fix: Resolve issue with Gemini adapter (#1494)
<!-- .github/pull_request_template.md --> ## Description Resolve Gemini Adapter issues: 1. resolve embedding batch issue, 2. Resolve slowness because gemini tokenizer was sending word per word to Googles API to count tokens (using OpenAI's local tokenizer to count tokens for Gemini now) 3. Update deprecated library and move to instructor ## Type of Change <!-- Please check the relevant option --> - [x] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Pre-submission Checklist <!-- Please check all boxes that apply before submitting your PR --> - [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## 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.
This commit is contained in:
Igor Ilic
GitHub
parent
ee45afed42
commit
38cdacbcb6
22 changed files with 222 additions and 330 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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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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@ -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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Returns:
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"""
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raise NotImplementedError()
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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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@ -101,6 +103,15 @@ class FastembedEmbeddingEngine(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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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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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,
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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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@ -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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@ -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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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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@ -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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@ -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",
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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.endpoint = endpoint
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self.huggingface_tokenizer_name = huggingface_tokenizer
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self.batch_size = batch_size
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self.tokenizer = self.get_tokenizer()
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enable_mocking = os.getenv("MOCK_EMBEDDING", "false")
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@ -122,6 +124,15 @@ class OllamaEmbeddingEngine(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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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 a HuggingFace tokenizer for the embedding engine.
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@ -19,9 +19,17 @@ class EmbeddingConfig(BaseSettings):
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embedding_api_key: Optional[str] = None
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embedding_api_version: Optional[str] = None
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embedding_max_completion_tokens: Optional[int] = 8191
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embedding_batch_size: Optional[int] = None
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huggingface_tokenizer: Optional[str] = None
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model_config = SettingsConfigDict(env_file=".env", extra="allow")
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def model_post_init(self, __context) -> None:
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# If embedding batch size is not defined use 2048 as default for OpenAI and 100 for all other embedding models
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if not self.embedding_batch_size and self.embedding_provider.lower() == "openai":
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self.embedding_batch_size = 2048
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elif not self.embedding_batch_size:
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self.embedding_batch_size = 100
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def to_dict(self) -> dict:
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"""
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Serialize all embedding configuration settings to a dictionary.
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@ -31,6 +31,7 @@ def get_embedding_engine() -> EmbeddingEngine:
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config.embedding_endpoint,
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config.embedding_api_key,
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config.embedding_api_version,
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config.embedding_batch_size,
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config.huggingface_tokenizer,
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llm_config.llm_api_key,
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llm_config.llm_provider,
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@ -46,6 +47,7 @@ def create_embedding_engine(
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embedding_endpoint,
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embedding_api_key,
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embedding_api_version,
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embedding_batch_size,
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huggingface_tokenizer,
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llm_api_key,
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llm_provider,
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@ -84,6 +86,7 @@ def create_embedding_engine(
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model=embedding_model,
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dimensions=embedding_dimensions,
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max_completion_tokens=embedding_max_completion_tokens,
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batch_size=embedding_batch_size,
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)
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if embedding_provider == "ollama":
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@ -95,6 +98,7 @@ def create_embedding_engine(
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max_completion_tokens=embedding_max_completion_tokens,
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endpoint=embedding_endpoint,
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huggingface_tokenizer=huggingface_tokenizer,
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batch_size=embedding_batch_size,
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)
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from .LiteLLMEmbeddingEngine import LiteLLMEmbeddingEngine
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@ -108,4 +112,5 @@ def create_embedding_engine(
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model=embedding_model,
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dimensions=embedding_dimensions,
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max_completion_tokens=embedding_max_completion_tokens,
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batch_size=embedding_batch_size,
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)
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@ -1,115 +1,155 @@
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import litellm
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from pydantic import BaseModel
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from typing import Type, Optional
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from litellm import acompletion, JSONSchemaValidationError
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"""Adapter for Generic API LLM provider API"""
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from cognee.shared.logging_utils import get_logger
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from cognee.modules.observability.get_observe import get_observe
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from cognee.infrastructure.llm.exceptions import MissingSystemPromptPathError
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import litellm
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import instructor
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from typing import Type
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from pydantic import BaseModel
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from openai import ContentFilterFinishReasonError
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from litellm.exceptions import ContentPolicyViolationError
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from instructor.core import InstructorRetryException
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from cognee.infrastructure.llm.exceptions import ContentPolicyFilterError
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from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.llm_interface import (
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LLMInterface,
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)
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from cognee.infrastructure.llm.LLMGateway import LLMGateway
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from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.llm.rate_limiter import (
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rate_limit_async,
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sleep_and_retry_async,
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)
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logger = get_logger()
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observe = get_observe()
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class GeminiAdapter(LLMInterface):
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"""
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Handles interactions with a language model API.
