Merge branch 'dev' into chore/update-cognee-ui-cli-mcp-docker-image
This commit is contained in:
Boris
GitHub
commit
b01cf4e6bd
27 changed files with 244 additions and 364 deletions
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@ -30,6 +30,9 @@ EMBEDDING_DIMENSIONS=3072
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EMBEDDING_MAX_TOKENS=8191
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# If embedding key is not provided same key set for LLM_API_KEY will be used
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#EMBEDDING_API_KEY="your_api_key"
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# Note: OpenAI support up to 2048 elements and Gemini supports a maximum of 100 elements in an embedding batch,
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# Cognee sets the optimal batch size for OpenAI and Gemini, but a custom size can be defined if necessary for other models
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#EMBEDDING_BATCH_SIZE=2048
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# If using BAML structured output these env variables will be used
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BAML_LLM_PROVIDER=openai
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@ -52,18 +55,18 @@ BAML_LLM_API_VERSION=""
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################################################################################
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# Configure storage backend (local filesystem or S3)
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# STORAGE_BACKEND="local" # Default: uses local filesystem
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#
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#
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# -- To switch to S3 storage, uncomment and fill these: ---------------------
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# STORAGE_BACKEND="s3"
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# STORAGE_BUCKET_NAME="your-bucket-name"
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# AWS_REGION="us-east-1"
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# AWS_ACCESS_KEY_ID="your-access-key"
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# AWS_SECRET_ACCESS_KEY="your-secret-key"
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#
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#
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# -- S3 Root Directories (optional) -----------------------------------------
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# DATA_ROOT_DIRECTORY="s3://your-bucket/cognee/data"
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# SYSTEM_ROOT_DIRECTORY="s3://your-bucket/cognee/system"
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#
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#
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# -- Cache Directory (auto-configured for S3) -------------------------------
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# When STORAGE_BACKEND=s3, cache automatically uses S3: s3://BUCKET/cognee/cache
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# To override the automatic S3 cache location, uncomment:
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|
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2
.github/actions/cognee_setup/action.yml
vendored
2
.github/actions/cognee_setup/action.yml
vendored
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@ -41,4 +41,4 @@ runs:
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EXTRA_ARGS="$EXTRA_ARGS --extra $extra"
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done
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fi
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uv sync --extra api --extra docs --extra evals --extra gemini --extra codegraph --extra ollama --extra dev --extra neo4j $EXTRA_ARGS
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uv sync --extra api --extra docs --extra evals --extra codegraph --extra ollama --extra dev --extra neo4j $EXTRA_ARGS
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4
.github/workflows/test_llms.yml
vendored
4
.github/workflows/test_llms.yml
vendored
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@ -27,7 +27,7 @@ jobs:
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env:
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LLM_PROVIDER: "gemini"
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LLM_API_KEY: ${{ secrets.GEMINI_API_KEY }}
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LLM_MODEL: "gemini/gemini-1.5-flash"
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LLM_MODEL: "gemini/gemini-2.0-flash"
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EMBEDDING_PROVIDER: "gemini"
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EMBEDDING_API_KEY: ${{ secrets.GEMINI_API_KEY }}
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EMBEDDING_MODEL: "gemini/text-embedding-004"
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@ -83,4 +83,4 @@ jobs:
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EMBEDDING_MODEL: "openai/text-embedding-3-large"
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EMBEDDING_DIMENSIONS: "3072"
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EMBEDDING_MAX_TOKENS: "8191"
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run: uv run python ./examples/python/simple_example.py
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run: uv run python ./examples/python/simple_example.py
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@ -31,7 +31,7 @@ COPY README.md pyproject.toml uv.lock entrypoint.sh ./
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# Install the project's dependencies using the lockfile and settings
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra gemini --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-install-project --no-dev --no-editable
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uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-install-project --no-dev --no-editable
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# Copy Alembic configuration
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COPY alembic.ini /app/alembic.ini
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@ -42,7 +42,7 @@ COPY alembic/ /app/alembic
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COPY ./cognee /app/cognee
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COPY ./distributed /app/distributed
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RUN --mount=type=cache,target=/root/.cache/uv \
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uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra gemini --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-dev --no-editable
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uv sync --extra debug --extra api --extra postgres --extra neo4j --extra llama-index --extra ollama --extra mistral --extra groq --extra anthropic --frozen --no-dev --no-editable
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FROM python:3.12-slim-bookworm
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@ -8,6 +8,7 @@ requires-python = ">=3.10"
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dependencies = [
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# For local cognee repo usage remove comment bellow and add absolute path to cognee. Then run `uv sync --reinstall` in the mcp folder on local cognee changes.
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#"cognee[postgres,codegraph,gemini,huggingface,docs,neo4j] @ file:/Users/igorilic/Desktop/cognee",
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# TODO: Remove gemini from optional dependecnies for new Cognee version after 0.3.4
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"cognee[postgres,codegraph,gemini,huggingface,docs,neo4j]==0.3.4",
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"fastmcp>=2.10.0,<3.0.0",
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"mcp>=1.12.0,<2.0.0",
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@ -148,7 +148,9 @@ async def add(
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await setup()
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user, authorized_dataset = await resolve_authorized_user_dataset(dataset_id, dataset_name, user)
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user, authorized_dataset = await resolve_authorized_user_dataset(
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dataset_name=dataset_name, dataset_id=dataset_id, user=user
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)
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await reset_dataset_pipeline_run_status(
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authorized_dataset.id, user, pipeline_names=["add_pipeline", "cognify_pipeline"]
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@ -627,7 +627,6 @@ def start_ui(
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env=env,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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text=True,
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shell=True,
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)
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else:
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|
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@ -637,7 +636,6 @@ def start_ui(
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env=env,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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text=True,
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preexec_fn=os.setsid if hasattr(os, "setsid") else None,
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)
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|
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|
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@ -34,3 +34,12 @@ class EmbeddingEngine(Protocol):
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- int: An integer representing the number of dimensions in the embedding vector.
