chore: remove unused dependencies and make some optional (#661)

<!-- .github/pull_request_template.md -->

## Description
<!-- Provide a clear description of the changes in this PR -->

## DCO Affirmation
I affirm that all code in every commit of this pull request conforms to
the terms of the Topoteretes Developer Certificate of Origin

.github/workflows/test_chromadb.yml

@@ -44,7 +44,7 @@ jobs:
           installer-parallel: true
 
       - name: Install dependencies
-        run: poetry install --no-interaction
+        run: poetry install --extras chromadb --no-interaction
 
       - name: Run chromadb test
         env:

.github/workflows/test_cognee_server_start.yml

@@ -41,7 +41,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          poetry install --no-interaction
+          poetry install --extras api --no-interaction
 
       - name: Run cognee server
         env:

Dockerfile

@@ -2,6 +2,8 @@ FROM python:3.11-slim
 
 # Define Poetry extras to install
 ARG POETRY_EXTRAS="\
+# API \
+api \
 # Storage & Databases \
 filesystem postgres weaviate qdrant neo4j falkordb milvus kuzu \
 # Notebooks & Interactive Environments \

cognee/api/client.py

@@ -169,9 +169,9 @@ app.include_router(get_settings_router(), prefix="/api/v1/settings", tags=["sett
 
 app.include_router(get_visualize_router(), prefix="/api/v1/visualize", tags=["visualize"])
 
-app.include_router(
+codegraph_routes = get_code_pipeline_router()
-    get_code_pipeline_router(), prefix="/api/v1/code-pipeline", tags=["code-pipeline"]
+if codegraph_routes:
-)
+    app.include_router(codegraph_routes, prefix="/api/v1/code-pipeline", tags=["code-pipeline"])
 
 
 def start_api_server(host: str = "0.0.0.0", port: int = 8000):

cognee/api/v1/add/routers/get_add_router.py

@@ -2,10 +2,10 @@ from fastapi import Form, UploadFile, Depends
 from fastapi.responses import JSONResponse
 from fastapi import APIRouter
 from typing import List
-import aiohttp
 import subprocess
 import logging
-import os
+
+import requests
 from cognee.modules.users.models import User
 from cognee.modules.users.methods import get_authenticated_user
 
@@ -36,17 +36,12 @@ def get_add_router() -> APIRouter:
                     )
                 else:
                     # Fetch and store the data from other types of URL using curl
-                    async with aiohttp.ClientSession() as session:
+                    response = requests.get(data)
-                        async with session.get(data) as resp:
+                    response.raise_for_status()
-                            if resp.status == 200:
+
-                                file_data = await resp.read()
+                    file_data = await response.content()
-                                filename = os.path.basename(data)
+
-                                with open(f".data/{filename}", "wb") as f:
+                    return await cognee_add(file_data)
-                                    f.write(file_data)
-                                await cognee_add(
-                                    "data://.data/",
-                                    f"{data.split('/')[-1]}",
-                                )
             else:
                 await cognee_add(
                     data,

cognee/api/v1/cognify/code_graph_pipeline.py

@@ -25,6 +25,7 @@ from cognee.tasks.summarization import summarize_text
 from cognee.infrastructure.llm import get_max_chunk_tokens
 
 monitoring = get_base_config().monitoring_tool
+
 if monitoring == MonitoringTool.LANGFUSE:
     from langfuse.decorators import observe
 

cognee/api/v1/cognify/routers/get_code_pipeline_router.py

@@ -3,7 +3,6 @@ import logging
 from fastapi import APIRouter
 from fastapi.responses import JSONResponse
 from cognee.api.DTO import InDTO
-from cognee.api.v1.cognify.code_graph_pipeline import run_code_graph_pipeline
 from cognee.modules.retrieval.code_retriever import CodeRetriever
 from cognee.modules.storage.utils import JSONEncoder
 
@@ -22,11 +21,19 @@ class CodePipelineRetrievePayloadDTO(InDTO):
 
 
 def get_code_pipeline_router() -> APIRouter:
+    try:
+        import run_code_graph_pipeline
+    except ModuleNotFoundError:
+        logger.error("codegraph dependencies not found. Skipping codegraph API routes.")
+        return None
+
     router = APIRouter()
 
