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fix: replace dspy with official version and add custom dataset

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Boris Arzentar 2024-04-21 16:11:51 +02:00
parent b2aecf833b
commit bdd664a2aa
12 changed files with 423 additions and 1167 deletions
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1
cognee/modules/cognify/__init__.py
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84
cognee/modules/cognify/dataset.py Normal file
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@ -0,0 +1,84 @@
import random
from datasets import load_dataset
from dspy.datasets.dataset import Dataset
class HotPotQA(Dataset):
def __init__(self, *args, only_hard_examples=True, keep_details='dev_titles', unofficial_dev=True, **kwargs) -> None:
super().__init__(*args, **kwargs)
assert only_hard_examples, "Care must be taken when adding support for easy examples." \
"Dev must be all hard to match official dev, but training can be flexible."
hf_official_train = load_dataset("hotpot_qa", 'fullwiki', split='train')
hf_official_dev = load_dataset("hotpot_qa", 'fullwiki', split='validation')
official_train = []
for raw_example in hf_official_train:
if raw_example['level'] == 'hard':
if keep_details is True:
keys = ['id', 'question', 'answer', 'type', 'supporting_facts', 'context']
elif keep_details == 'dev_titles':
keys = ['question', 'answer', 'supporting_facts']
else:
keys = ['question', 'answer']
example = {k: raw_example[k] for k in keys}
if 'supporting_facts' in example:
example['gold_titles'] = set(example['supporting_facts']['title'])
del example['supporting_facts']
official_train.append(example)
rng = random.Random(0)
rng.shuffle(official_train)
self._train = official_train[:len(official_train)*75//100]
if unofficial_dev:
self._dev = official_train[len(official_train)*75//100:]
else:
self._dev = None
for example in self._train:
if keep_details == 'dev_titles':
del example['gold_titles']
test = []
for raw_example in hf_official_dev:
assert raw_example['level'] == 'hard'
example = {k: raw_example[k] for k in ['id', 'question', 'answer', 'type', 'supporting_facts']}
if 'supporting_facts' in example:
example['gold_titles'] = set(example['supporting_facts']['title'])
del example['supporting_facts']
test.append(example)
self._test = test
if __name__ == '__main__':
from dsp.utils import dotdict
data_args = dotdict(train_seed=1, train_size=16, eval_seed=2023, dev_size=200*5, test_size=0)
dataset = HotPotQA(**data_args)
print(dataset)
print(dataset.train[0].question)
print(dataset.train[15].question)
print(len(dataset.train), len(dataset.dev), len(dataset.test))
print(dataset.dev[0].question)
print(dataset.dev[340].question)
print(dataset.dev[937].question)
"""
What was the population of the city where Woodward Avenue ends in 2010?
Where did the star , who is also an executive producer, of the Mick begin her carrer?
16 1000 0
Both London and German have seen attacks during war, there was one specific type of attack that Germany called the blitz, what did London call a similar attack?
Pre-Madonna was a collection of demos by the singer who was a leading presence during the emergence of what network?
Alan Mills composed the classic folk song that tells the story of what?
"""

2
cognee/modules/cognify/evaluate.py
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@ -1,6 +1,5 @@
import dsp
import dspy
from dspy.datasets import HotPotQA
from dspy.evaluate.evaluate import Evaluate
from dspy.primitives.example import Example
from cognee.modules.data.extraction.knowledge_graph.extract_knowledge_graph_module import ExtractKnowledgeGraph
@ -8,6 +7,7 @@ from cognee.root_dir import get_absolute_path
from cognee.config import Config
from cognee.shared.data_models import Answer
from cognee.infrastructure.llm.get_llm_client import get_llm_client
from cognee.modules.cognify.dataset import HotPotQA
config = Config()
config.load()

27
cognee/modules/cognify/extract_categories.py
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@ -1,27 +0,0 @@
from typing import List
import dspy
from cognee.shared.data_models import TextContent
class CategoriesFromText(dspy.Signature):
"""
Instructions:
You are a classification engine and should classify content.
Make sure to use one of the existing classification options nad not invent your own.
"""
text: str = dspy.InputField()
categories: List[TextContent] = dspy.OutputField(desc = "JSON array of categories in which the text belongs.")
class ExtractCategories(dspy.Module):
def __init__(self, lm = dspy.OpenAI(
model = "gpt-3.5-turbo",
max_tokens = 4096
)):
super().__init__()
self.lm = lm
self.extract_categories_from_text = dspy.TypedPredictor(CategoriesFromText)
def forward(self, text: str):
with dspy.context(lm = self.lm):
return self.extract_categories_from_text(text = text).categories

35
cognee/modules/cognify/extract_cognitive_layers.py
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@ -1,35 +0,0 @@
from typing import List
import dspy
class LayersFromText(dspy.Signature):
"""
Instructions:
You are tasked with analyzing data in order to extract meaningful layers of information
that will contribute to constructing a detailed multilayer network or knowledge graph.
Consider the unique characteristics and inherent properties of the data at hand.
VERY IMPORTANT:
The context and domain you are working in is defined by the "category" input.
The content category, defined by "category" input, should play a major role in how you decompose into layers.