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"""
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raise NotImplementedError()
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def get_batch_size(self) -> int:
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"""
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Return the desired batch size for embedding calls
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|
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Returns:
|
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|
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"""
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raise NotImplementedError()
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|
|
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|||
|
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@ -42,11 +42,13 @@ class FastembedEmbeddingEngine(EmbeddingEngine):
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model: Optional[str] = "openai/text-embedding-3-large",
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dimensions: Optional[int] = 3072,
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max_completion_tokens: int = 512,
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batch_size: int = 100,
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):
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self.model = model
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self.dimensions = dimensions
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self.max_completion_tokens = max_completion_tokens
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self.tokenizer = self.get_tokenizer()
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self.batch_size = batch_size
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# self.retry_count = 0
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self.embedding_model = TextEmbedding(model_name=model)
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|
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@ -101,6 +103,15 @@ class FastembedEmbeddingEngine(EmbeddingEngine):
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"""
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return self.dimensions
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|
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def get_batch_size(self) -> int:
|
||||
"""
|
||||
Return the desired batch size for embedding calls
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||||
|
||||
Returns:
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||||
|
||||
"""
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||||
return self.batch_size
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|
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def get_tokenizer(self):
|
||||
"""
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||||
Instantiate and return the tokenizer used for preparing text for embedding.
|
||||
|
|
|
|||
|
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@ -58,6 +58,7 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
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endpoint: str = None,
|
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api_version: str = None,
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||||
max_completion_tokens: int = 512,
|
||||
batch_size: int = 100,
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):
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self.api_key = api_key
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self.endpoint = endpoint
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|
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@ -68,6 +69,7 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
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self.max_completion_tokens = max_completion_tokens
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self.tokenizer = self.get_tokenizer()
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self.retry_count = 0
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self.batch_size = batch_size
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enable_mocking = os.getenv("MOCK_EMBEDDING", "false")
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if isinstance(enable_mocking, bool):
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|
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@ -165,6 +167,15 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
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"""
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return self.dimensions
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def get_batch_size(self) -> int:
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"""
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Return the desired batch size for embedding calls
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|
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Returns:
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||||
|
||||
"""
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return self.batch_size
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def get_tokenizer(self):
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"""
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Load and return the appropriate tokenizer for the specified model based on the provider.
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|
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@ -183,9 +194,15 @@ class LiteLLMEmbeddingEngine(EmbeddingEngine):
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model=model, max_completion_tokens=self.max_completion_tokens
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)
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elif "gemini" in self.provider.lower():
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tokenizer = GeminiTokenizer(
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model=model, max_completion_tokens=self.max_completion_tokens
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# Since Gemini tokenization needs to send an API request to get the token count we will use TikToken to
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# count tokens as we calculate tokens word by word
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tokenizer = TikTokenTokenizer(
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model=None, max_completion_tokens=self.max_completion_tokens
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)
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# Note: Gemini Tokenizer expects an LLM model as input and not the embedding model
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# tokenizer = GeminiTokenizer(
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# llm_model=llm_model, max_completion_tokens=self.max_completion_tokens
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# )
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elif "mistral" in self.provider.lower():
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tokenizer = MistralTokenizer(
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model=model, max_completion_tokens=self.max_completion_tokens
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|
|
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|||
|
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@ -54,12 +54,14 @@ class OllamaEmbeddingEngine(EmbeddingEngine):
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max_completion_tokens: int = 512,
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||||
endpoint: Optional[str] = "http://localhost:11434/api/embeddings",
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huggingface_tokenizer: str = "Salesforce/SFR-Embedding-Mistral",
|
||||
batch_size: int = 100,
|
||||
):
|
||||
self.model = model
|
||||
self.dimensions = dimensions
|
||||
self.max_completion_tokens = max_completion_tokens
|
||||
self.endpoint = endpoint
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||||
self.huggingface_tokenizer_name = huggingface_tokenizer
|
||||
self.batch_size = batch_size
|
||||
self.tokenizer = self.get_tokenizer()
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||||
|
||||
enable_mocking = os.getenv("MOCK_EMBEDDING", "false")
|
||||
|
|
@ -122,6 +124,15 @@ class OllamaEmbeddingEngine(EmbeddingEngine):
|
|||
"""
|
||||
return self.dimensions
|
||||
|
||||
def get_batch_size(self) -> int:
|
||||
"""
|
||||
Return the desired batch size for embedding calls
|
||||
|
||||
Returns:
|
||||
|
||||
"""
|
||||
return self.batch_size
|
||||
|
||||
def get_tokenizer(self):
|
||||
"""
|
||||
Load and return a HuggingFace tokenizer for the embedding engine.
|
||||
|
|
|
|||
|
|
@ -19,9 +19,17 @@ class EmbeddingConfig(BaseSettings):
|
|||
embedding_api_key: Optional[str] = None
|
||||
embedding_api_version: Optional[str] = None
|
||||
embedding_max_completion_tokens: Optional[int] = 8191
|
||||
embedding_batch_size: Optional[int] = None
|
||||
huggingface_tokenizer: Optional[str] = None
|
||||
model_config = SettingsConfigDict(env_file=".env", extra="allow")
|
||||
|
||||
def model_post_init(self, __context) -> None:
|
||||
# If embedding batch size is not defined use 2048 as default for OpenAI and 100 for all other embedding models
|
||||
if not self.embedding_batch_size and self.embedding_provider.lower() == "openai":
|
||||
self.embedding_batch_size = 2048
|
||||
elif not self.embedding_batch_size:
|
||||
self.embedding_batch_size = 100
|
||||
|
||||
def to_dict(self) -> dict:
|
||||
"""
|
||||
Serialize all embedding configuration settings to a dictionary.
|
||||
|
|
|
|||
|
|
@ -31,6 +31,7 @@ def get_embedding_engine() -> EmbeddingEngine:
|
|||
config.embedding_endpoint,
|
||||
config.embedding_api_key,
|
||||
config.embedding_api_version,
|
||||
config.embedding_batch_size,
|
||||
config.huggingface_tokenizer,
|
||||
llm_config.llm_api_key,
|
||||
llm_config.llm_provider,
|
||||
|
|
@ -46,6 +47,7 @@ def create_embedding_engine(
|
|||
embedding_endpoint,
|
||||
embedding_api_key,
|
||||
embedding_api_version,
|
||||
embedding_batch_size,
|
||||
huggingface_tokenizer,
|
||||
llm_api_key,
|
||||
llm_provider,
|
||||
|
|
@ -84,6 +86,7 @@ def create_embedding_engine(
|
|||
model=embedding_model,
|
||||
dimensions=embedding_dimensions,
|
||||
max_completion_tokens=embedding_max_completion_tokens,
|
||||
batch_size=embedding_batch_size,
|
||||
)
|
||||
|
||||
if embedding_provider == "ollama":
|
||||
|
|
@ -95,6 +98,7 @@ def create_embedding_engine(
|
|||
max_completion_tokens=embedding_max_completion_tokens,
|
||||
endpoint=embedding_endpoint,
|
||||
huggingface_tokenizer=huggingface_tokenizer,
|
||||
batch_size=embedding_batch_size,
|
||||
)
|
||||
|
||||
from .LiteLLMEmbeddingEngine import LiteLLMEmbeddingEngine
|
||||
|
|
@ -108,4 +112,5 @@ def create_embedding_engine(
|
|||
model=embedding_model,
|
||||
dimensions=embedding_dimensions,
|
||||
max_completion_tokens=embedding_max_completion_tokens,
|
||||
batch_size=embedding_batch_size,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,115 +1,155 @@
|
|||
import litellm
|
||||
from pydantic import BaseModel
|
||||
from typing import Type, Optional
|
||||
from litellm import acompletion, JSONSchemaValidationError
|
||||
"""Adapter for Generic API LLM provider API"""
|
||||
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.modules.observability.get_observe import get_observe
|
||||
from cognee.infrastructure.llm.exceptions import MissingSystemPromptPathError
|
||||
import litellm
|
||||
import instructor
|
||||
from typing import Type
|
||||
from pydantic import BaseModel
|
||||
from openai import ContentFilterFinishReasonError
|
||||
from litellm.exceptions import ContentPolicyViolationError
|
||||
from instructor.core import InstructorRetryException
|
||||
|
||||
from cognee.infrastructure.llm.exceptions import ContentPolicyFilterError
|
||||
from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import (
|
||||
LLMInterface,
|
||||
)
|
||||
from cognee.infrastructure.llm.LLMGateway import LLMGateway
|
||||
from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import (
|
||||
rate_limit_async,
|
||||
sleep_and_retry_async,
|
||||
)
|
||||
|
||||
logger = get_logger()
|
||||
observe = get_observe()
|
||||
|
||||
|
||||
class GeminiAdapter(LLMInterface):
|
||||
"""
|
||||
Handles interactions with a language model API.
