feat: Add support for ChromaDB (#622)

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

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

# Add Support for ChromaDB

## Summary
This PR adds support for ChromaDB as a vector database option in the
Cognee application. ChromaDB is a modern, open-source embedding database
designed for AI applications.

## Changes
- Created a new ChromaDBAdapter implementation for vector database
operations
- Added comprehensive test suite for ChromaDB functionality
- Updated docker-compose.yml to include ChromaDB service
- Modified environment configuration to support ChromaDB settings
- Updated vector engine creation logic to support ChromaDB as an option

## Technical Details
- Implemented `ChromaDBAdapter.py` (347 lines) with full CRUD operations
for vector data
- Created test suite (`test_chromadb.py`) with 171 lines of test
coverage
- Updated vector engine creation process to dynamically select ChromaDB
when configured
- Modified settings router to accommodate new database option
- Updated environment template with ChromaDB configuration options

## Docker Changes
- Added ChromaDB service to docker-compose.yml with appropriate
configuration

This PR enhances Cognee's flexibility by providing an alternative vector
database option, allowing users to choose the most appropriate database
for their specific use case.



## 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

Tested with UI + tests.


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **New Features**
- Expanded vector database integration by adding support for Chromadb,
enabling enhanced data management and search functionalities.
- **Tests**
- Added automated tests to validate the Chromadb integration and related
operations.
- **Chores**
- Updated configuration guidance and dependency management to include
Chromadb.
  - Provided an optional container deployment template for Chromadb.
- Added a new entry to ignore the `.chromadb_data/` directory in version
control.
- Introduced a new GitHub Actions workflow for testing Chromadb
integration.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Co-authored-by: hajdul88 <52442977+hajdul88@users.noreply.github.com>
Co-authored-by: Vasilije <8619304+Vasilije1990@users.noreply.github.com>
This commit is contained in:
Dmitrii Galkin 2025-03-13 18:13:04 +04:00 committed by GitHub
parent daf7d4ae26
commit e147fa5bde
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
11 changed files with 1247 additions and 64 deletions

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@ -30,7 +30,7 @@ GRAPH_DATABASE_URL=
GRAPH_DATABASE_USERNAME=
GRAPH_DATABASE_PASSWORD=
# "qdrant", "pgvector", "weaviate", "milvus" or "lancedb"
# "qdrant", "pgvector", "weaviate", "milvus", "lancedb" or "chromadb"
VECTOR_DB_PROVIDER="lancedb"
# Not needed if using "lancedb" or "pgvector"
VECTOR_DB_URL=

63
.github/workflows/test_chromadb.yml vendored Normal file
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@ -0,0 +1,63 @@
name: test | chromadb
on:
workflow_dispatch:
pull_request:
types: [labeled, synchronize]
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
cancel-in-progress: true
env:
RUNTIME__LOG_LEVEL: ERROR
jobs:
run_chromadb_integration_test:
name: chromadb test
runs-on: ubuntu-22.04
defaults:
run:
shell: bash
services:
chromadb:
image: chromadb/chroma:0.6.3
volumes:
- chroma-data:/chroma/chroma
ports:
- 3002:8000
steps:
- name: Check out
uses: actions/checkout@master
- name: Setup Python
uses: actions/setup-python@v5
with:
python-version: '3.11.x'
- name: Install Poetry
uses: snok/install-poetry@v1.4.1
with:
virtualenvs-create: true
virtualenvs-in-project: true
installer-parallel: true
- name: Install dependencies
run: poetry install --no-interaction
- name: Run chromadb test
env:
ENV: 'dev'
VECTOR_DB_PROVIDER: chromadb
VECTOR_DB_URL: http://localhost:3002
VECTOR_DB_KEY: test-token
LLM_MODEL: ${{ secrets.LLM_MODEL }}
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
run: poetry run python ./cognee/tests/test_chromadb.py

3
.gitignore vendored
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@ -186,3 +186,6 @@ node_modules/
# Evals
SWE-bench_testsample/
# ChromaDB Data
.chromadb_data/

