output, bez dodataka.\n",
- "- Odgovor mora da bude string koji moze da se ucita uz pmoc komande json.loads().\n",
- "- Imena zakona mogu biti samo sledeca: zakon_o_radu, zakon_o_porezu_na_dohodak_gradjana, zakon_o_zastiti_podataka_o_licnosti, zakon_o_zastiti_potrosaca, porodicni_zakon.\n",
- "- Jedno pitanje korisnika moze da se odnosi na vise zakona.\n",
- "- Ukoliko mislis da zakon odgovara korisnikovom pitanju ali nisi 100% siguran onda ga svakako stavi u odgovor.\n",
- "- Ukoliko korisnikovo pitanje ne odgovara ni jedno zakonu vrati genericki string: \"nema_zakona\".\n",
- "- Zakone uvek moras vracati kao listu stringova bez obzira da li ih je 1 ili vise.\n",
- "- Primer JSON odgovora:\n",
- "\n",
- "{{\n",
- " response: [\"ime_zakona\"]\n",
- "}}\n",
- "\n",
- "**PITANJE KORISINKA:**\n",
- "{query}\n",
- "\"\"\""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [],
- "source": [
- "def semantic_query_router(\n",
- " query: str, prompt: str, temperature: float, model: str = \"gpt-3.5-turbo\"\n",
- ") -> List[str]:\n",
- " response = openai_client.chat.completions.create(\n",
- " model=model,\n",
- " response_format={\"type\": \"json_object\"},\n",
- " messages=[{\"role\": \"system\", \"content\": prompt.format(query=query)}],\n",
- " temperature=temperature,\n",
- " )\n",
- " collections = json.loads(response.choices[0].message.content)[\"response\"]\n",
- " return collections"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "CPU times: user 2 µs, sys: 0 ns, total: 2 µs\n",
- "Wall time: 4.05 µs\n"
- ]
- }
- ],
- "source": [
- "%time\n",
- "# query = \"da li sajt sme da mi salje newsletter ako se nisam prijavila?\"\n",
- "query = \"koliko dana slobodnog imam za porodisljko odsustvo?\"\n",
- "\n",
- "response = semantic_query_router(query=query, prompt=prompt, temperature=0.0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['zakon_o_radu']"
- ]
- },
- "execution_count": 30,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "response"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "venv",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.10.13"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/router/router_prompt.py b/router/router_prompt.py
index 8adb1d7..322ad45 100644
--- a/router/router_prompt.py
+++ b/router/router_prompt.py
@@ -8,21 +8,18 @@
- zakon_o_zastiti_podataka_o_licnosti
- Zakon o zaštiti podataka o ličnosti štiti prava građana na privatnost njihovih ličnih podataka. Obavezuje sve organizacije koje obrađuju lične podatke da to čine transparentno, zakonito i u skladu sa definisanim svrhama. Zakon definiše prava lica na pristup, ispravku, brisanje i prenos svojih podataka o ličnosti. Takođe, ustanovljava Poverenika za informacije od javnog značaja i zaštitu podataka o ličnosti kao regulatorno telo koje nadzire primenu zakona.
- zakon_o_zastiti_potrosaca
- - Zakon o zaštiti potrošača osigurava da potrošači u Srbiji imaju prava na sigurnost i kvalitet proizvoda i usluga. Zakon propisuje obaveze trgovaca u pogledu pravilnog informisanja potrošača o proizvodima, uslugama, cenama i pravu na reklamaciju. Takođe, uključuje prava potrošača na odustanak od kupovine unutar određenog roka i prava u slučaju neispravnosti proizvoda.
+ - Zakon o zaštiti potrošača osigurava da potrošači u Srbiji imaju prava na sigurnost i kvalitet proizvoda i usluga. Zakon propisuje obaveze trgovaca u pogledu pravilnog informisanja potrošača o proizvodima, uslugama, cenama i pravu na reklamaciju. Takođe, uključuje prava potrošača na odustanak od kupovine unutar određenog roka i prava u slučaju neispravnosti proizvoda kao i prava koja su vezana za ugovore na daljinu.
- porodicni_zakon
- Porodični zakon reguliše pravne odnose unutar porodice, uključujući brak, roditeljstvo, starateljstvo, hraniteljstvo i usvojenje. Zakon definiše prava i obaveze bračnih partnera, kao i prava dece i roditeljske odgovornosti. Takođe se bavi pitanjima nasleđivanja i alimentacije.