|
||||
Adapter for Gemini API LLM provider.
|
||||
|
||||
Public methods include:
|
||||
- acreate_structured_output
|
||||
- show_prompt
|
||||
This class initializes the API adapter with necessary credentials and configurations for
|
||||
interacting with the gemini LLM models. It provides methods for creating structured outputs
|
||||
based on user input and system prompts.
|
||||
|
||||
Public methods:
|
||||
- acreate_structured_output(text_input: str, system_prompt: str, response_model:
|
||||
Type[BaseModel]) -> BaseModel
|
||||
"""
|
||||
|
||||
MAX_RETRIES = 5
|
||||
name: str
|
||||
model: str
|
||||
api_key: str
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
endpoint,
|
||||
api_key: str,
|
||||
model: str,
|
||||
api_version: str,
|
||||
max_completion_tokens: int,
|
||||
endpoint: Optional[str] = None,
|
||||
api_version: Optional[str] = None,
|
||||
streaming: bool = False,
|
||||
) -> None:
|
||||
self.api_key = api_key
|
||||
fallback_model: str = None,
|
||||
fallback_api_key: str = None,
|
||||
fallback_endpoint: str = None,
|
||||
):
|
||||
self.model = model
|
||||
self.api_key = api_key
|
||||
self.endpoint = endpoint
|
||||
self.api_version = api_version
|
||||
self.streaming = streaming
|
||||
self.max_completion_tokens = max_completion_tokens
|
||||
|
||||
@observe(as_type="generation")
|
||||
self.fallback_model = fallback_model
|
||||
self.fallback_api_key = fallback_api_key
|
||||
self.fallback_endpoint = fallback_endpoint
|
||||
|
||||
self.aclient = instructor.from_litellm(litellm.acompletion, mode=instructor.Mode.JSON)
|
||||
|
||||
@sleep_and_retry_async()
|
||||
@rate_limit_async
|
||||
async def acreate_structured_output(
|
||||
self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
|
||||
) -> BaseModel:
|
||||
"""
|
||||
Generate structured output from the language model based on the provided input and
|
||||
system prompt.
|
||||
Generate a response from a user query.
|
||||
|
||||
This method handles retries and raises a ValueError if the request fails or the response
|
||||
does not conform to the expected schema, logging errors accordingly.
|
||||
This asynchronous method sends a user query and a system prompt to a language model and
|
||||
retrieves the generated response. It handles API communication and retries up to a
|
||||
specified limit in case of request failures.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
|
||||
- text_input (str): The user input text to generate a response for.
|
||||
- system_prompt (str): The system's prompt or context to influence the language
|
||||
model's generation.
|
||||
- response_model (Type[BaseModel]): A model type indicating the expected format of
|
||||
the response.
|
||||
- text_input (str): The input text from the user to generate a response for.
|
||||
- system_prompt (str): A prompt that provides context or instructions for the
|
||||
response generation.
|
||||
- response_model (Type[BaseModel]): A Pydantic model that defines the structure of
|
||||
the expected response.
|
||||
|
||||
Returns:
|
||||
--------
|
||||
|
||||
- BaseModel: Returns the generated response as an instance of the specified response
|
||||
model.
|
||||
- BaseModel: An instance of the specified response model containing the structured
|
||||
output from the language model.
|
||||
"""
|
||||
|
||||
try:
|
||||
if response_model is str:
|
||||
response_schema = {"type": "string"}
|
||||
else:
|
||||
response_schema = response_model
|
||||
return await self.aclient.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"""{text_input}""",
|
||||
},
|
||||
{
|
||||
"role": "system",
|
||||
"content": system_prompt,
|
||||
},
|
||||
],
|
||||
api_key=self.api_key,
|
||||
max_retries=5,
|
||||
api_base=self.endpoint,
|
||||
api_version=self.api_version,
|
||||
response_model=response_model,
|
||||
)
|
||||
except (
|
||||
ContentFilterFinishReasonError,
|
||||
ContentPolicyViolationError,
|
||||
InstructorRetryException,
|
||||
) as error:
|
||||
if (
|
||||
isinstance(error, InstructorRetryException)
|
||||
and "content management policy" not in str(error).lower()
|
||||
):
|
||||
raise error
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": system_prompt},
|
||||
{"role": "user", "content": text_input},
|
||||
]
|
||||
|
||||
try:
|
||||
response = await acompletion(
|
||||
model=f"{self.model}",
|
||||
messages=messages,
|
||||
api_key=self.api_key,
|
||||
max_completion_tokens=self.max_completion_tokens,
|
||||
temperature=0.1,
|
||||
response_format=response_schema,
|
||||
timeout=100,
|
||||
num_retries=self.MAX_RETRIES,
|
||||
if not (self.fallback_model and self.fallback_api_key and self.fallback_endpoint):
|
||||
raise ContentPolicyFilterError(
|
||||
f"The provided input contains content that is not aligned with our content policy: {text_input}"
|
||||
)
|
||||
|
||||
if response.choices and response.choices[0].message.content:
|
||||
content = response.choices[0].message.content
|
||||
if response_model is str:
|
||||
return content
|
||||
return response_model.model_validate_json(content)
|
||||
|
||||
except litellm.exceptions.BadRequestError as e:
|
||||
logger.error(f"Bad request error: {str(e)}")
|
||||
raise ValueError(f"Invalid request: {str(e)}")
|
||||
|
||||
raise ValueError("Failed to get valid response after retries")
|
||||
|
||||
except JSONSchemaValidationError as e:
|
||||
logger.error(f"Schema validation failed: {str(e)}")
|
||||
logger.debug(f"Raw response: {e.raw_response}")
|
||||
raise ValueError(f"Response failed schema validation: {str(e)}")
|
||||
try:
|
||||
return await self.aclient.chat.completions.create(
|
||||
model=self.fallback_model,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"""{text_input}""",