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@ -27,7 +27,13 @@ class LLMConfigInputDTO(InDTO):
class VectorDBConfigInputDTO(InDTO):
provider: Union[Literal["lancedb"], Literal["qdrant"], Literal["weaviate"], Literal["pgvector"]]
provider: Union[
Literal["lancedb"],
Literal["chromadb"],
Literal["qdrant"],
Literal["weaviate"],
Literal["pgvector"],
]
url: str
api_key: str

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@ -0,0 +1,367 @@
import logging
from typing import Dict, List, Optional, Any
import os
import json
from uuid import UUID
from chromadb import AsyncHttpClient, Settings
from cognee.exceptions import InvalidValueError
from cognee.infrastructure.engine.utils import parse_id
from cognee.infrastructure.engine import DataPoint
from cognee.infrastructure.databases.vector.models.ScoredResult import ScoredResult
from ..embeddings.EmbeddingEngine import EmbeddingEngine
from ..vector_db_interface import VectorDBInterface
from ..utils import normalize_distances
logger = logging.getLogger("ChromaDBAdapter")
class IndexSchema(DataPoint):
text: str
metadata: dict = {"index_fields": ["text"]}
def model_dump(self):
data = super().model_dump()
return process_data_for_chroma(data)
def process_data_for_chroma(data):
"""Convert complex data types to a format suitable for ChromaDB storage."""
processed_data = {}
for key, value in data.items():
if isinstance(value, UUID):
processed_data[key] = str(value)
elif isinstance(value, dict):
# Store dictionaries as JSON strings with special prefix
processed_data[f"{key}__dict"] = json.dumps(value)
elif isinstance(value, list):
# Store lists as JSON strings with special prefix
processed_data[f"{key}__list"] = json.dumps(value)
elif isinstance(value, (str, int, float, bool)) or value is None:
processed_data[key] = value
else:
processed_data[key] = str(value)
return processed_data
def restore_data_from_chroma(data):
"""Restore original data structure from ChromaDB storage format."""
restored_data = {}
dict_keys = []
list_keys = []
# First, identify all special keys
for key in data.keys():
if key.endswith("__dict"):
dict_keys.append(key)
elif key.endswith("__list"):
list_keys.append(key)
else:
restored_data[key] = data[key]
# Process dictionary fields
for key in dict_keys:
original_key = key[:-6] # Remove '__dict' suffix
try:
restored_data[original_key] = json.loads(data[key])
except Exception as e:
logger.debug(f"Error restoring dictionary from JSON: {e}")
restored_data[key] = data[key]
# Process list fields
for key in list_keys:
original_key = key[:-6] # Remove '__list' suffix
try:
restored_data[original_key] = json.loads(data[key])
except Exception as e:
logger.debug(f"Error restoring list from JSON: {e}")
restored_data[key] = data[key]
return restored_data
class ChromaDBAdapter(VectorDBInterface):
name = "ChromaDB"
url: str
api_key: str
connection: AsyncHttpClient = None
def __init__(
self, url: Optional[str], api_key: Optional[str], embedding_engine: EmbeddingEngine
):
self.embedding_engine = embedding_engine
self.url = url
self.api_key = api_key
async def get_connection(self) -> AsyncHttpClient:
if self.connection is None:
settings = Settings(
chroma_client_auth_provider="token", chroma_client_auth_credentials=self.api_key
)
self.connection = await AsyncHttpClient(host=self.url, settings=settings)
return self.connection
async def embed_data(self, data: list[str]) -> list[list[float]]:
return await self.embedding_engine.embed_text(data)
async def has_collection(self, collection_name: str) -> bool:
client = await self.get_connection()
collections = await client.list_collections()
# In ChromaDB v0.6.0, list_collections returns collection names directly
return collection_name in collections
async def create_collection(self, collection_name: str, payload_schema=None):
client = await self.get_connection()
if not await self.has_collection(collection_name):
await client.create_collection(name=collection_name, metadata={"hnsw:space": "cosine"})
async def create_data_points(self, collection_name: str, data_points: list[DataPoint]):
client = await self.get_connection()
if not await self.has_collection(collection_name):
await self.create_collection(collection_name)
collection = await client.get_collection(collection_name)
texts = [DataPoint.get_embeddable_data(data_point) for data_point in data_points]
embeddings = await self.embed_data(texts)
ids = [str(data_point.id) for data_point in data_points]
metadatas = []
for data_point in data_points:
metadata = data_point.model_dump()
metadatas.append(process_data_for_chroma(metadata))
await collection.upsert(
ids=ids, embeddings=embeddings, metadatas=metadatas, documents=texts
)
async def create_vector_index(self, index_name: str, index_property_name: str):
"""Create a vector index as a ChromaDB collection."""
await self.create_collection(f"{index_name}_{index_property_name}")
async def index_data_points(
self, index_name: str, index_property_name: str, data_points: list[DataPoint]
):
"""Index data points using the specified index property."""
await self.create_data_points(
f"{index_name}_{index_property_name}",
[
IndexSchema(
id=data_point.id,
text=getattr(data_point, data_point.metadata["index_fields"][0]),
)
for data_point in data_points
],
)
async def retrieve(self, collection_name: str, data_point_ids: list[str]):