"""
text: str = dspy.InputField()
text_category: str = dspy.InputField(desc = "Category in which the text belongs.")
cognitive_layers: List[str] = dspy.OutputField(desc = "JSON array of cognitive layers.")
class ExtractCognitiveLayers(dspy.Module):
def __init__(self, lm = dspy.OpenAI(
model = "gpt-3.5-turbo",
max_tokens = 4096
)):
super().__init__()
self.lm = lm
self.extract_cognitive_layers_from_text = dspy.TypedChainOfThought(LayersFromText)
def forward(self, text: str, category: str):
with dspy.context(lm = self.lm):
return self.extract_cognitive_layers_from_text(
text = text,
text_category = category
).cognitive_layers

91
cognee/modules/cognify/test.py Normal file
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@ -0,0 +1,91 @@
import dspy
from cognee.modules.data.extraction.knowledge_graph.extract_knowledge_graph_module import ExtractKnowledgeGraph
from cognee.root_dir import get_absolute_path
from cognee.config import Config
config = Config()
config.load()
def run():
gpt4 = dspy.OpenAI(model = config.openai_model, api_key = config.openai_key, model_type = "chat", max_tokens = 4096)
compiled_extract_knowledge_graph = ExtractKnowledgeGraph(lm = gpt4)
compiled_extract_knowledge_graph.load(get_absolute_path("./programs/extract_knowledge_graph/extract_knowledge_graph.json"))
text = """The 1985 FA Charity Shield (also known as the General Motors FA
Charity Shield for sponsorship reasons) was the 63rd FA Charity Shield,
an annual football match played between the winners of the previous
season's First Division and FA Cup competitions. The match was played on
10 August 1985 at Wembley Stadium and contested by Everton,
who had won the 1984\u201385 First Division, and Manchester United,
who had won the 1984\u201385 FA Cup. Everton won 2\u20130 with goals from
Trevor Steven and Adrian Heath. Trevor Steven put Everton into the lead
when he swept home from six yards after a cross from the left in the first half.
The second goal came in the second half when Manchester United goalkeeper
Gary Bailey dropped a cross from the left to allow Adrian Heath to tip the
ball past him into the left corner of the net.\r\nThe 1995 FA Charity Shield
(also known as the Littlewoods FA Charity Shield for sponsorship reasons) was the
73rd FA Charity Shield, an annual football match played between the winners of
the previous season's Premier League and FA Cup competitions. The match was
played on 13 August 1995 at Wembley Stadium and contested by Blackburn Rovers,
who had won the Premier League and FA Cup winners Everton. It was Blackburn's
second successive Charity Shield appearance, while Everton were appearing in
their eleventh and their first since 1987. Everton won the match 1\u20130
with a goal from Vinny Samways when he caught Tim Flowers off his line and
lifted the ball over him from the left of the penalty area and into the right
corner of the net. Dave Watson lifted the trophy for Everton.\r\nThe 1972 FA
Charity Shield was contested between Manchester City and Aston Villa.\r\nThe
1997 FA Charity Shield (known as the Littlewoods FA Charity Shield for
sponsorship reasons) was the 75th FA Charity Shield, an annual football match
played between the winners of the previous season's Premier League and
FA Cup competitions. The match was played on 3 August 1997 at Wembley Stadium
and contested by Manchester United, who had won the 1996\u201397 FA Premier League,
and Chelsea, who had won the 1996\u201397 FA Cup. Manchester United won the match
4\u20132 on penalties after the match had finished at 1\u20131 after 90 minutes.
\r\nThe 1956 FA Charity Shield was the 34th FA Charity Shield, an annual football
match held between the winners of the previous season's Football League and
FA Cup competitions. The match was contested by Manchester United, who had won
the 1955\u201356 Football League, and Manchester City, who had won the
1955\u201356 FA Cup, at Maine Road, Manchester, on 24 October 1956. Manchester
United won the match 1\u20130, Dennis Viollet scoring the winning goal.
Manchester United goalkeeper David Gaskell made his debut for the club during
the game, taking the place of injured goalkeeper Ray Wood, and, at the age of
16 years and 19 days, became the youngest player ever to play for the club.
\r\nThe 1937 FA Charity Shield was the 24th FA Charity Shield, a football match
between the winners of the previous season's First Division and FA Cup competitions.
The match was contested by league champions Manchester City and FA Cup winners
Sunderland, and was played at Maine Road, the home ground of Manchester City.
Manchester City won the game, 2\u20130.\r\nThe 2000 FA Charity Shield (also known
as the One 2 One FA Charity Shield for sponsorship reasons) was the
78th FA Charity Shield, an annual football match played between the winners
of the previous season's Premier League and FA Cup competitions. The match
was played between Manchester United, who won the 1999\u20132000 Premier League,
and Chelsea, who won the 1999\u20132000 FA Cup, and resulted in a 2\u20130 Chelsea win.
The goals were scored by Jimmy Floyd Hasselbaink and Mario Melchiot. Roy Keane
was sent off for a challenge on Gustavo Poyet and was the last person to be
sent off at the old Wembley Stadium.\r\nThe 2001 FA Charity Shield (also known
as the One 2 One FA Charity Shield for sponsorship reasons) was the 79th FA Charity Shield,
an annual football match played between the winners of the previous season's
Premier League and FA Cup. The match was contested between Liverpool, winners of
the 2000\u201301 FA Cup and Manchester United, who won the 2000\u201301 Premier
League on 12 August 2001. It was the first Shield match to be held at the
Millennium Stadium following the closure of Wembley Stadium for reconstruction.