|
||||
},
|
||||
{
|
||||
"role": "system",
|
||||
"content": system_prompt,
|
||||
},
|
||||
],
|
||||
max_retries=5,
|
||||
api_key=self.fallback_api_key,
|
||||
api_base=self.fallback_endpoint,
|
||||
response_model=response_model,
|
||||
)
|
||||
except (
|
||||
ContentFilterFinishReasonError,
|
||||
ContentPolicyViolationError,
|
||||
InstructorRetryException,
|
||||
) as error:
|
||||
if (
|
||||
isinstance(error, InstructorRetryException)
|
||||
and "content management policy" not in str(error).lower()
|
||||
):
|
||||
raise error
|
||||
else:
|
||||
raise ContentPolicyFilterError(
|
||||
f"The provided input contains content that is not aligned with our content policy: {text_input}"
|
||||
)
|
||||
|
|
|
|||
|
|
@ -6,7 +6,7 @@ from typing import Type
|
|||
from pydantic import BaseModel
|
||||
from openai import ContentFilterFinishReasonError
|
||||
from litellm.exceptions import ContentPolicyViolationError
|
||||
from instructor.exceptions import InstructorRetryException
|
||||
from instructor.core import InstructorRetryException
|
||||
|
||||
from cognee.infrastructure.llm.exceptions import ContentPolicyFilterError
|
||||
from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import (
|
||||
|
|
@ -56,9 +56,7 @@ class GenericAPIAdapter(LLMInterface):
|
|||
self.fallback_api_key = fallback_api_key
|
||||
self.fallback_endpoint = fallback_endpoint
|
||||
|
||||
self.aclient = instructor.from_litellm(
|
||||
litellm.acompletion, mode=instructor.Mode.JSON, api_key=api_key
|
||||
)
|
||||
self.aclient = instructor.from_litellm(litellm.acompletion, mode=instructor.Mode.JSON)
|
||||
|
||||
@sleep_and_retry_async()
|
||||
@rate_limit_async
|
||||
|
|
@ -102,6 +100,7 @@ class GenericAPIAdapter(LLMInterface):
|
|||
},
|
||||
],
|
||||
max_retries=5,
|
||||
api_key=self.api_key,
|
||||
api_base=self.endpoint,
|
||||
response_model=response_model,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -143,7 +143,6 @@ def get_llm_client(raise_api_key_error: bool = True):
|
|||
max_completion_tokens=max_completion_tokens,
|
||||
endpoint=llm_config.llm_endpoint,
|
||||
api_version=llm_config.llm_api_version,
|
||||
streaming=llm_config.llm_streaming,
|
||||
)
|
||||
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -5,15 +5,13 @@ from typing import Type
|
|||
from pydantic import BaseModel
|
||||
from openai import ContentFilterFinishReasonError
|
||||
from litellm.exceptions import ContentPolicyViolationError
|
||||
from instructor.exceptions import InstructorRetryException
|
||||
from instructor.core import InstructorRetryException
|
||||
|
||||
from cognee.infrastructure.llm.LLMGateway import LLMGateway
|
||||
from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import (
|
||||
LLMInterface,
|
||||
)
|
||||
from cognee.infrastructure.llm.exceptions import (
|
||||
ContentPolicyFilterError,
|
||||
MissingSystemPromptPathError,
|
||||
)
|
||||
from cognee.infrastructure.files.utils.open_data_file import open_data_file
|
||||
from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import (
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@ from typing import List, Any
|
|||
from ..tokenizer_interface import TokenizerInterface
|
||||
|
||||
|
||||
# NOTE: DEPRECATED as to count tokens you need to send an API request to Google it is too slow to use with Cognee
|
||||
class GeminiTokenizer(TokenizerInterface):
|
||||
"""
|
||||
Implements a tokenizer interface for the Gemini model, managing token extraction and
|
||||
|
|
@ -16,10 +17,10 @@ class GeminiTokenizer(TokenizerInterface):
|
|||
|
||||
def __init__(
|
||||
self,
|
||||
model: str,
|
||||
llm_model: str,
|
||||
max_completion_tokens: int = 3072,
|
||||
):
|
||||
self.model = model
|
||||
self.llm_model = llm_model
|
||||
self.max_completion_tokens = max_completion_tokens
|
||||
|
||||
# Get LLM API key from config
|
||||
|
|
@ -28,12 +29,11 @@ class GeminiTokenizer(TokenizerInterface):
|
|||
get_llm_config,
|
||||
)
|
||||
|
||||
config = get_embedding_config()
|
||||
llm_config = get_llm_config()
|
||||
|
||||
import google.generativeai as genai
|
||||
from google import genai
|
||||
|
||||
genai.configure(api_key=config.embedding_api_key or llm_config.llm_api_key)
|
||||
self.client = genai.Client(api_key=llm_config.llm_api_key)
|
||||
|
||||
def extract_tokens(self, text: str) -> List[Any]:
|
||||
"""
|
||||
|
|
@ -77,6 +77,7 @@ class GeminiTokenizer(TokenizerInterface):
|
|||
|
||||
- int: The number of tokens in the given text.
|
||||
"""
|
||||
import google.generativeai as genai
|
||||
|
||||
return len(genai.embed_content(model=f"models/{self.model}", content=text))
|
||||
tokens_response = self.client.models.count_tokens(model=self.llm_model, contents=text)
|
||||
|
||||
return tokens_response.total_tokens
|
||||
|
|
|
|||
|
|
@ -1,47 +1,31 @@
|
|||
from uuid import UUID
|
||||
from typing import Optional
|
||||
|
||||
from cognee.api.v1.exceptions import DatasetNotFoundError
|
||||
from cognee.modules.users.models import User
|
||||
from cognee.modules.users.methods import get_default_user
|
||||
from cognee.modules.data.methods import (
|
||||
create_authorized_dataset,
|
||||
get_authorized_dataset,
|
||||
get_authorized_dataset_by_name,
|
||||
from cognee.modules.pipelines.layers.resolve_authorized_user_datasets import (
|
||||
resolve_authorized_user_datasets,
|
||||
)
|
||||
|
||||
|
||||
async def resolve_authorized_user_dataset(dataset_id: UUID, dataset_name: str, user: User):
|
||||
async def resolve_authorized_user_dataset(
|
||||
dataset_name: str, dataset_id: Optional[UUID] = None, user: Optional[User] = None
|
||||
):
|
||||
"""
|
||||
Function handles creation and dataset authorization if dataset already exist for Cognee.