"""Retrieve data points by their IDs from a collection."""
client = await self.get_connection()
collection = await client.get_collection(collection_name)
results = await collection.get(ids=data_point_ids, include=["metadatas"])
return [
ScoredResult(
id=parse_id(id),
payload=restore_data_from_chroma(metadata),
score=0,
)
for id, metadata in zip(results["ids"], results["metadatas"])
]
async def get_distance_from_collection_elements(
self, collection_name: str, query_text: str = None, query_vector: List[float] = None
):
"""Calculate distance between query and all elements in a collection."""
if query_text is None and query_vector is None:
raise InvalidValueError(message="One of query_text or query_vector must be provided!")
if query_text and not query_vector:
query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
client = await self.get_connection()
try:
collection = await client.get_collection(collection_name)
collection_count = await collection.count()
results = await collection.query(
query_embeddings=[query_vector],
include=["metadatas", "distances"],
n_results=collection_count,
)
result_values = []
for i, (id, metadata, distance) in enumerate(
zip(results["ids"][0], results["metadatas"][0], results["distances"][0])
):
result_values.append(
{
"id": parse_id(id),
"payload": restore_data_from_chroma(metadata),
"_distance": distance,
}
)
normalized_values = normalize_distances(result_values)
scored_results = []
for i, result in enumerate(result_values):
scored_results.append(
ScoredResult(
id=result["id"],
payload=result["payload"],
score=normalized_values[i],
)
)
return scored_results
except Exception as e:
logger.error(f"Error in get_distance_from_collection_elements: {str(e)}")
return []
async def search(
self,
collection_name: str,
query_text: str = None,
query_vector: List[float] = None,
limit: int = 5,
with_vector: bool = False,
normalized: bool = True,
):
"""Search for similar items in a collection using text or vector query."""
if query_text is None and query_vector is None:
raise InvalidValueError(message="One of query_text or query_vector must be provided!")
if query_text and not query_vector:
query_vector = (await self.embedding_engine.embed_text([query_text]))[0]
try:
client = await self.get_connection()
collection = await client.get_collection(collection_name)
results = await collection.query(
query_embeddings=[query_vector],
include=["metadatas", "distances", "embeddings"]
if with_vector
else ["metadatas", "distances"],
n_results=limit,
)
vector_list = []
for i, (id, metadata, distance) in enumerate(
zip(results["ids"][0], results["metadatas"][0], results["distances"][0])
):
item = {
"id": parse_id(id),
"payload": restore_data_from_chroma(metadata),
"_distance": distance,
}
if with_vector and "embeddings" in results:
item["vector"] = results["embeddings"][0][i]
vector_list.append(item)
# Normalize vector distance
normalized_values = normalize_distances(vector_list)
for i in range(len(normalized_values)):
vector_list[i]["score"] = normalized_values[i]
# Create and return ScoredResult objects
return [
ScoredResult(
id=row["id"],
payload=row["payload"],
score=row["score"],
vector=row.get("vector") if with_vector else None,
)
for row in vector_list
]
except Exception as e:
logger.error(f"Error in search: {str(e)}")
return []
async def batch_search(
self,
collection_name: str,
query_texts: List[str],
limit: int = 5,
with_vectors: bool = False,
):
"""Perform multiple searches in a single request for efficiency."""
query_vectors = await self.embed_data(query_texts)
client = await self.get_connection()
collection = await client.get_collection(collection_name)
results = await collection.query(
query_embeddings=query_vectors,
include=["metadatas", "distances", "embeddings"]
if with_vectors
else ["metadatas", "distances"],
n_results=limit,
)
all_results = []
for i in range(len(query_texts)):
vector_list = []
for j, (id, metadata, distance) in enumerate(
zip(results["ids"][i], results["metadatas"][i], results["distances"][i])
):
item = {
"id": parse_id(id),
"payload": restore_data_from_chroma(metadata),
"_distance": distance,
}
if with_vectors and "embeddings" in results:
item["vector"] = results["embeddings"][i][j]
vector_list.append(item)
normalized_values = normalize_distances(vector_list)
query_results = []
for j, item in enumerate(vector_list):
result = ScoredResult(
id=item["id"],
payload=item["payload"],
score=normalized_values[j],
)
if with_vectors and "embeddings" in results:
result.vector = item.get("vector")
query_results.append(result)
all_results.append(query_results)
return all_results
async def delete_data_points(self, collection_name: str, data_point_ids: list[str]):
"""Remove data points from a collection by their IDs."""
client = await self.get_connection()
collection = await client.get_collection(collection_name)
await collection.delete(ids=data_point_ids)
return True
async def prune(self):
"""Delete all collections in the ChromaDB database."""
client = await self.get_connection()
collections = await client.list_collections()
for collection_name in collections:
await client.delete_collection(collection_name)
return True
async def get_collection_names(self):
"""Get a list of all collection names in the database."""
client = await self.get_connection()
return await client.list_collections()