\r\nAston Villa Football Club ( ; nicknamed Villa, The Villa, The Villans
and The Lions) is a professional football club in Aston, Birmingham, that plays
in the Championship, the second level of English football. Founded in 1874,
they have played at their current home ground, Villa Park, since 1897. Aston Villa
were one of the founder members of the Football League in 1888 and of the
Premier League in 1992.\r\nThe 1996 FA Charity Shield (also known as the
Littlewoods FA Charity Shield for sponsorship reasons) was the 74th FA Charity Shield,
an annual football match played between the winners of the previous season's Premier
League and FA Cup competitions. The match was played on 11 August 1996 at Wembley
Stadium and contested by Manchester United, who had won the Double of Premier League
and FA Cup in 1995\u201396, and Newcastle United, who had finished as runners-up
in the Premier League. Manchester United won the match 4\u20130 with goals from
Eric Cantona, Nicky Butt, David Beckham and Roy Keane."""
prediction = compiled_extract_knowledge_graph(context = text, question = "")
print(prediction.graph)
if __name__ == "__main__":
run()

2
cognee/modules/cognify/train.py
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@ -1,6 +1,5 @@
import dsp
import dspy
from dspy.datasets import HotPotQA
from dspy.teleprompt import BootstrapFewShot
from dspy.primitives.example import Example
from cognee.config import Config
@ -9,6 +8,7 @@ from cognee.root_dir import get_absolute_path
from cognee.infrastructure.files.storage import LocalStorage
from cognee.shared.data_models import Answer
from cognee.infrastructure.llm.get_llm_client import get_llm_client
from cognee.modules.cognify.dataset import HotPotQA
config = Config()
config.load()

125
cognee/programs/extract_knowledge_graph/extract_knowledge_graph.json
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File diff suppressed because one or more lines are too long

174
notebooks/dspy.ipynb
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@ -1,174 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from dspy.datasets import HotPotQA\n",
"\n",
"hotpot_dataset = HotPotQA(train_seed = 1, eval_seed = 2023, dev_size = 20, keep_details = True)\n",
"example_data = hotpot_dataset.dev[0]\n",
"\n",
"context_text = \"\\n\\n\".join(\"\\n\".join(focused_context) for focused_context in example_data.context[\"sentences\"])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from cognee.modules.cognify.extract_categories import ExtractCategories\n",
"\n",
"extract_categories = ExtractCategories()\n",
"\n",
"categories = extract_categories(text = context_text)\n",
"print(categories)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from cognee.modules.cognify.extract_cognitive_layers import ExtractCognitiveLayers\n",
"from cognee.shared.data_models import TextContent\n",
"\n",
"extract_cognitive_layers = ExtractCognitiveLayers()\n",
"\n",
"category = categories[0].subclass[0].value\n",
"\n",
"cognitive_layers = extract_cognitive_layers(text = context_text, category = category)\n",
"\n",
"print(cognitive_layers)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import dspy\n",
"from uuid import uuid4\n",
"from cognee.infrastructure.databases.graph.get_graph_client import get_graph_client, GraphDBType\n",
"from cognee.modules.cognify.generate_knowledge_graph import GenerateKnowledgeGraph\n",
"from cognee.shared.data_models import Node, Edge\n",
"\n",
"dspy.configure(trace=[])\n",
"\n",
"generate_knowledge_graph = GenerateKnowledgeGraph().activate_assertions()\n",
"\n",
"graph_file_name = str(uuid4()) + \".pkl\"\n",
"\n",
"graph_client = get_graph_client(GraphDBType.NETWORKX, graph_file_name)\n",
"\n",
"graph = generate_knowledge_graph(layer = 'Transportation Infrastructure', text = context_text)\n",
"\n",
"root_node_per_category = {}\n",
"\n",
"for node in graph.nodes:\n",
" if node.entity_type is not None and node.entity_name is not None:\n",
" graph_client.add_node(node.id, entity_name = node.entity_name, entity_type = node.entity_type)\n",
"\n",
" if node.entity_type not in root_node_per_category:\n",
" root_node = Node(\n",
" id = node.entity_type + \" root\",\n",
" entity_name = node.entity_type,\n",
" entity_type = node.entity_type + \" root\"\n",
" )\n",
" root_node_per_category[node.entity_type] = root_node\n",
" graph_client.add_node(\n",
" id = root_node.id,\n",
" entity_name = root_node.entity_name,\n",
" entity_type = root_node.entity_type\n",
" )\n",
"\n",
" graph_client.add_edge(\n",
" node.id,\n",
" root_node_per_category[node.entity_type].id,\n",
" relationship_name = \"is\"\n",
" )\n",
"\n",
"for edge in graph.edges:\n",
" if edge.source_node_id is not None and edge.target_node_id is not None and edge.relationship_name is not None:\n",
" graph_client.add_edge(edge.source_node_id, edge.target_node_id, relationship_name = edge.relationship_name)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Graph is visualized at: https://hub.graphistry.com/graph/graph.html?dataset=842a911115124473bbf23f2769dc3e96&type=arrow&viztoken=65c1d750-91fa-4e42-8696-6e8e000c34ae&usertag=993172cb-pygraphistry-0.33.5&splashAfter=1712859766&info=true\n"