|
||||
Verifies that provided user has necessary permission for provided Dataset.
|
||||
If Dataset does not exist creates the Dataset and gives permission for the user creating the dataset.
|
||||
|
||||
Args:
|
||||
dataset_id: Id of the dataset.
|
||||
dataset_name: Name of the dataset.
|
||||
dataset_id: Id of the dataset.
|
||||
user: Cognee User request is being processed for, if None default user will be used.
|
||||
|
||||
Returns:
|
||||
Tuple[User, Dataset]: A tuple containing the user and the authorized dataset.
|
||||
"""
|
||||
if not user:
|
||||
user = await get_default_user()
|
||||
|
||||
if dataset_id:
|
||||
authorized_dataset = await get_authorized_dataset(user, dataset_id, "write")
|
||||
elif dataset_name:
|
||||
authorized_dataset = await get_authorized_dataset_by_name(dataset_name, user, "write")
|
||||
user, authorized_datasets = await resolve_authorized_user_datasets(
|
||||
datasets=dataset_id if dataset_id else dataset_name, user=user
|
||||
)
|
||||
|
||||
if not authorized_dataset:
|
||||
authorized_dataset = await create_authorized_dataset(
|
||||
dataset_name=dataset_name, user=user
|
||||
)
|
||||
else:
|
||||
raise ValueError("Either dataset_id or dataset_name must be provided.")
|
||||
|
||||
if not authorized_dataset:
|
||||
raise DatasetNotFoundError(
|
||||
message=f"Dataset ({str(dataset_id) or dataset_name}) not found."
|
||||
)
|
||||
|
||||
return user, authorized_dataset
|
||||
return user, authorized_datasets[0]
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
from uuid import UUID
|
||||
from typing import Union, Tuple, List
|
||||
from typing import Union, Tuple, List, Optional
|
||||
|
||||
from cognee.modules.users.methods import get_default_user
|
||||
from cognee.modules.users.models import User
|
||||
|
|
@ -13,7 +13,7 @@ from cognee.modules.data.methods import (
|
|||
|
||||
|
||||
async def resolve_authorized_user_datasets(
|
||||
datasets: Union[str, UUID, list[str], list[UUID]], user: User = None
|
||||
datasets: Union[str, UUID, list[str], list[UUID]], user: Optional[User] = None
|
||||
) -> Tuple[User, List[Dataset]]:
|
||||
"""
|
||||
Function handles creation and dataset authorization if datasets already exist for Cognee.
|
||||
|
|
|
|||
|
|
@ -19,7 +19,9 @@ from cognee.modules.search.types import (
|
|||
from cognee.modules.search.operations import log_query, log_result
|
||||
from cognee.modules.users.models import User
|
||||
from cognee.modules.data.models import Dataset
|
||||
from cognee.modules.users.permissions.methods import get_specific_user_permission_datasets
|
||||
from cognee.modules.data.methods.get_authorized_existing_datasets import (
|
||||
get_authorized_existing_datasets,
|
||||
)
|
||||
|
||||
from .get_search_type_tools import get_search_type_tools
|
||||
from .no_access_control_search import no_access_control_search
|
||||
|
|
@ -202,7 +204,9 @@ async def authorized_search(
|
|||
Not to be used outside of active access control mode.
|
||||
"""
|
||||
# Find datasets user has read access for (if datasets are provided only return them. Provided user has read access)
|
||||
search_datasets = await get_specific_user_permission_datasets(user.id, "read", dataset_ids)
|
||||
search_datasets = await get_authorized_existing_datasets(
|
||||
datasets=dataset_ids, permission_type="read", user=user
|
||||
)
|
||||
|
||||
if use_combined_context:
|
||||
search_responses = await search_in_datasets_context(
|
||||
|
|
|
|||
|
|
@ -39,7 +39,7 @@ async def index_data_points(data_points: list[DataPoint]):
|
|||
field_name = index_name_and_field[first_occurence + 1 :]
|
||||
try:
|
||||
# In case the amount of indexable points is too large we need to send them in batches
|
||||
batch_size = 100
|
||||
batch_size = vector_engine.embedding_engine.get_batch_size()
|
||||
for i in range(0, len(indexable_points), batch_size):
|
||||
batch = indexable_points[i : i + batch_size]
|
||||
await vector_engine.index_data_points(index_name, field_name, batch)
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ from cognee.modules.graph.models.EdgeType import EdgeType
|
|||
logger = get_logger(level=ERROR)
|
||||
|
||||
|
||||
async def index_graph_edges(batch_size: int = 1024):
|
||||
async def index_graph_edges():
|
||||
"""
|
||||
Indexes graph edges by creating and managing vector indexes for relationship types.
|
||||
|
||||
|
|
@ -72,6 +72,8 @@ async def index_graph_edges(batch_size: int = 1024):
|
|||
for index_name, indexable_points in index_points.items():
|
||||
index_name, field_name = index_name.split(".")
|
||||
|
||||
# Get maximum batch size for embedding model
|
||||
batch_size = vector_engine.embedding_engine.get_batch_size()
|
||||
# We save the data in batches of {batch_size} to not put a lot of pressure on the database
|
||||
for start in range(0, len(indexable_points), batch_size):
|
||||
batch = indexable_points[start : start + batch_size]
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
import pytest
|
||||
from unittest.mock import AsyncMock, patch
|
||||
from unittest.mock import AsyncMock, patch, MagicMock
|
||||
from cognee.tasks.storage.index_graph_edges import index_graph_edges
|
||||
|
||||
|
||||
|
|
@ -16,6 +16,7 @@ async def test_index_graph_edges_success():
|
|||
],
|
||||
)
|
||||
mock_vector_engine = AsyncMock()
|
||||
mock_vector_engine.embedding_engine.get_batch_size = MagicMock(return_value=100)
|
||||
|
||||
# Patch the globals of the function so that when it does:
|
||||
# vector_engine = get_vector_engine()
|
||||
|
|
|
|||
149
poetry.lock
generated
149
poetry.lock
generated
|
|
@ -1,4 +1,4 @@
|
|||
# This file is automatically @generated by Poetry 2.1.4 and should not be changed by hand.
|
||||
# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand.
