122 lines
2.9 KiB
Markdown
122 lines
2.9 KiB
Markdown
# cognee API Reference
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## Overview
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The Cognee API has:
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1. Python library configuration entry points
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2. FastAPI server
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## Python Library
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# Module: cognee.config
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This module provides functionalities to configure various aspects of the system's operation in the cognee library.
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It interfaces with a set of Pydantic settings singleton classes to manage the system's configuration.
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## Overview
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The config class in this module offers a series of static methods to configure the system's directories, various machine learning models, and other parameters.
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## Methods
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### system_root_directory(system_root_directory: str)
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Sets the root directory of the system where essential system files and operations are managed. Parameters:
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system_root_directory (str): The path to set as the system's root directory.
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Example:
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```python
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cognee.config.system_root_directory('/path/to/system/root')
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```
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### data_root_directory(data_root_directory: str)
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Sets the directory for storing data used and generated by the system.
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Parameters:
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data_root_directory (str): The path to set as the data root directory.
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Example:
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```python
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import cognee
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cognee.config.data_root_directory('/path/to/data/root')
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```
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### set_classification_model(classification_model: object)
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Assigns a machine learning model for classification tasks within the system.
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Parameters:
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classification_model (object): The Pydantic model to use for classification.
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Check cognee.shared.data_models for existing models.
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Example:
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```python
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import cognee
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cognee.config.set_classification_model(model)
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```
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set_summarization_model(summarization_model: object)
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Sets the Pydantic model to be used for summarization tasks.
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Parameters:
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summarization_model (object): The model to use for summarization.
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Check cognee.shared.data_models for existing models.
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Example:
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```python
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import cognee
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cognee.config.set_summarization_model(my_summarization_model)
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```
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### set_llm_model(llm_model: object)
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Determines the model to handle LLMs. Parameters:
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llm_model (object): The model to use for LLMs.
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Example:
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```python
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import cognee
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cognee.config.set_llm_model("openai")
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```
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### set_graph_engine(graph_engine: object)
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Sets the engine to manage graph processing tasks.
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Parameters:
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graph_engine (object): The engine for graph tasks.
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Example:
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```python
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from cognee.shared.data_models import GraphDBType
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cognee.config.set_graph_engine(GraphDBType.NEO4J)
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```
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## API
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For each API endpoint, provide the following details:
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### Endpoint 1: Root
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- URL: /add
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- Method: POST
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- Auth Required: No
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- Description: Root endpoint that returns a welcome message.
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#### Response
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```json
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{
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"message": "Hello, World, I am alive!"
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}
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```
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### Endpoint 1: Health Check
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- URL: /health
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- Method: GET
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- Auth Required: No
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- Description: Health check endpoint that returns the server status.
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#### Response
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```json
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{
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"status": "OK"
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}
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```
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More endpoints are available in the FastAPI server. Documentation is in progress
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