]
}
],
"source": [
"import networkx as nx\n",
"import pandas as pd\n",
"import graphistry\n",
"from cognee.config import Config\n",
"from cognee.utils import render_graph\n",
"from cognee.infrastructure.databases.graph.get_graph_client import get_graph_client, GraphDBType\n",
"\n",
"config = Config()\n",
"config.load()\n",
"\n",
"graphistry.register(\n",
" api = 3,\n",
" username = config.graphistry_username,\n",
" password = config.graphistry_password\n",
")\n",
"\n",
"graph_client = get_graph_client(GraphDBType.NETWORKX, \"32652370-04d9-418e-916d-3086aa41685c.pkl\")\n",
"graph = graph_client.graph\n",
"\n",
"edges = nx.to_pandas_edgelist(graph)\n",
"\n",
"nodes_data = [{\n",
" \"id\": node_id,\n",
" \"entity_name\": node[\"entity_name\"],\n",
" \"entity_type\": node[\"entity_type\"]\n",
"} for (node_id, node) in graph.nodes(data = True)]\n",
"\n",
"nodes = pd.DataFrame(nodes_data)\n",
"\n",
"plotter = graphistry.edges(edges, source = \"source\", destination = \"target\").nodes(nodes, \"id\")\n",
"\n",
"plotter.bind(edge_title = \"relationship_name\", edge_label = \"relationship_name\", point_title = \"entity_name\", point_label = \"entity_name\")\n",
"url = plotter.plot(render = False, as_files = True)\n",
"print(f\"Graph is visualized at: {url}\")"
]
}
],
"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": "3.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

709
notebooks/full_run.ipynb
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@ -10,7 +10,7 @@
"from os import path\n",
"import cognee\n",
"import dspy\n",
"from dspy.datasets import HotPotQA\n",
"from cognee.modules.cognify.dataset import HotPotQA\n",
"\n",
"data_directory_path = path.abspath(\"../.data\")\n",
"cognee.config.data_root_directory(data_directory_path)\n",
@ -45,27 +45,10 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"id": "44603a2a",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/borisarzentar/Projects/Topoteretes/cognee/.venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"['train_dataset']\n",
"10\n"
]
}
],
"outputs": [],
"source": [
"from os import path\n",
"import cognee\n",
@ -84,607 +67,10 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"id": "65bfaf09",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:NetworkXAdapter:File /Users/borisarzentar/Projects/Topoteretes/cognee/.cognee_system/databases/cognee_graph.pkl not found. Initializing an empty graph.<frozen abc>:123: ResourceWarning: unclosed <socket.socket fd=76, family=2, type=1, proto=6, laddr=('192.168.9.243', 63064), raddr=('35.245.15.233', 6333)>\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=76 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"Fetching 7 files: 100%|██████████| 7/7 [00:00<00:00, 67806.30it/s]\n",
"WARNING:NetworkXAdapter:File /Users/borisarzentar/Projects/Topoteretes/cognee/.cognee_system/databases/cognee_graph.pkl not found. Initializing an empty graph.WARNING:NetworkXAdapter:File /Users/borisarzentar/Projects/Topoteretes/cognee/.cognee_system/databases/cognee_graph.pkl not found. Initializing an empty graph."
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing document (faf8441f25bf58d5a55a2821e5e40a9e).\n",
"Processing document (0a4c678b77a75927992f72b81fda2398).\n",
"Processing document (ae7c88b9432a5f51a512885da6f2d1c6).\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/borisarzentar/Projects/Topoteretes/cognee/.venv/lib/python3.11/site-packages/networkx/classes/reportviews.py:921: ResourceWarning: unclosed <ssl.SSLSocket fd=79, family=2, type=1, proto=0, laddr=('192.168.9.243', 63068), raddr=('35.245.15.233', 6333)>\n",
" self._report = lambda n, nbr, k, dd: (n, nbr, k, dd)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/threading.py:910: ResourceWarning: unclosed <socket.socket fd=79, family=2, type=1, proto=6, laddr=('192.168.9.243', 63070), raddr=('35.245.15.233', 6333)>\n",
" self._invoke_excepthook = _make_invoke_excepthook()\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=79 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"Fetching 7 files: 100%|██████████| 7/7 [00:00<00:00, 78085.45it/s]\n",
"/Users/borisarzentar/Projects/Topoteretes/cognee/.venv/lib/python3.11/site-packages/pydantic/_internal/_core_utils.py:197: ResourceWarning: unclosed <ssl.SSLSocket fd=79, family=2, type=1, proto=0, laddr=('192.168.9.243', 63072), raddr=('35.245.15.233', 6333)>\n",
" return f(schema, self._walk)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=75 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"Fetching 7 files: 100%|██████████| 7/7 [00:00<00:00, 118866.91it/s]\n",
"/Users/borisarzentar/Projects/Topoteretes/cognee/.venv/lib/python3.11/site-packages/pydantic/_internal/_core_utils.py:200: ResourceWarning: unclosed <ssl.SSLSocket fd=75, family=2, type=1, proto=0, laddr=('192.168.9.243', 63073), raddr=('35.245.15.233', 6333)>\n",
" schema = self._schema_type_to_method[schema['type']](schema.copy(), f)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"Fetching 7 files: 100%|██████████| 7/7 [00:00<00:00, 75282.38it/s]\n",