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@ -82,6 +82,22 @@ def create_vector_engine(
embedding_engine=embedding_engine,
)
elif vector_db_provider == "chromadb":
try:
import chromadb
except ImportError:
raise ImportError(
"ChromaDB is not installed. Please install it with 'pip install chromadb'"
)
from .chromadb.ChromaDBAdapter import ChromaDBAdapter
return ChromaDBAdapter(
url=vector_db_url,
api_key=vector_db_key,
embedding_engine=embedding_engine,
)
else:
from .lancedb.LanceDBAdapter import LanceDBAdapter

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@ -0,0 +1,169 @@
import os
import logging
import pathlib
import cognee
from cognee.modules.data.models import Data
from cognee.modules.search.types import SearchType
from cognee.modules.retrieval.utils.brute_force_triplet_search import brute_force_triplet_search
from cognee.modules.users.methods import get_default_user
logging.basicConfig(level=logging.DEBUG)
async def test_local_file_deletion(data_text, file_location):
from sqlalchemy import select
import hashlib
from cognee.infrastructure.databases.relational import get_relational_engine
engine = get_relational_engine()
async with engine.get_async_session() as session:
# Get hash of data contents
encoded_text = data_text.encode("utf-8")
data_hash = hashlib.md5(encoded_text).hexdigest()
# Get data entry from database based on hash contents
data = (await session.scalars(select(Data).where(Data.content_hash == data_hash))).one()
assert os.path.isfile(data.raw_data_location), (
f"Data location doesn't exist: {data.raw_data_location}"
)
# Test deletion of data along with local files created by cognee
await engine.delete_data_entity(data.id)
assert not os.path.exists(data.raw_data_location), (
f"Data location still exists after deletion: {data.raw_data_location}"
)
async with engine.get_async_session() as session:
# Get data entry from database based on file path
data = (
await session.scalars(select(Data).where(Data.raw_data_location == file_location))
).one()
assert os.path.isfile(data.raw_data_location), (
f"Data location doesn't exist: {data.raw_data_location}"
)
# Test local files not created by cognee won't get deleted
await engine.delete_data_entity(data.id)
assert os.path.exists(data.raw_data_location), (
f"Data location doesn't exists: {data.raw_data_location}"
)
async def test_getting_of_documents(dataset_name_1):
# Test getting of documents for search per dataset
from cognee.modules.users.permissions.methods import get_document_ids_for_user
user = await get_default_user()
document_ids = await get_document_ids_for_user(user.id, [dataset_name_1])
assert len(document_ids) == 1, (
f"Number of expected documents doesn't match {len(document_ids)} != 1"
)
# Test getting of documents for search when no dataset is provided
user = await get_default_user()
document_ids = await get_document_ids_for_user(user.id)
assert len(document_ids) == 2, (
f"Number of expected documents doesn't match {len(document_ids)} != 2"
)
async def main():
cognee.config.set_vector_db_config(
{
"vector_db_url": "http://localhost:3002",
"vector_db_key": "test-token",
"vector_db_provider": "chromadb",
}
)
data_directory_path = str(
pathlib.Path(
os.path.join(pathlib.Path(__file__).parent, ".data_storage/test_chromadb")
).resolve()
)
cognee.config.data_root_directory(data_directory_path)
cognee_directory_path = str(
pathlib.Path(
os.path.join(pathlib.Path(__file__).parent, ".cognee_system/test_chromadb")
).resolve()
)
cognee.config.system_root_directory(cognee_directory_path)
await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True)
dataset_name_1 = "natural_language"
dataset_name_2 = "quantum"
explanation_file_path = os.path.join(
pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt"
)
await cognee.add([explanation_file_path], dataset_name_1)
text = """A quantum computer is a computer that takes advantage of quantum mechanical phenomena.
At small scales, physical matter exhibits properties of both particles and waves, and quantum computing leverages this behavior, specifically quantum superposition and entanglement, using specialized hardware that supports the preparation and manipulation of quantum states.