     @router.post("/index", response_model=None)
     async def code_pipeline_index(payload: CodePipelineIndexPayloadDTO):
         """This endpoint is responsible for running the indexation on code repo."""
+        from cognee.api.v1.cognify.code_graph_pipeline import run_code_graph_pipeline
+
         try:
             async for result in run_code_graph_pipeline(payload.repo_path, payload.include_docs):
                 logger.info(result)

cognee/api/v1/users/routers/get_visualize_router.py

@@ -1,11 +1,7 @@
-from fastapi import Form, UploadFile, Depends
+import logging
+from fastapi import Depends
 from fastapi.responses import JSONResponse
 from fastapi import APIRouter
-from typing import List
-import aiohttp
-import subprocess
-import logging
-import os
 from cognee.modules.users.models import User
 from cognee.modules.users.methods import get_authenticated_user
 

cognee/eval_framework/benchmark_adapters/twowikimultihop_adapter.py

@@ -1,8 +1,4 @@
-import requests
+from typing import Any
-import os
-import json
-import random
-from typing import Optional, Any, List, Tuple
 from cognee.eval_framework.benchmark_adapters.hotpot_qa_adapter import HotpotQAAdapter
 
 

cognee/infrastructure/databases/vector/embeddings/OllamaEmbeddingEngine.py

@@ -1,9 +1,11 @@
 import asyncio
-import httpx
+import aiohttp
 import logging
 from typing import List, Optional
 import os
 
+import aiohttp.http_exceptions
+
 from cognee.infrastructure.databases.vector.embeddings.EmbeddingEngine import EmbeddingEngine
 from cognee.infrastructure.databases.exceptions.EmbeddingException import EmbeddingException
 from cognee.infrastructure.llm.tokenizer.HuggingFace import HuggingFaceTokenizer
@@ -48,14 +50,10 @@ class OllamaEmbeddingEngine(EmbeddingEngine):
         if self.mock:
             return [[0.0] * self.dimensions for _ in text]
 
-        embeddings = []
+        embeddings = await asyncio.gather(*[self._get_embedding(prompt) for prompt in text])
-        async with httpx.AsyncClient() as client:
-            for prompt in text:
-                embedding = await self._get_embedding(client, prompt)
-                embeddings.append(embedding)
         return embeddings
 
-    async def _get_embedding(self, client: httpx.AsyncClient, prompt: str) -> List[float]:
+    async def _get_embedding(self, prompt: str) -> List[float]:
         """
         Internal method to call the Ollama embeddings endpoint for a single prompt.
         """
@@ -71,13 +69,13 @@ class OllamaEmbeddingEngine(EmbeddingEngine):
         retries = 0
         while retries < self.MAX_RETRIES:
             try:
-                response = await client.post(
+                async with aiohttp.ClientSession() as session:
-                    self.endpoint, json=payload, headers=headers, timeout=60.0
+                    async with session.post(
-                )
+                        self.endpoint, json=payload, headers=headers, timeout=60.0
-                response.raise_for_status()
+                    ) as response:
-                data = response.json()
+                        data = await response.json()
-                return data["embedding"]
+                        return data["embedding"]
-            except httpx.HTTPStatusError as e:
+            except aiohttp.http_exceptions.HttpBadRequest as e:
                 logger.error(f"HTTP error on attempt {retries + 1}: {e}")
                 retries += 1
                 await asyncio.sleep(min(2**retries, 60))

cognee/infrastructure/llm/anthropic/adapter.py

@@ -1,7 +1,6 @@
 from typing import Type
 from pydantic import BaseModel
 import instructor
-import anthropic
 
 from cognee.exceptions import InvalidValueError
 from cognee.infrastructure.llm.llm_interface import LLMInterface
@@ -15,9 +14,12 @@ class AnthropicAdapter(LLMInterface):
     model: str
 
     def __init__(self, max_tokens: int, model: str = None):
+        import anthropic
+
         self.aclient = instructor.patch(
             create=anthropic.Anthropic().messages.create, mode=instructor.Mode.ANTHROPIC_TOOLS
         )
+
         self.model = model
         self.max_tokens = max_tokens
 

cognee/infrastructure/llm/gemini/adapter.py

@@ -2,7 +2,6 @@ from typing import Type, Optional
 from pydantic import BaseModel
 import logging
 import litellm
-import asyncio
 from litellm import acompletion, JSONSchemaValidationError
 from cognee.shared.data_models import MonitoringTool
 from cognee.exceptions import InvalidValueError
@@ -13,6 +12,7 @@ from cognee.base_config import get_base_config
 logger = logging.getLogger(__name__)
 
 monitoring = get_base_config().monitoring_tool
+
 if monitoring == MonitoringTool.LANGFUSE:
     from langfuse.decorators import observe
 

cognee/infrastructure/llm/openai/adapter.py

@@ -13,6 +13,7 @@ from cognee.infrastructure.llm.prompts import read_query_prompt
 from cognee.base_config import get_base_config
 