|
||||
|
||||
[[package]]
|
||||
name = "accelerate"
|
||||
|
|
@ -878,7 +878,7 @@ description = "Extensible memoizing collections and decorators"
|
|||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
|
||||
markers = "extra == \"deepeval\" or extra == \"chromadb\" or extra == \"docs\""
|
||||
files = [
|
||||
{file = "cachetools-6.2.0-py3-none-any.whl", hash = "sha256:1c76a8960c0041fcc21097e357f882197c79da0dbff766e7317890a65d7d8ba6"},
|
||||
{file = "cachetools-6.2.0.tar.gz", hash = "sha256:38b328c0889450f05f5e120f56ab68c8abaf424e1275522b138ffc93253f7e32"},
|
||||
|
|
@ -2749,28 +2749,6 @@ files = [
|
|||
{file = "giturlparse-0.12.0.tar.gz", hash = "sha256:c0fff7c21acc435491b1779566e038757a205c1ffdcb47e4f81ea52ad8c3859a"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "google-ai-generativelanguage"
|
||||
version = "0.6.15"
|
||||
description = "Google Ai Generativelanguage API client library"
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\""
|
||||
files = [
|
||||
{file = "google_ai_generativelanguage-0.6.15-py3-none-any.whl", hash = "sha256:5a03ef86377aa184ffef3662ca28f19eeee158733e45d7947982eb953c6ebb6c"},
|
||||
{file = "google_ai_generativelanguage-0.6.15.tar.gz", hash = "sha256:8f6d9dc4c12b065fe2d0289026171acea5183ebf2d0b11cefe12f3821e159ec3"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
google-api-core = {version = ">=1.34.1,<2.0.dev0 || >=2.11.dev0,<3.0.0dev", extras = ["grpc"]}
|
||||
google-auth = ">=2.14.1,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0dev"
|
||||
proto-plus = [
|
||||
{version = ">=1.22.3,<2.0.0dev"},
|
||||
{version = ">=1.25.0,<2.0.0dev", markers = "python_version >= \"3.13\""},
|
||||
]
|
||||
protobuf = ">=3.20.2,<4.21.0 || >4.21.0,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4.21.4 || >4.21.4,<4.21.5 || >4.21.5,<6.0.0dev"
|
||||
|
||||
[[package]]
|
||||
name = "google-api-core"
|
||||
version = "2.25.1"
|
||||
|
|
@ -2778,7 +2756,7 @@ description = "Google API client core library"
|
|||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\" or extra == \"docs\""
|
||||
markers = "extra == \"docs\""
|
||||
files = [
|
||||
{file = "google_api_core-2.25.1-py3-none-any.whl", hash = "sha256:8a2a56c1fef82987a524371f99f3bd0143702fecc670c72e600c1cda6bf8dbb7"},
|
||||
{file = "google_api_core-2.25.1.tar.gz", hash = "sha256:d2aaa0b13c78c61cb3f4282c464c046e45fbd75755683c9c525e6e8f7ed0a5e8"},
|
||||
|
|
@ -2808,26 +2786,6 @@ grpc = ["grpcio (>=1.33.2,<2.0.0)", "grpcio (>=1.49.1,<2.0.0) ; python_version >
|
|||
grpcgcp = ["grpcio-gcp (>=0.2.2,<1.0.0)"]
|
||||
grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "google-api-python-client"
|
||||
version = "2.183.0"
|
||||
description = "Google API Client Library for Python"
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\""
|
||||
files = [
|
||||
{file = "google_api_python_client-2.183.0-py3-none-any.whl", hash = "sha256:2005b6e86c27be1db1a43f43e047a0f8e004159f3cceddecb08cf1624bddba31"},
|
||||
{file = "google_api_python_client-2.183.0.tar.gz", hash = "sha256:abae37e04fecf719388e5c02f707ed9cdf952f10b217c79a3e76c636762e3ea9"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
google-api-core = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0"
|
||||
google-auth = ">=1.32.0,<2.24.0 || >2.24.0,<2.25.0 || >2.25.0,<3.0.0"
|
||||
google-auth-httplib2 = ">=0.2.0,<1.0.0"
|
||||
httplib2 = ">=0.19.0,<1.0.0"
|
||||
uritemplate = ">=3.0.1,<5"
|
||||
|
||||
[[package]]
|
||||
name = "google-auth"
|
||||
version = "2.41.0"
|
||||
|
|
@ -2835,7 +2793,7 @@ description = "Google Authentication Library"
|
|||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
|
||||
markers = "extra == \"deepeval\" or extra == \"chromadb\" or extra == \"docs\""
|
||||
files = [
|
||||
{file = "google_auth-2.41.0-py2.py3-none-any.whl", hash = "sha256:d8bed9b53ab63b7b0374656b8e1bef051f95bb14ecc0cf21ba49de7911d62e09"},
|
||||
{file = "google_auth-2.41.0.tar.gz", hash = "sha256:c9d7b534ea4a5d9813c552846797fafb080312263cd4994d6622dd50992ae101"},
|
||||
|
|
@ -2856,23 +2814,6 @@ requests = ["requests (>=2.20.0,<3.0.0)"]
|
|||
testing = ["aiohttp (<3.10.0)", "aiohttp (>=3.6.2,<4.0.0)", "aioresponses", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (<39.0.0) ; python_version < \"3.8\"", "cryptography (>=38.0.3)", "cryptography (>=38.0.3)", "flask", "freezegun", "grpcio", "mock", "oauth2client", "packaging", "pyjwt (>=2.0)", "pyopenssl (<24.3.0)", "pyopenssl (>=20.0.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-localserver", "pyu2f (>=0.1.5)", "requests (>=2.20.0,<3.0.0)", "responses", "urllib3"]
|
||||
urllib3 = ["packaging", "urllib3"]
|
||||
|
||||
[[package]]
|
||||
name = "google-auth-httplib2"
|
||||
version = "0.2.0"
|
||||
description = "Google Authentication Library: httplib2 transport"
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\""
|
||||
files = [
|
||||
{file = "google-auth-httplib2-0.2.0.tar.gz", hash = "sha256:38aa7badf48f974f1eb9861794e9c0cb2a0511a4ec0679b1f886d108f5640e05"},
|
||||
{file = "google_auth_httplib2-0.2.0-py2.py3-none-any.whl", hash = "sha256:b65a0a2123300dd71281a7bf6e64d65a0759287df52729bdd1ae2e47dc311a3d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
google-auth = "*"
|
||||
httplib2 = ">=0.19.0"
|
||||
|
||||
[[package]]
|
||||
name = "google-cloud-vision"
|
||||
version = "3.10.2"
|
||||
|
|
@ -2922,31 +2863,6 @@ websockets = ">=13.0.0,<15.1.0"
|
|||
aiohttp = ["aiohttp (<4.0.0)"]
|
||||
local-tokenizer = ["protobuf", "sentencepiece (>=0.2.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "google-generativeai"
|
||||
version = "0.8.5"
|
||||
description = "Google Generative AI High level API client library and tools."