"/Users/borisarzentar/Projects/Topoteretes/cognee/.venv/lib/python3.11/site-packages/pydantic/_internal/_core_utils.py:200: ResourceWarning: unclosed <ssl.SSLSocket fd=75, family=2, type=1, proto=0, laddr=('192.168.9.243', 63074), raddr=('35.245.15.233', 6333)>\n",
" schema = self._schema_type_to_method[schema['type']](schema.copy(), f)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Document (DOCUMENT__faf8441f25bf58d5a55a2821e5e40a9e) classified.\n",
"Document (DOCUMENT__0a4c678b77a75927992f72b81fda2398) classified.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=75 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Document (DOCUMENT__ae7c88b9432a5f51a512885da6f2d1c6) classified.\n",
"Document (DOCUMENT__faf8441f25bf58d5a55a2821e5e40a9e) summarized.\n",
"Document (DOCUMENT__ae7c88b9432a5f51a512885da6f2d1c6) summarized.\n",
"Document (DOCUMENT__0a4c678b77a75927992f72b81fda2398) summarized.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[nltk_data] Downloading package stopwords to\n",
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"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=85 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=88 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=83 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=81 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=79 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=84 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=82 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
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"/Users/borisarzentar/Projects/Topoteretes/cognee/.venv/lib/python3.11/site-packages/ipykernel/iostream.py:720: ResourceWarning: unclosed <socket.socket fd=75, family=2, type=1, proto=6, laddr=('192.168.9.243', 63090), raddr=('35.245.15.233', 6333)>\n",
" with self._buffer_lock:\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"Fetching 7 files: 100%|██████████| 7/7 [00:00<00:00, 61941.20it/s]\n",
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"/Users/borisarzentar/Projects/Topoteretes/cognee/.venv/lib/python3.11/site-packages/networkx/classes/reportviews.py:947: ResourceWarning: unclosed <ssl.SSLSocket fd=75, family=2, type=1, proto=0, laddr=('192.168.9.243', 63092), raddr=('35.245.15.233', 6333)>\n",
" return (\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
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" return (\n",
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"/Users/borisarzentar/Projects/Topoteretes/cognee/.venv/lib/python3.11/site-packages/networkx/classes/reportviews.py:947: ResourceWarning: unclosed <ssl.SSLSocket fd=81, family=2, type=1, proto=0, laddr=('192.168.9.243', 63099), raddr=('35.245.15.233', 6333)>\n",
" return (\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
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},
{
"data": {
"text/plain": [
"<networkx.classes.multidigraph.MultiDiGraph at 0x296f97350>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"from os import path\n",
"import cognee\n",
@ -702,42 +88,10 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"id": "a514cf38",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/json/decoder.py:353: ResourceWarning: unclosed <socket.socket fd=79, family=2, type=1, proto=6, laddr=('192.168.9.243', 63138), raddr=('35.245.15.233', 6333)>\n",
" obj, end = self.scan_once(s, idx)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/json/decoder.py:353: ResourceWarning: unclosed <socket.socket fd=81, family=2, type=1, proto=6, laddr=('192.168.9.243', 63139), raddr=('35.245.15.233', 6333)>\n",
" obj, end = self.scan_once(s, idx)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/json/decoder.py:353: ResourceWarning: unclosed <socket.socket fd=88, family=2, type=1, proto=6, laddr=('192.168.9.243', 63140), raddr=('35.245.15.233', 6333)>\n",
" obj, end = self.scan_once(s, idx)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=88 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/json/decoder.py:353: ResourceWarning: unclosed <socket.socket fd=89, family=2, type=1, proto=6, laddr=('192.168.9.243', 63141), raddr=('35.245.15.233', 6333)>\n",
" obj, end = self.scan_once(s, idx)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=89 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Graph is visualized at: https://hub.graphistry.com/graph/graph.html?dataset=bbc22f0135744f678f1bbc590b2501d8&type=arrow&viztoken=380021af-aef1-4173-8ced-ce8adf956812&usertag=993172cb-pygraphistry-0.33.7&splashAfter=1713632217&info=true\n"
]
}
],
"outputs": [],
"source": [
"import networkx as nx\n",
"import pandas as pd\n",
@ -778,52 +132,14 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"id": "e916c484",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
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" return _validate_core_schema(schema)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=79 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
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" return _validate_core_schema(schema)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n",