 monitoring = get_base_config().monitoring_tool
+
 if monitoring == MonitoringTool.LANGFUSE:
     from langfuse.decorators import observe
 

cognee/modules/data/extraction/extract_summary.py

@@ -4,7 +4,6 @@ from typing import Type
 
 from instructor.exceptions import InstructorRetryException
 from pydantic import BaseModel
-from tenacity import RetryError
 
 from cognee.infrastructure.llm.get_llm_client import get_llm_client
 from cognee.infrastructure.llm.prompts import read_query_prompt
@@ -36,7 +35,7 @@ async def extract_code_summary(content: str):
     else:
         try:
             result = await extract_summary(content, response_model=SummarizedCode)
-        except (RetryError, InstructorRetryException) as e:
+        except InstructorRetryException as e:
             logger.error("Failed to extract code summary, falling back to mock summary", exc_info=e)
             result = get_mock_summarized_code()
 

cognee/modules/data/extraction/extract_topics_naive.py

@@ -1,63 +0,0 @@
-import re
-from nltk.downloader import download
-from nltk.stem import WordNetLemmatizer
-from nltk.tokenize import sent_tokenize, word_tokenize
-from nltk.corpus import stopwords, wordnet
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.decomposition import TruncatedSVD
-
-
-def extract_topics(text: str):
-    sentences = sent_tokenize(text)
-
-    try:
-        wordnet.ensure_loaded()
-    except LookupError:
-        download("wordnet")
-        wordnet.ensure_loaded()
-
-    lemmatizer = WordNetLemmatizer()
-    base_notation_sentences = [lemmatizer.lemmatize(sentence) for sentence in sentences]
-
-    tf_vectorizer = TfidfVectorizer(tokenizer=word_tokenize, token_pattern=None)
-    transformed_corpus = tf_vectorizer.fit_transform(base_notation_sentences)
-
-    svd = TruncatedSVD(n_components=10)
-    svd_corpus = svd.fit(transformed_corpus)
-
-    feature_scores = dict(zip(tf_vectorizer.vocabulary_, svd_corpus.components_[0]))
-
-    topics = sorted(
-        feature_scores,
-        # key = feature_scores.get,
-        key=lambda x: transformed_corpus[0, tf_vectorizer.vocabulary_[x]],
-        reverse=True,
-    )[:10]
-
-    return topics
-
-
-def clean_text(text: str):
-    text = re.sub(r"[ \t]{2,}|[\n\r]", " ", text.lower())
-    return re.sub(r"[`\"'.,;!?…]", "", text).strip()
-
-
-def remove_stop_words(text: str):
-    try:
-        stopwords.ensure_loaded()
-    except LookupError:
-        download("stopwords")
-        stopwords.ensure_loaded()
-
-    stop_words = set(stopwords.words("english"))
-    text = text.split()
-    text = [word for word in text if word not in stop_words]
-    return " ".join(text)
-
-
-if __name__ == "__main__":
-    text = """Lorem Ipsum is simply dummy text of the printing and typesetting industry... Lorem Ipsum has been the industry's standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book… It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of Lorem Ipsum.
-        Why do we use it?
-        It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout! The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like).
-    """
-    print(extract_topics(remove_stop_words(clean_text(text))))

cognee/shared/utils.py

@@ -21,7 +21,6 @@ from cognee.infrastructure.databases.graph import get_graph_engine
 
 from uuid import uuid4
 import pathlib
-import nltk
 from cognee.shared.exceptions import IngestionError
 
 # Analytics Proxy Url, currently hosted by Vercel
@@ -29,7 +28,10 @@ proxy_url = "https://test.prometh.ai"
 
 
 def get_entities(tagged_tokens):
+    import nltk
+
     nltk.download("maxent_ne_chunker", quiet=True)
+
     from nltk.chunk import ne_chunk
 
     return ne_chunk(tagged_tokens)
@@ -37,6 +39,7 @@ def get_entities(tagged_tokens):
 
 def extract_pos_tags(sentence):
     """Extract Part-of-Speech (POS) tags for words in a sentence."""
+    import nltk
 
     # Ensure that the necessary NLTK resources are downloaded
     nltk.download("words", quiet=True)
@@ -308,37 +311,6 @@ def embed_logo(p, layout_scale, logo_alpha, position):
     )
 