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\""
|
||||
files = [
|
||||
{file = "google_generativeai-0.8.5-py3-none-any.whl", hash = "sha256:22b420817fb263f8ed520b33285f45976d5b21e904da32b80d4fd20c055123a2"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
google-ai-generativelanguage = "0.6.15"
|
||||
google-api-core = "*"
|
||||
google-api-python-client = "*"
|
||||
google-auth = ">=2.15.0"
|
||||
protobuf = "*"
|
||||
pydantic = "*"
|
||||
tqdm = "*"
|
||||
typing-extensions = "*"
|
||||
|
||||
[package.extras]
|
||||
dev = ["Pillow", "absl-py", "black", "ipython", "nose2", "pandas", "pytype", "pyyaml"]
|
||||
|
||||
[[package]]
|
||||
name = "googleapis-common-protos"
|
||||
version = "1.70.0"
|
||||
|
|
@ -2954,7 +2870,7 @@ description = "Common protobufs used in Google APIs"
|
|||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
|
||||
markers = "extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
|
||||
files = [
|
||||
{file = "googleapis_common_protos-1.70.0-py3-none-any.whl", hash = "sha256:b8bfcca8c25a2bb253e0e0b0adaf8c00773e5e6af6fd92397576680b807e0fd8"},
|
||||
{file = "googleapis_common_protos-1.70.0.tar.gz", hash = "sha256:0e1b44e0ea153e6594f9f394fef15193a68aaaea2d843f83e2742717ca753257"},
|
||||
|
|
@ -3104,7 +3020,7 @@ description = "HTTP/2-based RPC framework"
|
|||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
|
||||
markers = "extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
|
||||
files = [
|
||||
{file = "grpcio-1.75.1-cp310-cp310-linux_armv7l.whl", hash = "sha256:1712b5890b22547dd29f3215c5788d8fc759ce6dd0b85a6ba6e2731f2d04c088"},
|
||||
{file = "grpcio-1.75.1-cp310-cp310-macosx_11_0_universal2.whl", hash = "sha256:8d04e101bba4b55cea9954e4aa71c24153ba6182481b487ff376da28d4ba46cf"},
|
||||
|
|
@ -3182,7 +3098,7 @@ description = "Status proto mapping for gRPC"
|
|||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\" or extra == \"docs\""
|
||||
markers = "extra == \"docs\""
|
||||
files = [
|
||||
{file = "grpcio_status-1.71.2-py3-none-any.whl", hash = "sha256:803c98cb6a8b7dc6dbb785b1111aed739f241ab5e9da0bba96888aa74704cfd3"},
|
||||
{file = "grpcio_status-1.71.2.tar.gz", hash = "sha256:c7a97e176df71cdc2c179cd1847d7fc86cca5832ad12e9798d7fed6b7a1aab50"},
|
||||
|
|
@ -3374,22 +3290,6 @@ http2 = ["h2 (>=3,<5)"]
|
|||
socks = ["socksio (==1.*)"]
|
||||
trio = ["trio (>=0.22.0,<1.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "httplib2"
|
||||
version = "0.31.0"
|
||||
description = "A comprehensive HTTP client library."
|
||||
optional = true
|
||||
python-versions = ">=3.6"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\""
|
||||
files = [
|
||||
{file = "httplib2-0.31.0-py3-none-any.whl", hash = "sha256:b9cd78abea9b4e43a7714c6e0f8b6b8561a6fc1e95d5dbd367f5bf0ef35f5d24"},
|
||||
{file = "httplib2-0.31.0.tar.gz", hash = "sha256:ac7ab497c50975147d4f7b1ade44becc7df2f8954d42b38b3d69c515f531135c"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
pyparsing = ">=3.0.4,<4"
|
||||
|
||||
[[package]]
|
||||
name = "httptools"
|
||||
version = "0.6.4"
|
||||
|
|
@ -4071,8 +3971,6 @@ groups = ["main"]
|
|||
markers = "extra == \"dlt\""
|
||||
files = [
|
||||
{file = "jsonpath-ng-1.7.0.tar.gz", hash = "sha256:f6f5f7fd4e5ff79c785f1573b394043b39849fb2bb47bcead935d12b00beab3c"},
|
||||
{file = "jsonpath_ng-1.7.0-py2-none-any.whl", hash = "sha256:898c93fc173f0c336784a3fa63d7434297544b7198124a68f9a3ef9597b0ae6e"},
|
||||
{file = "jsonpath_ng-1.7.0-py3-none-any.whl", hash = "sha256:f3d7f9e848cba1b6da28c55b1c26ff915dc9e0b1ba7e752a53d6da8d5cbd00b6"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8184,7 +8082,7 @@ description = "Beautiful, Pythonic protocol buffers"
|
|||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\" or extra == \"docs\""
|
||||
markers = "extra == \"docs\""
|
||||
files = [
|
||||
{file = "proto_plus-1.26.1-py3-none-any.whl", hash = "sha256:13285478c2dcf2abb829db158e1047e2f1e8d63a077d94263c2b88b043c75a66"},
|
||||
{file = "proto_plus-1.26.1.tar.gz", hash = "sha256:21a515a4c4c0088a773899e23c7bbade3d18f9c66c73edd4c7ee3816bc96a012"},
|
||||
|
|
@ -8256,7 +8154,6 @@ files = [
|
|||
{file = "psycopg2-2.9.10-cp311-cp311-win_amd64.whl", hash = "sha256:0435034157049f6846e95103bd8f5a668788dd913a7c30162ca9503fdf542cb4"},
|
||||
{file = "psycopg2-2.9.10-cp312-cp312-win32.whl", hash = "sha256:65a63d7ab0e067e2cdb3cf266de39663203d38d6a8ed97f5ca0cb315c73fe067"},
|
||||
{file = "psycopg2-2.9.10-cp312-cp312-win_amd64.whl", hash = "sha256:4a579d6243da40a7b3182e0430493dbd55950c493d8c68f4eec0b302f6bbf20e"},
|
||||
{file = "psycopg2-2.9.10-cp313-cp313-win_amd64.whl", hash = "sha256:91fd603a2155da8d0cfcdbf8ab24a2d54bca72795b90d2a3ed2b6da8d979dee2"},
|
||||
{file = "psycopg2-2.9.10-cp39-cp39-win32.whl", hash = "sha256:9d5b3b94b79a844a986d029eee38998232451119ad653aea42bb9220a8c5066b"},
|
||||
{file = "psycopg2-2.9.10-cp39-cp39-win_amd64.whl", hash = "sha256:88138c8dedcbfa96408023ea2b0c369eda40fe5d75002c0964c78f46f11fa442"},
|
||||
{file = "psycopg2-2.9.10.tar.gz", hash = "sha256:12ec0b40b0273f95296233e8750441339298e6a572f7039da5b260e3c8b60e11"},
|
||||
|
|
@ -8318,7 +8215,6 @@ files = [
|
|||
{file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:bb89f0a835bcfc1d42ccd5f41f04870c1b936d8507c6df12b7737febc40f0909"},
|
||||
{file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:f0c2d907a1e102526dd2986df638343388b94c33860ff3bbe1384130828714b1"},
|
||||
{file = "psycopg2_binary-2.9.10-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:f8157bed2f51db683f31306aa497311b560f2265998122abe1dce6428bd86567"},
|
||||
{file = "psycopg2_binary-2.9.10-cp313-cp313-win_amd64.whl", hash = "sha256:27422aa5f11fbcd9b18da48373eb67081243662f9b46e6fd07c3eb46e4535142"},
|
||||