- nema_zakona
- Korisnikovo pitanje ne odgovara ni jednom zakonu.
**FORMAT ODGOVORA:**
-- Odgovor vratiti u JSON formatu.
-- Odgovor treba da sadrzi samo JSON output, bez dodataka.
-- Odgovor mora da bude string koji moze da se ucita uz pmoc komande json.loads().
+- Odgovor vratiti u JSON formatu koji moze da se učita sa json.loads().
- Imena zakona mogu biti samo sledeca: zakon_o_radu, zakon_o_porezu_na_dohodak_gradjana, zakon_o_zastiti_podataka_o_licnosti, zakon_o_zastiti_potrosaca, porodicni_zakon, nema_zakona.
- Jedno pitanje korisnika moze da se odnosi na vise zakona.
-- Ukoliko mislis da zakon odgovara korisnikovom pitanju ali nisi 100% siguran onda ga svakako stavi u odgovor.
+- Vrati zakone koji mogu da pomognu prilikom generisanja odgovora.
- Ukoliko korisnikovo pitanje ne odgovara ni jednom zakonu vrati listu sa generickim stringom: ["nema_zakona"].
-- Zakone uvek moras vracati kao listu stringova bez obzira da li ih je 1 ili vise.
- Primer JSON odgovora:
{{
diff --git a/tests/test_router.py b/tests/test_router.py
index 9ae2ccc..373edc7 100644
--- a/tests/test_router.py
+++ b/tests/test_router.py
@@ -3,11 +3,11 @@
from pathlib import Path
from typing import List
-import yaml
from openai import OpenAI
from router.query_router import semantic_query_router
from router.router_prompt import ROUTER_PROMPT
+from utils import load_config_from_yaml
class RouterTest(unittest.TestCase):
@@ -15,8 +15,7 @@ class RouterTest(unittest.TestCase):
def setUp(self) -> None:
# Load configuration
config_path = Path("./config.yaml")
- with config_path.open("r") as file:
- self.config = yaml.safe_load(file)
+ self.config = load_config_from_yaml(yaml_file_path=config_path)
# Initialize OpenAI client
self.openai_client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
@@ -30,6 +29,7 @@ def setUp(self) -> None:
(
"Kako da zaštitim svoje podatke na internetu?",
["zakon_o_zastiti_podataka_o_licnosti"],
+ # ["zakon_o_zastiti_podataka_o_licnosti", "zakon_o_zastiti_potrosaca"],
),
(
"Šta su moja prava kao potrošača i lica na koje se podaci odnose kada kupujem online?",
@@ -46,6 +46,7 @@ def setUp(self) -> None:
(
"How can I protect my data online?",
["zakon_o_zastiti_podataka_o_licnosti"],
+ # ["zakon_o_zastiti_podataka_o_licnosti", "zakon_o_zastiti_potrosaca"],
),
(
"What are my rights as a consumer and data subject when shopping online?",
@@ -78,7 +79,8 @@ def _route_query(self, query) -> List[str]:
client=self.openai_client,
query=query,
prompt=ROUTER_PROMPT,
- temperature=self.config["openai"]["gpt_model"]["temperature"],
+ model=self.config.openai.router.model,
+ temperature=self.config.openai.router.temperature,
)
diff --git a/utils.py b/utils.py
new file mode 100644
index 0000000..6ea4683
--- /dev/null
+++ b/utils.py
@@ -0,0 +1,34 @@
+import yaml
+from pydantic import BaseModel
+
+
+class RouterConfig(BaseModel):
+ model: str
+ temperature: float
+
+
+class ChatConfig(BaseModel):
+ model: str
+ temperature: float
+ max_conversation: int
+
+
+class EmbeddingsConfig(BaseModel):
+ model: str
+ dimensions: int
+
+
+class OpenAIConfig(BaseModel):
+ embeddings: EmbeddingsConfig
+ chat: ChatConfig
+ router: RouterConfig
+
+
+class Config(BaseModel):
+ openai: OpenAIConfig
+
+
+def load_config_from_yaml(yaml_file_path: str) -> Config:
+ with open(yaml_file_path, "r") as file:
+ yaml_content = yaml.safe_load(file)
+ return Config(**yaml_content)