 
-def style_and_render_graph(p, G, layout_positions, node_attribute, node_colors, centrality):
-    """
-    Apply styling and render the graph into the plot.
-    """
-    from bokeh.plotting import figure, from_networkx
-    from bokeh.models import Circle, MultiLine, HoverTool, ColumnDataSource, Range1d
-    from bokeh.plotting import output_file, show
-
-    from bokeh.embed import file_html
-    from bokeh.resources import CDN
-
-    graph_renderer = from_networkx(G, layout_positions)
-    node_radii = [0.02 + 0.1 * centrality[node] for node in G.nodes()]
-    graph_renderer.node_renderer.data_source.data["radius"] = node_radii
-    graph_renderer.node_renderer.data_source.data["fill_color"] = node_colors
-    graph_renderer.node_renderer.glyph = Circle(
-        radius="radius",
-        fill_color="fill_color",
-        fill_alpha=0.9,
-        line_color="#000000",
-        line_width=1.5,
-    )
-    graph_renderer.edge_renderer.glyph = MultiLine(
-        line_color="#000000",
-        line_alpha=0.3,
-        line_width=1.5,
-    )
-    p.renderers.append(graph_renderer)
-    return graph_renderer
-
-
 def graph_to_tuple(graph):
     """
     Converts a networkx graph to a tuple of (nodes, edges).

cognee/tasks/repo_processor/get_local_dependencies.py

@@ -1,14 +1,12 @@
 import os
+import logging
+import aiofiles
 import importlib
 from typing import AsyncGenerator, Optional
 from uuid import NAMESPACE_OID, uuid5
 import tree_sitter_python as tspython
 from tree_sitter import Language, Node, Parser, Tree
 
-import aiofiles
-
-import logging
-
 from cognee.low_level import DataPoint
 from cognee.shared.CodeGraphEntities import (
     CodeFile,
@@ -19,15 +17,15 @@ from cognee.shared.CodeGraphEntities import (
 
 logger = logging.getLogger(__name__)
 
-PY_LANGUAGE = Language(tspython.language())
-source_code_parser = Parser(PY_LANGUAGE)
-
 
 class FileParser:
     def __init__(self):
         self.parsed_files = {}
 
     async def parse_file(self, file_path: str) -> tuple[str, Tree]:
+        PY_LANGUAGE = Language(tspython.language())
+        source_code_parser = Parser(PY_LANGUAGE)
+
         if file_path not in self.parsed_files:
             source_code = await get_source_code(file_path)
             source_code_tree = source_code_parser.parse(bytes(source_code, "utf-8"))

cognee/tasks/repo_processor/get_repo_file_dependencies.py

@@ -8,7 +8,6 @@ from uuid import NAMESPACE_OID, uuid5
 
 from cognee.infrastructure.engine import DataPoint
 from cognee.shared.CodeGraphEntities import CodeFile, Repository
-from cognee.tasks.repo_processor.get_local_dependencies import get_local_script_dependencies
 
 
 async def get_source_code_files(repo_path):
@@ -75,6 +74,9 @@ async def get_repo_file_dependencies(
         for chunk_number in range(number_of_chunks)
     ]
 
+    # Codegraph dependencies are not installed by default, so we import where we use them.
+    from cognee.tasks.repo_processor.get_local_dependencies import get_local_script_dependencies
+
     for start_range, end_range in chunk_ranges:
         # with ProcessPoolExecutor(max_workers=12) as executor:
         tasks = [