{file = "psycopg2_binary-2.9.10-cp38-cp38-macosx_12_0_x86_64.whl", hash = "sha256:eb09aa7f9cecb45027683bb55aebaaf45a0df8bf6de68801a6afdc7947bb09d4"},
|
||||
{file = "psycopg2_binary-2.9.10-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b73d6d7f0ccdad7bc43e6d34273f70d587ef62f824d7261c4ae9b8b1b6af90e8"},
|
||||
{file = "psycopg2_binary-2.9.10-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ce5ab4bf46a211a8e924d307c1b1fcda82368586a19d0a24f8ae166f5c784864"},
|
||||
|
|
@ -8528,7 +8424,7 @@ description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs
|
|||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
|
||||
markers = "extra == \"deepeval\" or extra == \"chromadb\" or extra == \"docs\""
|
||||
files = [
|
||||
{file = "pyasn1-0.6.1-py3-none-any.whl", hash = "sha256:0d632f46f2ba09143da3a8afe9e33fb6f92fa2320ab7e886e2d0f7672af84629"},
|
||||
{file = "pyasn1-0.6.1.tar.gz", hash = "sha256:6f580d2bdd84365380830acf45550f2511469f673cb4a5ae3857a3170128b034"},
|
||||
|
|
@ -8541,7 +8437,7 @@ description = "A collection of ASN.1-based protocols modules"
|
|||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
|
||||
markers = "extra == \"deepeval\" or extra == \"chromadb\" or extra == \"docs\""
|
||||
files = [
|
||||
{file = "pyasn1_modules-0.4.2-py3-none-any.whl", hash = "sha256:29253a9207ce32b64c3ac6600edc75368f98473906e8fd1043bd6b5b1de2c14a"},
|
||||
{file = "pyasn1_modules-0.4.2.tar.gz", hash = "sha256:677091de870a80aae844b1ca6134f54652fa2c8c5a52aa396440ac3106e941e6"},
|
||||
|
|
@ -9430,13 +9326,6 @@ optional = false
|
|||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
files = [
|
||||
{file = "PyYAML-6.0.3-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:c2514fceb77bc5e7a2f7adfaa1feb2fb311607c9cb518dbc378688ec73d8292f"},
|
||||
{file = "PyYAML-6.0.3-cp38-cp38-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:9c57bb8c96f6d1808c030b1687b9b5fb476abaa47f0db9c0101f5e9f394e97f4"},
|
||||
{file = "PyYAML-6.0.3-cp38-cp38-manylinux2014_s390x.manylinux_2_17_s390x.manylinux_2_28_s390x.whl", hash = "sha256:efd7b85f94a6f21e4932043973a7ba2613b059c4a000551892ac9f1d11f5baf3"},
|
||||
{file = "PyYAML-6.0.3-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:22ba7cfcad58ef3ecddc7ed1db3409af68d023b7f940da23c6c2a1890976eda6"},
|
||||
{file = "PyYAML-6.0.3-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:6344df0d5755a2c9a276d4473ae6b90647e216ab4757f8426893b5dd2ac3f369"},
|
||||
{file = "PyYAML-6.0.3-cp38-cp38-win32.whl", hash = "sha256:3ff07ec89bae51176c0549bc4c63aa6202991da2d9a6129d7aef7f1407d3f295"},
|
||||
{file = "PyYAML-6.0.3-cp38-cp38-win_amd64.whl", hash = "sha256:5cf4e27da7e3fbed4d6c3d8e797387aaad68102272f8f9752883bc32d61cb87b"},
|
||||
{file = "pyyaml-6.0.3-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:214ed4befebe12df36bcc8bc2b64b396ca31be9304b8f59e25c11cf94a4c033b"},
|
||||
{file = "pyyaml-6.0.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:02ea2dfa234451bbb8772601d7b8e426c2bfa197136796224e50e35a78777956"},
|
||||
{file = "pyyaml-6.0.3-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:b30236e45cf30d2b8e7b3e85881719e98507abed1011bf463a8fa23e9c3e98a8"},
|
||||
|
|
@ -10249,7 +10138,7 @@ description = "Pure-Python RSA implementation"
|
|||
optional = true
|
||||
python-versions = "<4,>=3.6"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\" or extra == \"docs\" or extra == \"deepeval\" or extra == \"chromadb\""
|
||||
markers = "extra == \"deepeval\" or extra == \"chromadb\" or extra == \"docs\""
|
||||
files = [
|
||||
{file = "rsa-4.9.1-py3-none-any.whl", hash = "sha256:68635866661c6836b8d39430f97a996acbd61bfa49406748ea243539fe239762"},
|
||||
{file = "rsa-4.9.1.tar.gz", hash = "sha256:e7bdbfdb5497da4c07dfd35530e1a902659db6ff241e39d9953cad06ebd0ae75"},
|
||||
|
|
@ -12003,19 +11892,6 @@ files = [
|
|||
[package.extras]
|
||||
dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake8-commas", "flake8-comprehensions", "flake8-continuation", "flake8-datetimez", "flake8-docstrings", "flake8-import-order", "flake8-literal", "flake8-modern-annotations", "flake8-noqa", "flake8-pyproject", "flake8-requirements", "flake8-typechecking-import", "flake8-use-fstring", "mypy", "pep8-naming", "types-PyYAML"]
|
||||
|
||||
[[package]]
|
||||
name = "uritemplate"
|
||||
version = "4.2.0"
|
||||
description = "Implementation of RFC 6570 URI Templates"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"gemini\""
|
||||
files = [
|
||||
{file = "uritemplate-4.2.0-py3-none-any.whl", hash = "sha256:962201ba1c4edcab02e60f9a0d3821e82dfc5d2d6662a21abd533879bdb8a686"},
|
||||
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||||
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|
||||
|
||||
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|
||||
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|
||||
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|
||||
|
|
@ -12882,7 +12758,6 @@ dlt = ["dlt"]
|
|||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
groq = ["groq"]
|
||||
huggingface = ["transformers"]
|
||||
|
|
@ -12901,4 +12776,4 @@ posthog = ["posthog"]
|
|||
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|
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||||
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|
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|
|
|||
|
|
@ -82,7 +82,6 @@ langchain = [
|
|||
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|
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|
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|
|
|
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103
uv.lock
generated
103
uv.lock
generated
|
|
@ -962,9 +962,6 @@ evals = [
|
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|
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|
||||
version = "2.3.0"
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue