Demo for relational db with cognee #620
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WalkthroughThe project now includes a new Python script and a new Jupyter notebook that implement a complete Pokémon data processing pipeline. Both files define data point classes for Pokémon and abilities, and introduce functions to fetch data via HTTP requests, load JSONL files, and process the data asynchronously using the DLT and Cognee libraries. Error handling and environment configuration are integrated to ensure robust data collection and processing. Changes
Sequence Diagram(s)sequenceDiagram
participant User as User
participant Main as Main/Notebook Entry
participant Setup as Data Setup (async)
participant Fetch as Data Fetch (pokemon_list/pokemon_details)
participant Loader as Data Loader (load_abilities_data/load_pokemon_data)
participant Cognee as Cognee Engine
User->>Main: Trigger data process
Main->>Setup: Initialize environment and DLT pipeline
Setup->>Fetch: Retrieve Pokémon list and details from API
Fetch-->>Setup: Return Pokémon data
Setup->>Loader: Load JSONL data and map abilities
Loader-->>Setup: Return structured data
Setup->>Cognee: Process data asynchronously via Cognee
Cognee-->>Setup: Return processed results
Setup-->>Main: Complete data processing
Poem
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Actionable comments posted: 0
🧹 Nitpick comments (13)
notebooks/pokemon_datapoints_notebook.ipynb (7)
105-110: Potential pagination improvement
Implementation is straightforward. However, if you need more than 50 records or want to retrieve them in multiple pages, consider passing anoffsetparameter to handle additional data segments.
134-141: Potential performance bottleneck
pokemon_detailsfetches each Pokémon's details in a loop, leading to serial HTTP requests. For large datasets, consider concurrency or parallel requests to improve performance.
205-221: Consider consolidating JSON line parsing
Bothload_abilities_dataandload_pokemon_dataparse JSONL in a similar pattern. A shared helper function can DRY (Don't Repeat Yourself) this logic, improving maintainability.
245-305: Validate the presence of keys in the JSON
Several keys (_dlt_id,_dlt_parent_id, etc.) are assumed to exist in the JSON. If the data is invalid or missing these keys, a KeyError will occur. Consider adding validation or fallback logic.
361-369: Add exception handling for prune and setup steps
If any of these async steps fail, the system might be left in a partially pruned state. Consider error handling and logging to maintain system integrity.Would you like me to generate a robust error handling snippet for these steps?
401-416: Consider capturing partial failures
When invokingrun_tasks, if certain data points fail, the code currently just prints results. You may want to handle partial failures or generate a report of which items failed to be stored.
474-484: Enhance search to handle edge cases
Currently, you search specifically for"pokemons?". Consider more flexible input or error handling to gracefully handle empty queries or invalid user input.examples/python/pokemon_datapoints_example.py (6)
70-74: Pagination and offset consideration
This function retrieves a subset of Pokémon. If you need complete data or larger sets, consider adding an offset parameter or concurrency to handle multiple pages efficiently.
76-82: Potential performance bottleneck
Fetching each Pokémon in a loop might slow down for large volumes. Explore concurrency or a batch approach for efficiency.
85-99: Validate JSON fields
The code assumesability__nameand_dlt_idexist in every JSON line. Add a validation step or default fallback to avoid unexpected KeyError.
120-155: Enhanced error messaging
This function already raisesFileNotFoundErrorif no JSONL files are found. Consider logging or providing instructions to the user to place data in the expected directory.
156-184: Handle potential errors in pruning
Just as with the notebook, if pruning or system setup fails, it can cause partial states. Adding try-except blocks can help handle these failures gracefully.
185-191: Return or log results
Themainfunction ends after callingpokemon_cognify. Consider returning final results or logs to clarify the outcomes to the user.
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📒 Files selected for processing (2)
examples/python/pokemon_datapoints_example.py(1 hunks)notebooks/pokemon_datapoints_notebook.ipynb(1 hunks)
🔇 Additional comments (1)
examples/python/pokemon_datapoints_example.py (1)
100-118: Check dictionary consistency
Here as well, keys likeid,_dlt_id, and_dlt_parent_idare assumed to exist. If data is incomplete, you may encounter runtime errors. Consider verifying the presence of keys before usage.
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Description
This demo uses pydantic models and dlt to pull data from the Pokémon API and structure it into a relational format. By feeding this structured data into cognee, it makes searching across multiple tables easier and more intuitive, thanks to the relational model.
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