"/Users/borisarzentar/.pyenv/versions/3.11.8/lib/python3.11/asyncio/selector_events.py:868: ResourceWarning: unclosed transport <_SelectorSocketTransport fd=81 read=idle write=<idle, bufsize=0>>\n",
" _warn(f\"unclosed transport {self!r}\", ResourceWarning, source=self)\n",
"ResourceWarning: Enable tracemalloc to get the object allocation traceback\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"Through the Window is an album by the American music project Prurient, the performing name of the artist Dominick Fernow. The three-song album was released on March 19, 2013 through the English label Blackest Ever Black. Though released in 2013, the tracks for \"Through the Window\" were recorded in October 2011 at the same time as Prurient's two Hydra Head Records releases — the studio album \"Bermuda Drain\" (2011) and the EP \"Time's Arrow\" (2011) — and were noted for musically showing more techno influences, akin to one of Fernow's other projects, Vatican Shadow.\n",
"Yes I Am is the fourth studio album by American singer-songwriter Melissa Etheridge, released by Island Records on September 21, 1993 (see 1993 in music). The title is generally thought to refer to Etheridge's recent coming out as a lesbian, confirming long-standing rumors about her personal life. This is the album that gave Etheridge national recognition. The rock ballad \"Come to My Window\" was the first single released from the album, which peaked at No. 25 on the \"Billboard\" Hot 100, and its video featured actress Juliette Lewis having a nervous breakdown. This single brought the album into the public consciousness and was quickly followed by \"I'm the Only One\", which became a major hit and reached No. 8 on the Hot 100, and \"If I Wanted To\", which hit No. 16.\n",
"Christian Joseph \"Chris\" Arena is an American singer-songwriter and producer whose work can be seen across television networks such as ABC, ABC Family, CBS, The CW and others as well as feature films. Arena was nominated for a 2016 Emmy Award for 'Outstanding Original Song in Daytime Drama' for his song 'Dreams' written for ABC's \"General Hospital\". Most recently, Arena's work can be seen on \"\" and \"Scream\" on MTV. Other tracks, \"Baby Fish\", \"Yes It Do\", \"Train\", \"City Inside Me\", and \"Closed Window\" were featured in Season 4 of ABC's \"Pretty Little Liars\". His song \"Dreams\" was used as a theme song on ABC's \"General Hospital\" in January 2015 as well as his latest song \"Nothing's Gonna Stop Us\", which was featured in TV Land's \"Younger\" in April 2015. His song \"For You\" was also picked for the romantic comedy/feature film, \"This Thing With Sarah\".\n",
"Melissa Lou Etheridge (born May 29, 1961) is an American singer-songwriter, guitarist, and activist. Her self-titled debut album \"Melissa Etheridge\" was released in 1988 and became an underground success. The album peaked at No. 22 on the \"Billboard\" 200, and its lead single, \"Bring Me Some Water\", garnered Etheridge her first Grammy Award nomination for Best Rock Vocal Performance, Female. In 1993, Etheridge won her first Grammy award for her single \"Ain't It Heavy\" from her third album, \"Never Enough\". Later that year, she released what would become her mainstream breakthrough album, \"Yes I Am\". Its tracks \"I'm the Only One\" and \"Come to My Window\" both reached the top 30 in the United States, and the latter earned Etheridge her second Grammy award. \"Yes I Am\" peaked at No. 15 on the \"Billboard\" 200, and spent 138 weeks on the chart, earning a RIAA certification of 6x Platinum, her largest to date.\n",
"American singer-songwriter Bridgit Mendler has embarked on two concert tours, one of which have been worldwide. Her debut tour, \"\", started on August 25, 2012. Mendler played at state fairs and music festivals in the United States and Canada to promote her debut album \"Hello My Name Is...\" with the new songs. \"Rocks at My Window\" and \"Hold On for Dear Love\" were the only songs from the album not to be performed. She performing two covers songs in the shows, \"Animal\", originally by Neon Trees, and \"This Love, by Maroon 5. She also sang a song from the soundtrack of \"Lemonade Mouth\", \"Somebody\". On January 19, 2013 Mendler in New York from UNICEF charity. Her second concert, the Summer Tour, visited North America in the leg one and started in Iowa and ended in Washington. In 2014 Mendler announced the second leg of the tour as part of the release of her upcoming second album. The leg started in Charlottetown, Canada, on June 28, 2014.\n",
"John Townes Van Zandt (March 7, 1944 – January 1, 1997), best known as Townes Van Zandt, was an American singer songwriter. He is widely regarded for his poetic, often heroically sad songs. In 1983, six years after Emmylou Harris had first popularized it, Willie Nelson and Merle Haggard covered his song \"Pancho and Lefty\", scoring a number one hit on the Billboard country music charts. Much of his life was spent touring various dive bars, often living in cheap motel rooms and backwoods cabins. For much of the 1970s, he lived in a simple shack without electricity or a phone.\n",
