GraphRAG

BadRequestError in graphrag often arises from incorrect data types or invalid values passed to the OpenAI API, particularly within parameters like `max_tokens`, `previous_response_id`, or content violating audit policies. To fix, carefully inspect the data being sent to the OpenAI API, ensuring all parameters conform to the expected data types and ranges as defined by the OpenAI documentation, and implement robust input validation and sanitization to prevent invalid or inappropriate content.

The "FailedToGenerateValidJsonError" often arises when attempting to serialize Python objects containing unserializable data types (e.g., custom classes, SQLAlchemy models) directly into JSON. To resolve this, ensure all objects being serialized are primitive types (string, integer, boolean, list, dictionary) or provide custom serialization logic (e.g., using a `default` function in `json.dumps` or converting objects to dictionaries manually) to handle non-serializable attributes. Consider using libraries like `marshmallow` for complex object serialization.

FileNotFoundError in graphrag usually means a specified file or database table path is incorrect or the resource doesn't exist at that location. Double-check the file path or table name for typos and ensure the file or table has been created or initialized before being accessed by the code. If using ChromaDB, ensure the collection name is spelled correctly and the database is accessible.

MaxRetryError in graphrag usually indicates that the embedding model is failing to connect to the API endpoint due to network issues, incorrect API keys, or rate limiting. To fix this, verify your internet connection, carefully check that your API keys are correctly set, and implement a retry mechanism with exponential backoff to handle temporary failures and avoid exceeding rate limits; consider reducing the number of concurrent requests.

UnicodeDecodeError arises when Python tries to decode a byte sequence into a string using the wrong encoding. Specify the correct encoding when opening files or decoding byte strings, often 'utf-8' is the universal solution. For example, use `open("file.txt", "r", encoding="utf-8")` or `byte_string.decode("utf-8", errors='ignore')`, where errors='ignore' can help to ignore invalid characters.

The "NotFoundError" in graphrag, especially with Azure OpenAI, usually indicates the specified resource (like a deployment name) doesn't exist or isn't accessible in your Azure OpenAI setup. Verify the deployment name, API key, and endpoint are correctly configured in your code and match the Azure OpenAI resource you created. Ensure your Azure OpenAI resource has the necessary models deployed and your account has the correct permissions.