# WordLift Reader
```bash
pip install llama-index-readers-wordlift
```
The WordLift GraphQL Reader is a connector to fetch and transform data from a WordLift Knowledge Graph using your the WordLift Key. The connector provides a convenient way to load data from WordLift using a GraphQL query and transform it into a list of documents for further processing.
## Usage
To use the WordLift GraphQL Reader, follow the steps below:
1. Set up the necessary configuration options, such as the API endpoint, headers, query, fields, and configuration options (make sure you have with you the [Wordlift Key](https://docs.wordlift.io/pages/key-concepts/#wordlift-key)).
2. Create an instance of the `WordLiftLoader` class, passing in the configuration options.
3. Use the `load_data` method to fetch and transform the data.
4. Process the loaded documents as needed.
Here's an example of how to use the WordLift GraphQL Reader:
```python
import json
from llama_index.core import VectorStoreIndex
from llama_index.core import Document
from langchain.llms import OpenAI
from llama_index.readers.wordlift import WordLiftLoader
# Set up the necessary configuration options
endpoint = "https://api.wordlift.io/graphql"
headers = {
"Authorization": "<YOUR_WORDLIFT_KEY>",
"Content-Type": "application/json",
}
query = """
# Your GraphQL query here
"""
fields = "<YOUR_FIELDS>"
config_options = {
"text_fields": ["<YOUR_TEXT_FIELDS>"],
"metadata_fields": ["<YOUR_METADATA_FIELDS>"],
}
# Create an instance of the WordLiftLoader
reader = WordLiftLoader(endpoint, headers, query, fields, config_options)
# Load the data
documents = reader.load_data()
# Convert the documents
converted_doc = []
for doc in documents:
converted_doc_id = json.dumps(doc.doc_id)
converted_doc.append(
Document(
text=doc.text,
doc_id=converted_doc_id,
embedding=doc.embedding,
doc_hash=doc.doc_hash,
extra_info=doc.extra_info,
)
)
# Create the index and query engine
index = VectorStoreIndex.from_documents(converted_doc)
query_engine = index.as_query_engine()
# Perform a query
result = query_engine.query("<YOUR_QUERY>")
# Process the result as needed
logging.info("Result: %s", result)
```
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"description": "# WordLift Reader\n\n```bash\npip install llama-index-readers-wordlift\n```\n\nThe WordLift GraphQL Reader is a connector to fetch and transform data from a WordLift Knowledge Graph using your the WordLift Key. The connector provides a convenient way to load data from WordLift using a GraphQL query and transform it into a list of documents for further processing.\n\n## Usage\n\nTo use the WordLift GraphQL Reader, follow the steps below:\n\n1. Set up the necessary configuration options, such as the API endpoint, headers, query, fields, and configuration options (make sure you have with you the [Wordlift Key](https://docs.wordlift.io/pages/key-concepts/#wordlift-key)).\n2. Create an instance of the `WordLiftLoader` class, passing in the configuration options.\n3. Use the `load_data` method to fetch and transform the data.\n4. Process the loaded documents as needed.\n\nHere's an example of how to use the WordLift GraphQL Reader:\n\n```python\nimport json\nfrom llama_index.core import VectorStoreIndex\nfrom llama_index.core import Document\nfrom langchain.llms import OpenAI\nfrom llama_index.readers.wordlift import WordLiftLoader\n\n# Set up the necessary configuration options\nendpoint = \"https://api.wordlift.io/graphql\"\nheaders = {\n \"Authorization\": \"<YOUR_WORDLIFT_KEY>\",\n \"Content-Type\": \"application/json\",\n}\n\nquery = \"\"\"\n# Your GraphQL query here\n\"\"\"\nfields = \"<YOUR_FIELDS>\"\nconfig_options = {\n \"text_fields\": [\"<YOUR_TEXT_FIELDS>\"],\n \"metadata_fields\": [\"<YOUR_METADATA_FIELDS>\"],\n}\n# Create an instance of the WordLiftLoader\nreader = WordLiftLoader(endpoint, headers, query, fields, config_options)\n\n# Load the data\ndocuments = reader.load_data()\n\n# Convert the documents\nconverted_doc = []\nfor doc in documents:\n converted_doc_id = json.dumps(doc.doc_id)\n converted_doc.append(\n Document(\n text=doc.text,\n doc_id=converted_doc_id,\n embedding=doc.embedding,\n doc_hash=doc.doc_hash,\n extra_info=doc.extra_info,\n )\n )\n\n# Create the index and query engine\nindex = VectorStoreIndex.from_documents(converted_doc)\nquery_engine = index.as_query_engine()\n\n# Perform a query\nresult = query_engine.query(\"<YOUR_QUERY>\")\n\n# Process the result as needed\nlogging.info(\"Result: %s\", result)\n```\n",
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