"Native Window is the self-titled debut album of American progressive rock band Native Window. It was released on June 23, 2009. The tracks on the album are all original material composed by the members of the band. The album came about soon after the band formed; it was because Steve Walsh, the lead singer for Kansas, would not write any new material. Phil Ehart, Rich Williams, Billy Greer, and David Ragsdale got together to write new songs, and released them under the Native Window name.\n",
"At My Window is an album released by Folk/country singer-songwriter Townes Van Zandt in 1987. This was Van Zandt's first studio album in the nine years that followed 1978's \"Flyin' Shoes\", and his only studio album recorded in the 1980s. Although the songwriter had become less prolific, this release showed that the quality of his material remained high.\n",
"Love Letter for Fire is a collaborative album of duets by American singer-songwriter Sam Beam, commonly known as Iron & Wine, and American singer-songwriter Jesca Hoop, released on April 15, 2016 on Sub Pop.\n",
"Little Window is the debut album of American singer-songwriter Baby Dee. The album was released in 2002 on the Durtro label. It was produced, composed, and performed entirely by Dee.\n"
]
}
],
"outputs": [],
"source": [
"from os import path\n",
"import cognee\n",
"from cognee.infrastructure.databases.graph.get_graph_client import get_graph_client, GraphDBType\n",
"from cognee.modules.search.vector.search_similarity import search_similarity\n",
"\n",
"data_directory_path = path.abspath(\"../.data\")\n",
@ -832,7 +148,10 @@
"cognee_directory_path = path.abspath(\"../.cognee_system\")\n",
"cognee.config.system_root_directory(cognee_directory_path)\n",
"\n",
"results = await search_similarity(\"At My Window was released by which American singer-songwriter?\")\n",
"graph_client = await get_graph_client(GraphDBType.NETWORKX)\n",
"graph = graph_client.graph\n",
"\n",
"results = await search_similarity(\"At My Window was released by which American singer-songwriter?\", graph)\n",
"\n",
"for result in results:\n",
" print(\"At My Window\" in result)\n",

338
poetry.lock generated
View file

@ -1129,26 +1129,25 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
[[package]]
name = "datasets"
version = "2.18.0"
version = "2.14.7"
description = "HuggingFace community-driven open-source library of datasets"
optional = false
python-versions = ">=3.8.0"
files = [
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[package.dependencies]
aiohttp = "*"
dill = ">=0.3.0,<0.3.9"
filelock = "*"
fsspec = {version = ">=2023.1.0,<=2024.2.0", extras = ["http"]}
huggingface-hub = ">=0.19.4"
dill = ">=0.3.0,<0.3.8"
fsspec = {version = ">=2023.1.0,<=2023.10.0", extras = ["http"]}
huggingface-hub = ">=0.14.0,<1.0.0"
multiprocess = "*"
numpy = ">=1.17"
packaging = "*"
pandas = "*"
pyarrow = ">=12.0.0"
pyarrow = ">=8.0.0"
pyarrow-hotfix = "*"
pyyaml = ">=5.1"
requests = ">=2.19.0"
@ -1156,18 +1155,18 @@ tqdm = ">=4.62.1"
xxhash = "*"
[package.extras]
apache-beam = ["apache-beam (>=2.26.0)"]
apache-beam = ["apache-beam (>=2.26.0,<2.44.0)"]
audio = ["librosa", "soundfile (>=0.12.1)"]
benchmarks = ["tensorflow (==2.12.0)", "torch (==2.0.1)", "transformers (==4.30.1)"]
dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "ruff (>=0.3.0)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
dev = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "black (>=23.1,<24.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "pyyaml (>=5.3.1)", "rarfile (>=4.0)", "ruff (>=0.0.241)", "s3fs", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "zstandard"]
docs = ["s3fs", "tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos", "torch", "transformers"]
jax = ["jax (>=0.3.14)", "jaxlib (>=0.3.14)"]
jax = ["jax (>=0.2.8,!=0.3.2,<=0.3.25)", "jaxlib (>=0.1.65,<=0.3.25)"]
metrics-tests = ["Werkzeug (>=1.0.1)", "accelerate", "bert-score (>=0.3.6)", "jiwer", "langdetect", "mauve-text", "nltk", "requests-file (>=1.5.1)", "rouge-score", "sacrebleu", "sacremoses", "scikit-learn", "scipy", "sentencepiece", "seqeval", "six (>=1.15.0,<1.16.0)", "spacy (>=3.0.0)", "texttable (>=1.6.3)", "tldextract", "tldextract (>=3.1.0)", "toml (>=0.10.1)", "typer (<0.5.0)"]
quality = ["ruff (>=0.3.0)"]
quality = ["black (>=23.1,<24.0)", "pyyaml (>=5.3.1)", "ruff (>=0.0.241)"]
s3 = ["s3fs"]
tensorflow = ["tensorflow (>=2.2.0,!=2.6.0,!=2.6.1)", "tensorflow-macos"]
tensorflow-gpu = ["tensorflow-gpu (>=2.2.0,!=2.6.0,!=2.6.1)"]
tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "jax (>=0.3.14)", "jaxlib (>=0.3.14)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch (>=2.0.0)", "transformers", "typing-extensions (>=4.6.1)", "zstandard"]
tests = ["Pillow (>=6.2.1)", "absl-py", "apache-beam (>=2.26.0,<2.44.0)", "elasticsearch (<8.0.0)", "faiss-cpu (>=1.6.4)", "joblib (<1.3.0)", "joblibspark", "librosa", "lz4", "py7zr", "pyspark (>=3.4)", "pytest", "pytest-datadir", "pytest-xdist", "rarfile (>=4.0)", "s3fs (>=2021.11.1)", "soundfile (>=0.12.1)", "sqlalchemy (<2.0.0)", "tensorflow (>=2.3,!=2.6.0,!=2.6.1)", "tensorflow-macos", "tiktoken", "torch", "transformers", "zstandard"]
