wehub-resource-sync
105 lines
2.8 KiB
Plaintext
105 lines
2.8 KiB
Plaintext
---
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id: cognee
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title: Cognee
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sidebar_label: Cognee
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---
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## Quick Summary
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Cognee is an open-source framework for anyone to easily implement graph RAG into their LLM application. You can learn more by visiting their [website here.](https://www.cognee.ai/)
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:::info
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With Cognee, you should see an increase in your [`ContextualRelevancyMetric`](/docs/metrics-contextual-relevancy), [`ContextualRecallMetric`](/docs/metrics-contextual-recall), and [`ContextualPrecisionMetric`](/docs/metrics-contextual-precision) scores.
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:::
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Unlike traditional vector databases that relies on simple embedding retrieval and re-rankings to retrieve `retrieval_context`s, Cognee stores and creates a "semantic graph" out of your data, which allows for more accurate retrievals.
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## Setup Cognee
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Simply add your LLM API key to the environment variables:
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```bash
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import os
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os.environ["LLM_API_KEY"] = "YOUR_OPENAI_API_KEY"
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```
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For those on Networkx, you can also create an account on Graphistry to visualize results:
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```python
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import cognee
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cognee.config.set_graphistry_config({
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"username": "YOUR_USERNAME",
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"password": "YOUR_PASSWORD"
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})
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```
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Finally, ingest your data into Cognee and run some retrievals:
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```python
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from cognee.api.v1.search import SearchType
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...
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text = "Cognee is the Graph RAG Framework"
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await cognee.add(text) # add a new piece of information
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await cognee.cognify() # create a semantic graph using cognee
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retrieval_context = await cognee.search(SearchType.INSIGHTS, query_text="What is Cognee?")
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for context in retrieval_context:
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print(context)
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```
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## Evaluating Cognee RAG Pipelines
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Unit testing RAG pipelines powered by Cognee is as simple as defining an `EvaluationDataset` and generating `actual_output`s and `retrieval_context`s at evaluation time. Building upon the previous example, first generate all the necessarily parameters required to test RAG:
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```python main.py
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...
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input = "What is Cognee?"
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retrieval_context = await cognee.search(SearchType.INSIGHTS, query_text="What is Cognee?")
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prompt = """
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Answer the user question based on the supporting context
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User Question:
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{input}
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Supporting Context:
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{retrieval_context}
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"""
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actual_output = generate(prompt) # hypothetical function, replace with your own LLM
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```
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Then, simply run `evaluate()`:
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```python
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from deepeval.metrics import (
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ContextualRecallMetric,
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ContextualPrecisionMetric,
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ContextualRelevancyMetric,
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)
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from deepeval.test_case import LLMTestCase
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from deepeval import evaluate
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...
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test_case = LLMTestCase(
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input=input,
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actual_output=actual_output,
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retrieval_context=retrieval_context,
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expected_output="Cognee is the Graph RAG Framework.",
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)
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evaluate(
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[test_case],
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metrics=[
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ContextualRecallMetric(),
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ContextualPrecisionMetric(),
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ContextualRelevancyMetric(),
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],
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)
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```
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That's it! Do you notice an increase in the contextual metric scores?
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