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Adapter for Gemini API LLM provider.
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Public methods include:
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- acreate_structured_output
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- show_prompt
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This class initializes the API adapter with necessary credentials and configurations for
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interacting with the gemini LLM models. It provides methods for creating structured outputs
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based on user input and system prompts.
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Public methods:
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- acreate_structured_output(text_input: str, system_prompt: str, response_model:
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Type[BaseModel]) -> BaseModel
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"""
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MAX_RETRIES = 5
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name: str
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model: str
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api_key: str
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def __init__(
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self,
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endpoint,
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api_key: str,
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model: str,
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api_version: str,
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max_completion_tokens: int,
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endpoint: Optional[str] = None,
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api_version: Optional[str] = None,
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streaming: bool = False,
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) -> None:
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self.api_key = api_key
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fallback_model: str = None,
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fallback_api_key: str = None,
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fallback_endpoint: str = None,
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):
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self.model = model
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self.api_key = api_key
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self.endpoint = endpoint
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self.api_version = api_version
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self.streaming = streaming
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self.max_completion_tokens = max_completion_tokens
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@observe(as_type="generation")
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self.fallback_model = fallback_model
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self.fallback_api_key = fallback_api_key
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self.fallback_endpoint = fallback_endpoint
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self.aclient = instructor.from_litellm(litellm.acompletion, mode=instructor.Mode.JSON)
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@sleep_and_retry_async()
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@rate_limit_async
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async def acreate_structured_output(
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self, text_input: str, system_prompt: str, response_model: Type[BaseModel]
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) -> BaseModel:
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"""
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Generate structured output from the language model based on the provided input and
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system prompt.
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Generate a response from a user query.
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This method handles retries and raises a ValueError if the request fails or the response
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does not conform to the expected schema, logging errors accordingly.
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This asynchronous method sends a user query and a system prompt to a language model and
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retrieves the generated response. It handles API communication and retries up to a
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specified limit in case of request failures.
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Parameters:
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-----------
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- text_input (str): The user input text to generate a response for.
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- system_prompt (str): The system's prompt or context to influence the language
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model's generation.
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- response_model (Type[BaseModel]): A model type indicating the expected format of
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the response.
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- text_input (str): The input text from the user to generate a response for.
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- system_prompt (str): A prompt that provides context or instructions for the
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response generation.
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- response_model (Type[BaseModel]): A Pydantic model that defines the structure of
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the expected response.
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Returns:
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--------
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- BaseModel: Returns the generated response as an instance of the specified response
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model.
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- BaseModel: An instance of the specified response model containing the structured
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output from the language model.
