22c0dd5b2d
* Update docs * fix: add cognee colors and logo * fix: add link to community discord --------- Co-authored-by: Boris Arzentar <borisarzentar@gmail.com>
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1.6 KiB
Markdown
57 lines
No EOL
1.6 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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To run cognee, you will need the following:
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1. A running postgres instance
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2. OpenAI API key (Ollama or Anyscale could work as [well](local_models.md))
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Navigate to cognee folder and run
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```
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docker compose up postgres
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```
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Add your LLM API key to the enviroment variables
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```
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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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or
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```
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cognee.config.llm_api_key = "YOUR_OPENAI_API_KEY"
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```
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If you are using Networkx, create an account on Graphistry to vizualize results:
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```
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cognee.config.set_graphistry_username = "YOUR_USERNAME"
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cognee.config.set_graphistry_password = "YOUR_PASSWORD"
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```
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## Run
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cognee is asynchronous by design, meaning that operations like adding information, processing it, and querying it can run concurrently without blocking the execution of other tasks.
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Make sure to await the results of the functions that you call.
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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", {'query': 'Tell me about NLP'}) # 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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```
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In the example above, we add a piece of information to cognee, use LLMs to create a GraphRAG, and then query cognee for the knowledge.
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cognee is composable and you can build your own cognee pipelines using our [templates.](templates.md) |