bb679c2dd7
* Update cognify and the networkx client to prepare for running in Neo4j * Fix for openai model * Add the fix to the infra so that the models can be passed to the library. Enable llm_provider to be passed. * Auto graph generation now works with neo4j * Added fixes for both neo4j and networkx * Explicitly name semantic node connections * Added updated docs, readme, chunkers and updates to cognify * Make docs build trigger only when changes on it happen * Update docs, test git actions * Separate cognify logic into tasks * Introduce dspy knowledge graph extraction --------- Co-authored-by: Boris Arzentar <borisarzentar@gmail.com>
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1.1 KiB
Markdown
37 lines
No EOL
1.1 KiB
Markdown
# QUICKSTART
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!!! tip "To understand how cognee works check out the [conceptual overview](conceptual_overview.md)"
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## Setup
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You will need a Weaviate instance and an OpenAI API key to use cognee.
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Weaviate let's you run an instance for 14 days for free. You can sign up at their website: [Weaviate](https://www.semi.technology/products/weaviate.html)
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You can also use Ollama or Anyscale as your LLM provider. For more info on local models check [here](local_models.md)
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```
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import os
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os.environ["WEAVIATE_URL"] = "YOUR_WEAVIATE_URL"
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os.environ["WEAVIATE_API_KEY"] = "YOUR_WEAVIATE_API_KEY"
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os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
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```
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## Run
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```
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import cognee
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text = """Natural language processing (NLP) is an interdisciplinary
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subfield of computer science and information retrieval"""
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cognee.add(text) # Add a new piece of information
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cognee.cognify() # Use LLMs and cognee to create knowledge
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search_results = cognee.search("SIMILARITY", "computer science") # Query cognee for the knowledge
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for result_text in search_results[0]:
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print(result_text)
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``` |