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"""
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try:
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if response_model is str:
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response_schema = {"type": "string"}
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else:
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response_schema = response_model
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return await self.aclient.chat.completions.create(
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model=self.model,
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messages=[
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{
|
||||
"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
|
||||
|
|
|
|||
|
|
@ -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"},
|
||||
{file = "uritemplate-4.2.0.tar.gz", hash = "sha256:480c2ed180878955863323eea31b0ede668795de182617fef9c6ca09e6ec9d0e"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "urllib3"
|
||||
version = "2.3.0"
|
||||
|
|
@ -12882,7 +12758,6 @@ dlt = ["dlt"]
|
|||
docs = ["unstructured"]
|
||||
evals = ["gdown", "matplotlib", "pandas", "plotly", "scikit-learn"]
|
||||
falkordb = ["falkordb"]
|
||||
gemini = ["google-generativeai"]
|
||||
graphiti = ["graphiti-core"]
|
||||
groq = ["groq"]
|
||||
huggingface = ["transformers"]
|
||||
|
|
@ -12901,4 +12776,4 @@ posthog = ["posthog"]
|
|||
[metadata]
|
||||
lock-version = "2.1"
|
||||
python-versions = ">=3.10,<=3.13"
|
||||
content-hash = "8c5ebda99705d8bcaf9d14e244a556d2627106d58d0fdce25318b4c1a647197a"
|
||||
content-hash = "c76267fe685339b5b5665342c81850a3e891cadaf760178bf3b04058f35b1014"
|
||||
|
|
|
|||
|
|
@ -82,7 +82,6 @@ langchain = [
|
|||
"langchain_text_splitters>=0.3.2,<1.0.0",
|
||||
]
|
||||
llama-index = ["llama-index-core>=0.12.11,<0.13"]
|
||||
gemini = ["google-generativeai>=0.8.4,<0.9"]
|
||||
huggingface = ["transformers>=4.46.3,<5"]
|
||||
ollama = ["transformers>=4.46.3,<5"]
|
||||
mistral = ["mistral-common>=1.5.2,<2"]
|
||||
|
|
|
|||
103
uv.lock
generated
103
uv.lock
generated
|
|
@ -962,9 +962,6 @@ evals = [
|
|||
falkordb = [
|
||||
{ name = "falkordb" },
|
||||
]
|
||||
gemini = [
|
||||
{ name = "google-generativeai" },
|
||||
]
|
||||
graphiti = [
|
||||
{ name = "graphiti-core" },
|
||||
]
|
||||
|
|
@ -1038,7 +1035,6 @@ requires-dist = [
|
|||
{ name = "filetype", specifier = ">=1.2.0,<2.0.0" },
|
||||
{ name = "gdown", marker = "extra == 'evals'", specifier = ">=5.2.0,<6" },
|
||||
{ name = "gitpython", marker = "extra == 'dev'", specifier = ">=3.1.43,<4" },
|
||||
{ name = "google-generativeai", marker = "extra == 'gemini'", specifier = ">=0.8.4,<0.9" },
|
||||
{ name = "graphiti-core", marker = "extra == 'graphiti'", specifier = ">=0.7.0,<0.8" },
|
||||
{ name = "groq", marker = "extra == 'groq'", specifier = ">=0.8.0,<1.0.0" },
|
||||
{ name = "gunicorn", specifier = ">=20.1.0,<24" },
|
||||
|
|
@ -1108,7 +1104,7 @@ requires-dist = [
|
|||
{ name = "uvicorn", specifier = ">=0.34.0,<1.0.0" },
|
||||
{ name = "websockets", specifier = ">=15.0.1,<16.0.0" },
|
||||
]
|
||||
provides-extras = ["api", "distributed", "neo4j", "neptune", "postgres", "postgres-binary", "notebook", "langchain", "llama-index", "gemini", "huggingface", "ollama", "mistral", "anthropic", "deepeval", "posthog", "falkordb", "groq", "chromadb", "docs", "codegraph", "evals", "graphiti", "aws", "dlt", "baml", "dev", "debug", "monitoring"]
|
||||
provides-extras = ["api", "distributed", "neo4j", "neptune", "postgres", "postgres-binary", "notebook", "langchain", "llama-index", "huggingface", "ollama", "mistral", "anthropic", "deepeval", "posthog", "falkordb", "groq", "chromadb", "docs", "codegraph", "evals", "graphiti", "aws", "dlt", "baml", "dev", "debug", "monitoring"]
|
||||
|
||||
[[package]]
|
||||
name = "colorama"
|
||||
|
|
@ -2176,21 +2172,6 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/dd/94/c6ff3388b8e3225a014e55aed957188639aa0966443e0408d38f0c9614a7/giturlparse-0.12.0-py2.py3-none-any.whl", hash = "sha256:412b74f2855f1da2fefa89fd8dde62df48476077a72fc19b62039554d27360eb", size = 15752, upload-time = "2023-09-24T07:22:35.465Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "google-ai-generativelanguage"
|
||||
version = "0.6.15"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "google-api-core", extra = ["grpc"] },
|
||||
{ name = "google-auth" },
|
||||
{ name = "proto-plus" },
|
||||
{ name = "protobuf" },
|
||||
]
|
||||
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|
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|
||||
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|
||||
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|
||||
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|
|
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|
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|
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|
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|
|
|||
Loading…
Add table
Reference in a new issue