pyproject.toml

@@ -20,53 +20,51 @@ classifiers = [
 [tool.poetry.dependencies]
 python = ">=3.10,<=3.13"
 openai = "^1.59.4"
-pydantic = "2.10.5"
 python-dotenv = "1.0.1"
-fastapi = "0.115.7"
+pydantic = "2.10.5"
-uvicorn = "0.34.0"
+pydantic-settings = "^2.2.1"
-requests = "2.32.3"
-aiohttp = "3.10.10"
 typing_extensions = "4.12.2"
-nest_asyncio = "1.6.0"
+nltk = "3.9.1"
 numpy = "1.26.4"
-datasets = "3.1.0"
+pandas = "2.2.3"
-falkordb = {version = "1.0.9", optional = true}
-kuzu = {version = "0.8.2", optional = true}
 boto3 = "^1.26.125"
 botocore="^1.35.54"
-gunicorn = "^20.1.0"
 sqlalchemy = "2.0.36"
-instructor = "1.7.2"
-networkx = "^3.2.1"
 aiosqlite = "^0.20.0"
-pandas = "2.2.3"
+tiktoken = "<=0.9.0"
+litellm = ">=1.57.4"
+instructor = "1.7.2"
+langfuse = "^2.32.0"
 filetype = "^1.2.0"
-dlt = {extras = ["sqlalchemy"], version = "^1.4.1"}
+aiohttp = "^3.11.14"
 aiofiles = "^23.2.1"
-qdrant-client = {version = "^1.9.0", optional = true}
+owlready2 = "^0.47"
 graphistry = "^0.33.5"
-tenacity = "^9.0.0"
-weaviate-client = {version = "4.9.6", optional = true}
-scikit-learn = "^1.5.0"
 pypdf = ">=4.1.0,<6.0.0"
-neo4j = {version = "^5.20.0", optional = true}
 jinja2 = "^3.1.3"
 matplotlib = "^3.8.3"
-tiktoken = "<=0.9.0"
+networkx = "^3.2.1"
+lancedb = "0.16.0"
+alembic = "^1.13.3"
+pre-commit = "^4.0.1"
+scikit-learn = "^1.6.1"
+fastapi = {version = "0.115.7", optional = true}
+fastapi-users = {version = "14.0.0", extras = ["sqlalchemy"]}
+uvicorn = {version = "0.34.0", optional = true}
+gunicorn = {version = "^20.1.0", optional = true}
+dlt = {extras = ["sqlalchemy"], version = "^1.4.1"}
+qdrant-client = {version = "^1.9.0", optional = true}
+weaviate-client = {version = "4.9.6", optional = true}
+neo4j = {version = "^5.20.0", optional = true}
+falkordb = {version = "1.0.9", optional = true}
+kuzu = {version = "0.8.2", optional = true}
+chromadb = {version = "^0.6.0", optional = true}
 langchain_text_splitters = {version = "0.3.2", optional = true}
 langsmith = {version = "0.2.3", optional = true}
-langdetect = "1.0.9"
 posthog = {version = "^3.5.0", optional = true}
-lancedb = "0.16.0"
-chromadb = "^0.6.0"
-litellm = ">=1.57.4"
 groq = {version = "0.8.0", optional = true}
-langfuse = "^2.32.0"
+anthropic = {version = "^0.26.1", optional = true}
-pydantic-settings = "^2.2.1"
-anthropic = "^0.26.1"
 sentry-sdk = {extras = ["fastapi"], version = "^2.9.0"}
-fastapi-users = {version = "14.0.0", extras = ["sqlalchemy"]}
-alembic = "^1.13.3"
 asyncpg =  {version = "0.30.0", optional = true}
 pgvector = {version = "^0.3.5", optional = true}
 psycopg2 = {version = "^2.9.10", optional = true}
@@ -75,24 +73,18 @@ deepeval = {version = "^2.0.1", optional = true}
 transformers = {version = "^4.46.3", optional = true}
 pymilvus = {version = "^2.5.0", optional = true}
 unstructured = { extras = ["csv", "doc", "docx", "epub", "md", "odt", "org", "ppt", "pptx", "rst", "rtf", "tsv", "xlsx"], version = "^0.16.13", optional = true }
-pre-commit = "^4.0.1"
-httpx = "0.27.0"
-bokeh="^3.6.2"
-nltk = "3.9.1"
-google-generativeai = {version = "^0.8.4", optional = true}
 mistral-common = {version = "^1.5.2", optional = true}
 fastembed = {version = "^0.6.0", optional = true, markers = "python_version < '3.13'"}
 tree-sitter = {version = "^0.24.0", optional = true}
 tree-sitter-python = {version = "^0.23.6", optional = true}
 plotly = {version = "^6.0.0", optional = true}
 gdown = {version = "^5.2.0", optional = true}
-pyside6 = {version = "^6.8.2.1", optional = true}
 qasync = {version = "^0.27.1", optional = true}
 graphiti-core = {version = "^0.7.0", optional = true}
-owlready2 = "^0.47"
 
 
 [tool.poetry.extras]
+api = ["fastapi", "fastapi-users", "uvicorn", "gunicorn"]
 filesystem = ["s3fs", "botocore"]
 weaviate = ["weaviate-client"]
 qdrant = ["qdrant-client"]
@@ -105,12 +97,14 @@ gemini = ["google-generativeai"]
 huggingface = ["transformers"]
 ollama = ["transformers"]
 mistral = ["mistral-common"]
+anthropic = ["anthropic"]
 deepeval = ["deepeval"]
 posthog = ["posthog"]
 falkordb = ["falkordb"]
 kuzu = ["kuzu"]
 groq = ["groq"]
 milvus = ["pymilvus"]
+chromadb = ["chromadb"]
 docs = ["unstructured"]
 codegraph = ["fastembed", "transformers", "tree-sitter", "tree-sitter-python"]
 evals = ["plotly", "gdown"]