Classical physics cannot explain the operation of these quantum devices, and a scalable quantum computer could perform some calculations exponentially faster (with respect to input size scaling) than any modern "classical" computer. In particular, a large-scale quantum computer could break widely used encryption schemes and aid physicists in performing physical simulations; however, the current state of the technology is largely experimental and impractical, with several obstacles to useful applications. Moreover, scalable quantum computers do not hold promise for many practical tasks, and for many important tasks quantum speedups are proven impossible.
The basic unit of information in quantum computing is the qubit, similar to the bit in traditional digital electronics. Unlike a classical bit, a qubit can exist in a superposition of its two "basis" states. When measuring a qubit, the result is a probabilistic output of a classical bit, therefore making quantum computers nondeterministic in general. If a quantum computer manipulates the qubit in a particular way, wave interference effects can amplify the desired measurement results. The design of quantum algorithms involves creating procedures that allow a quantum computer to perform calculations efficiently and quickly.
Physically engineering high-quality qubits has proven challenging. If a physical qubit is not sufficiently isolated from its environment, it suffers from quantum decoherence, introducing noise into calculations. Paradoxically, perfectly isolating qubits is also undesirable because quantum computations typically need to initialize qubits, perform controlled qubit interactions, and measure the resulting quantum states. Each of those operations introduces errors and suffers from noise, and such inaccuracies accumulate.
In principle, a non-quantum (classical) computer can solve the same computational problems as a quantum computer, given enough time. Quantum advantage comes in the form of time complexity rather than computability, and quantum complexity theory shows that some quantum algorithms for carefully selected tasks require exponentially fewer computational steps than the best known non-quantum algorithms. Such tasks can in theory be solved on a large-scale quantum computer whereas classical computers would not finish computations in any reasonable amount of time. However, quantum speedup is not universal or even typical across computational tasks, since basic tasks such as sorting are proven to not allow any asymptotic quantum speedup. Claims of quantum supremacy have drawn significant attention to the discipline, but are demonstrated on contrived tasks, while near-term practical use cases remain limited.
"""
await cognee.add([text], dataset_name_2)
await cognee.cognify([dataset_name_2, dataset_name_1])
from cognee.infrastructure.databases.vector import get_vector_engine
await test_getting_of_documents(dataset_name_1)
vector_engine = get_vector_engine()
random_node = (await vector_engine.search("Entity_name", "Quantum computer"))[0]
random_node_name = random_node.payload["text"]
search_results = await cognee.search(
query_type=SearchType.INSIGHTS, query_text=random_node_name
)
assert len(search_results) != 0, "The search results list is empty."
print("\n\nExtracted sentences are:\n")
for result in search_results:
print(f"{result}\n")
search_results = await cognee.search(
query_type=SearchType.CHUNKS, query_text=random_node_name, datasets=[dataset_name_2]
)
assert len(search_results) != 0, "The search results list is empty."
print("\n\nExtracted chunks are:\n")
for result in search_results:
print(f"{result}\n")
graph_completion = await cognee.search(
query_type=SearchType.GRAPH_COMPLETION,
query_text=random_node_name,
datasets=[dataset_name_2],
)
assert len(graph_completion) != 0, "Completion result is empty."
print("Completion result is:")
print(graph_completion)
search_results = await cognee.search(
query_type=SearchType.SUMMARIES, query_text=random_node_name
)
assert len(search_results) != 0, "Query related summaries don't exist."
print("\n\nExtracted summaries are:\n")
for result in search_results:
print(f"{result}\n")
history = await cognee.get_search_history()
assert len(history) == 8, "Search history is not correct."
await cognee.prune.prune_data()
assert not os.path.isdir(data_directory_path), "Local data files are not deleted"
await cognee.prune.prune_system(metadata=True)
tables_in_database = await vector_engine.get_table_names()
assert len(tables_in_database) == 0, "ChromaDB database is not empty"
if __name__ == "__main__":
import asyncio
asyncio.run(main())

