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# goal: store results from app.py into vector store
from structured_apparatus_chain import (
arxiv_chain as apparatus_arxiv_chain,
pub_med_chain as apparatus_pub_med_chain,
wikipedia_chain as apparatus_wikipedia_chain
)
from structured_experiment_chain import (
arxiv_chain as experiment_arxiv_chain,
pub_med_chain as experiment_pub_med_chain,
wikipedia_chain as experiment_wikipedia_chain
)
from weaviate_utils import init_client
from datetime import datetime, timezone
def main():
# exp_qury = "fabricating cellolouse based electronics"
# exp_qury = "fabrication of spider silk"
# app_query = "microscope"
# app_query = "A gas Condenser"
app_query = "Electron Microscope"
app_data = apparatus_arxiv_chain.invoke(app_query)
# exp_data = experiment_arxiv_chain.invoke(exp_qury)
weaviate_client = init_client()
component_collection = weaviate_client.collections.get("Component")
component_image_collection = weaviate_client.collections.get("ComponentImage")
science_experiment_collection = weaviate_client.collections.get("ScienceEperiment")
app_components = app_data["Material"]
for i in app_components:
app_uuid = component_collection.data.insert({
"Tags": app_data['Fields_of_study'],
"FeildsOfStudy" : app_data['Fields_of_study'],
"ToolName" : i,
"UsedInComps" : [app_query]
})
response = component_collection.query.bm25(
query="something that goes in a microscope",
limit=5
)
# exp_uuid = science_experiment_collection.data.insert({
# # "DateCreated": datetime.now(timezone.utc),
# "FieldsOfStudy": exp_data['Fields_of_study'],
# "Tags": exp_data['Fields_of_study'],
# "Experiment_Name": exp_data['Experiment_Name'],
# "Material": exp_data['Material'],
# "Sources": exp_data['Sources'],
# "Protocal": exp_data['Protocal'],
# "Purpose_of_Experiments": exp_data['Purpose_of_Experiments'],
# "Safety_Precaution": exp_data['Safety_Precuation'], # Corrected spelling mistake
# "Level_of_Difficulty": exp_data['Level_of_Difficulty'],
# })
response = science_experiment_collection.query.bm25(
query="silk",
limit=3
)
jj = science_experiment_collection.query.near_text(
query="biology",
limit=2
)
# uuid = component_collection.data.insert({
# "DateCreated" : datetime.now(timezone.utc),
# "UsedInComps" : [query],
# "ToolName" : shap_e_sample,
# "Tags" : shap_e_list,
# "feildsOfStudy" : shap_e_list,
# # "GlbBlob" : base_64_result,
# })
x = 0
if __name__ == '__main__':
main()
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