torch = ["torch"]
vision = ["Pillow (>=6.2.1)"]
@ -1226,18 +1225,17 @@ files = [
[[package]]
name = "dill"
version = "0.3.8"
version = "0.3.7"
description = "serialize all of Python"
optional = false
python-versions = ">=3.8"
python-versions = ">=3.7"
files = [
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{file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
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]
[package.extras]
graph = ["objgraph (>=1.7.2)"]
profile = ["gprof2dot (>=2022.7.29)"]
[[package]]
name = "diskcache"
@ -1334,45 +1332,40 @@ files = [
]
[[package]]
name = "dspy"
version = "2.0.8"
name = "dspy-ai"
version = "2.4.3"
description = "DSPy"
optional = false
python-versions = ">=3.9,<3.12"
files = []
develop = false
python-versions = ">=3.9"
files = [
{file = "dspy-ai-2.4.3.tar.gz", hash = "sha256:d4dce88358cec53ee70025831baafb5621ac3453a2ad200faede8e128b22369b"},
{file = "dspy_ai-2.4.3-py3-none-any.whl", hash = "sha256:0742b83da7ec8750580b2c0b24eb8ec2594fee704a7ab0a369c621e36387b2ec"},
]
[package.dependencies]
backoff = "^2.2.1"
datasets = "^2.14.6"
joblib = "^1.3.2"
backoff = ">=2.2.1,<2.3.0"
datasets = ">=2.14.6,<2.15.0"
joblib = ">=1.3.2,<1.4.0"
openai = ">=0.28.1,<2.0.0"
optuna = "^3.4.0"
pandas = "^2.1.1"
pydantic = "^2.0"
regex = "^2023.10.3"
requests = "^2.31.0"
rich = "^13.7.1"
structlog = "^24.1.0"
tqdm = "^4.66.1"
ujson = "^5.8.0"
optuna = "*"
pandas = "*"
pydantic = ">=2.5.0,<=2.7"
regex = "*"
requests = "*"
tqdm = "*"
ujson = "*"
[package.extras]
aws = ["boto3 (>=1.34.78,<2.0.0)"]
anthropic = ["anthropic (>=0.18.0,<0.19.0)"]
chromadb = ["chromadb (>=0.4.14,<0.5.0)"]
docs = ["autodoc_pydantic", "docutils (<0.17)", "furo (>=2023.3.27)", "m2r2", "myst-nb", "myst-parser", "sphinx (>=4.3.0)", "sphinx-autobuild", "sphinx-automodapi (==0.16.0)", "sphinx-reredirects (>=0.1.2,<0.2.0)", "sphinx_rtd_theme"]
marqo = ["marqo"]
milvus = ["pymilvus (>=2.3.6,<3.0.0)"]
pinecone = ["pinecone-client (>=2.2.4,<3.0.0)"]
postgres = ["pgvector (>=0.2.5,<0.3.0)", "psycopg2 (>=2.9.9,<3.0.0)"]
qdrant = ["fastembed (>=0.1.0,<0.2.0)", "qdrant-client (>=1.6.2,<2.0.0)"]
weaviate = ["weaviate-client (>=4.5.4,<5.0.0)"]
[package.source]
type = "git"
url = "https://github.com/Vasilije1990/dspy.git"
reference = "main"
resolved_reference = "1f53443551e3298739e3f3f770421b4890201d0c"
dev = ["pytest (>=6.2.5)"]
docs = ["autodoc-pydantic", "docutils (<0.17)", "furo (>=2023.3.27)", "m2r2", "myst-nb", "myst-parser", "sphinx (>=4.3.0)", "sphinx-autobuild", "sphinx-automodapi (==0.16.0)", "sphinx-reredirects (>=0.1.2)", "sphinx-rtd-theme"]
faiss-cpu = ["faiss-cpu", "sentence-transformers"]
marqo = ["marqo", "marqo (>=3.1.0,<3.2.0)"]
mongodb = ["pymongo (>=3.12.0,<3.13.0)"]
pinecone = ["pinecone-client (>=2.2.4,<2.3.0)"]
qdrant = ["fastembed", "fastembed (>=0.1.0)", "qdrant-client", "qdrant-client (>=1.6.2)"]
weaviate = ["weaviate-client (>=3.26.1,<3.27.0)", "weaviate-client (>=4.5.4,<4.6.0)"]
[[package]]
name = "duckdb"
@ -1730,17 +1723,18 @@ files = [
[[package]]
name = "fsspec"
version = "2024.2.0"
version = "2023.10.0"
description = "File-system specification"
optional = false
python-versions = ">=3.8"
files = [
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aiohttp = {version = "<4.0.0a0 || >4.0.0a0,<4.0.0a1 || >4.0.0a1", optional = true, markers = "extra == \"http\""}
requests = {version = "*", optional = true, markers = "extra == \"http\""}
[package.extras]
abfs = ["adlfs"]
@ -1757,7 +1751,7 @@ github = ["requests"]
gs = ["gcsfs"]
gui = ["panel"]
hdfs = ["pyarrow (>=1)"]
http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)"]
http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
libarchive = ["libarchive-c"]
oci = ["ocifs"]
s3 = ["s3fs"]
@ -2547,13 +2541,13 @@ files = [
[[package]]
name = "joblib"
version = "1.4.0"
version = "1.3.2"
description = "Lightweight pipelining with Python functions"
optional = false
python-versions = ">=3.8"
python-versions = ">=3.7"
files = [
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]
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@ -3582,27 +3576,31 @@ files = [
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name = "multiprocess"
version = "0.70.16"
version = "0.70.15"
description = "better multiprocessing and multithreading in Python"
optional = false
python-versions = ">=3.8"
python-versions = ">=3.7"
files = [
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]
[[package]]
@ -7077,4 +7075,4 @@ weaviate = ["weaviate-client"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9.0,<3.12"
content-hash = "a541095d5a95b5c55b467df9a62e088d9276933af6372cd6e3b51fbaafcdd0e6"
content-hash = "deea0ca0fe759aabe96f616f95cf647714d970873466cfa8ae942f158b3423d0"

2
pyproject.toml
View file

@ -57,10 +57,10 @@ xmltodict = "^0.13.0"
neo4j = "^5.18.0"
jinja2 = "^3.1.3"
matplotlib = "^3.8.3"
dspy = { git = "https://github.com/Vasilije1990/dspy.git", branch = "main" }
nest-asyncio = "^1.6.0"
structlog = "^24.1.0"
tiktoken = "^0.6.0"
dspy-ai = "2.4.3"
[tool.poetry.extras]