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@ -60,6 +60,25 @@ services:
- 3001:3000
networks:
- cognee-network
chromadb:
image: chromadb/chroma:0.6.3
container_name: chromadb
profiles:
- chromadb
environment:
- IS_PERSISTENT=TRUE
- CHROMA_SERVER_AUTH_CREDENTIALS=${VECTOR_DB_KEY}
- CHROMA_SERVER_AUTH_CREDENTIALS_PROVIDER=chromadb.auth.token.TokenConfigServerAuthCredentialsProvider
- CHROMA_SERVER_AUTH_TOKEN_TRANSPORT_HEADER=AUTHORIZATION
- CHROMA_SERVER_AUTH_PROVIDER=chromadb.auth.token.TokenAuthServerProvider
volumes:
- .chromadb_data/:/chroma/chroma/
networks:
- cognee-network
ports:
- "3002:8000"
# UNCOMMENT IF USING POSTGRES
# postgres:
# image: pgvector/pgvector:pg17
@ -75,12 +94,12 @@ services:
# networks:
# - cognee-network
# UNCOMMENT THE VOLUES SECTION BELOW AS WELL TO USE POSTGRES
networks:
cognee-network:
name: cognee-network
# UNCOMMENT IF USING POSTGRES
# volumes:
# postgres_data:
volumes:
chromadb_data:
# UNCOMMENT IF USING POSTGRES
# postgres_data:

653
poetry.lock generated
View file

@ -344,6 +344,24 @@ types-python-dateutil = ">=2.8.10"
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@ -3108,10 +3297,9 @@ typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "t
name = "humanfriendly"
version = "10.0"
description = "Human friendly output for text interfaces using Python"
optional = true
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
groups = ["main"]
markers = "(python_version == \"3.10\" or python_version == \"3.11\" or python_version == \"3.12\") and extra == \"codegraph\""
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test = ["flufl.flake8", "importlib-resources (>=1.3) ; python_version < \"3.9\"", "jaraco.test (>=5.4)", "packaging", "pyfakefs", "pytest (>=6,!=8.1.*)", "pytest-perf (>=0.9.2)"]
type = ["pytest-mypy"]
[[package]]
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description = "Read resources from Python packages"
optional = false
python-versions = ">=3.9"
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cover = ["pytest-cov"]
doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
enabler = ["pytest-enabler (>=2.2)"]
test = ["jaraco.test (>=5.4)", "pytest (>=6,!=8.1.*)", "zipp (>=3.17)"]
type = ["pytest-mypy"]
[[package]]
name = "iniconfig"
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@ -3988,6 +4196,34 @@ files = [
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python-versions = ">=3.6"
groups = ["main"]
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durationpy = ">=0.7"
google-auth = ">=1.0.1"
oauthlib = ">=3.2.2"
python-dateutil = ">=2.5.3"
pyyaml = ">=5.4.1"
requests = "*"
requests-oauthlib = "*"
six = ">=1.9.0"
urllib3 = ">=1.24.2"
websocket-client = ">=0.32.0,<0.40.0 || >0.40.0,<0.41.dev0 || >=0.43.dev0"
[package.extras]
adal = ["adal (>=1.0.2)"]
[[package]]
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@ -5218,10 +5454,9 @@ mkdocstrings = ">=0.26"
name = "mmh3"
version = "5.1.0"
description = "Python extension for MurmurHash (MurmurHash3), a set of fast and robust hash functions."
optional = true
optional = false
python-versions = ">=3.9"
groups = ["main"]
markers = "(python_version == \"3.10\" or python_version == \"3.11\" or python_version == \"3.12\") and extra == \"codegraph\""
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@ -5318,10 +5553,9 @@ type = ["mypy (==1.14.1)"]
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description = "An implementation of time.monotonic() for Python 2 & < 3.3"
optional = true
optional = false
python-versions = "*"
groups = ["main"]
markers = "extra == \"posthog\""
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@ -5331,10 +5565,9 @@ files = [
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optional = true
optional = false
python-versions = "*"
groups = ["main"]
markers = "(python_version == \"3.10\" or python_version == \"3.11\" or python_version == \"3.12\") and extra == \"codegraph\""
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description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic"
optional = false
python-versions = ">=3.6"
groups = ["dev"]
groups = ["main", "dev"]
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description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
optional = true
optional = false
python-versions = ">=3.10"
groups = ["main"]
markers = "(python_version == \"3.10\" or python_version == \"3.11\" or python_version == \"3.12\") and extra == \"codegraph\""
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name = "opentelemetry-api"
version = "1.30.0"
description = "OpenTelemetry Python API"
optional = true
optional = false
python-versions = ">=3.8"
groups = ["main"]
markers = "extra == \"deepeval\""
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name = "opentelemetry-exporter-otlp-proto-common"
version = "1.30.0"
description = "OpenTelemetry Protobuf encoding"
optional = true
optional = false
python-versions = ">=3.8"
groups = ["main"]
markers = "extra == \"deepeval\""
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@ -6001,10 +6231,9 @@ opentelemetry-proto = "1.30.0"
name = "opentelemetry-exporter-otlp-proto-grpc"
version = "1.30.0"
description = "OpenTelemetry Collector Protobuf over gRPC Exporter"
optional = true
optional = false
python-versions = ">=3.8"
groups = ["main"]
markers = "extra == \"deepeval\""
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opentelemetry-proto = "1.30.0"
opentelemetry-sdk = ">=1.30.0,<1.31.0"
[[package]]
name = "opentelemetry-instrumentation"
version = "0.51b0"
description = "Instrumentation Tools & Auto Instrumentation for OpenTelemetry Python"
optional = false
python-versions = ">=3.8"
groups = ["main"]
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wrapt = ">=1.0.0,<2.0.0"
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optional = false
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groups = ["main"]
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opentelemetry-semantic-conventions = "0.51b0"
opentelemetry-util-http = "0.51b0"
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opentelemetry-semantic-conventions = "0.51b0"
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version = "1.30.0"
description = "OpenTelemetry Python Proto"
optional = true
optional = false
python-versions = ">=3.8"
groups = ["main"]
markers = "extra == \"deepeval\""
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name = "opentelemetry-sdk"
version = "1.30.0"
description = "OpenTelemetry Python SDK"
optional = true
optional = false
python-versions = ">=3.8"
groups = ["main"]
markers = "extra == \"deepeval\""
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optional = false
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@ -10337,9 +10797,9 @@ markers = {main = "extra == \"docs\""}
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version = "1.8.0"
description = "WebSocket client for Python with low level API options"
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optional = ["python-socks", "wsaccel"]
test = ["websockets"]
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[[package]]
name = "win-precise-time"
version = "1.4.2"
@ -10799,4 +11338,4 @@ weaviate = ["weaviate-client"]
[metadata]
lock-version = "2.1"
python-versions = ">=3.10,<=3.13"
content-hash = "d7c274c557b0d86891baf091c00ba85df5f94c784ff4fbf925028607da8f5eb3"
content-hash = "49992df84ea53be0143c8dbc7bd11cbb97ca762714c61dabd1527c8c3e42fd6b"

View file

@ -57,6 +57,7 @@ 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"