--- a
+++ b/src/Matcher/database_builder.py
@@ -0,0 +1,108 @@
+# Import the load_dotenv function
+from dotenv import load_dotenv
+import os
+import json
+from langchain.docstore.document import Document
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+
+
+import json
+import os
+from langchain.docstore.document import Document
+
+embeddings = HuggingFaceEmbeddings(model_name="dmlls/all-mpnet-base-v2-negation")
+
+class TrialDatabaseBuilder:
+    def __init__(self, json_directory, desired_fields, fields_to_concatenate):
+        self.json_directory = json_directory
+        self.desired_fields = desired_fields
+        self.fields_to_concatenate = fields_to_concatenate
+        self.docs = []
+        self.ids = []
+
+    def load_json_files(self):
+        for filename in os.listdir(self.json_directory):
+            if filename.endswith('.json'):
+                file_path = os.path.join(self.json_directory, filename)
+                with open(file_path, 'r') as file:
+                    json_data = json.load(file)
+                    extracted_data = {field: json_data.get(field) for field in self.desired_fields}
+                    self.ids.append(extracted_data["nct_id"])
+                    
+                    metadata = {
+                        "id": extracted_data.get("nct_id", ""),
+                        "gender": extracted_data.get("gender", ""),
+                        "condition": extracted_data.get("condition", ""),
+                        "phase": extracted_data.get("phase", ""),
+                        "minimum_age": extracted_data.get("minimum_age", ""),
+                        "maximum_age": extracted_data.get("maximum_age", ""),
+                        
+                    }
+                    metadata = {k: v for k, v in metadata.items() if v is not None}
+                        
+                    concatenated_string = ', '.join(str(extracted_data[field]) for field in self.fields_to_concatenate)
+                    doc  = Document(page_content=concatenated_string, metadata=metadata)
+                    self.docs.append(doc)
+                    
+    def build_vectorstore(self):
+        vectorstore = Chroma.from_documents(self.docs, embeddings, persist_directory="../../data/db/", collection_name="trials")
+        vectorstore.persist()
+        vectorstore = None
+
+
+class CriteriaDatabaseBuilder:
+    def __init__(self, json_directory, desired_fields):
+        self.json_directory = json_directory
+        self.desired_fields = desired_fields
+        self.docs = []
+
+    def load_json_files(self):
+        for filename in os.listdir(self.json_directory):
+            if filename.endswith('.json'):
+                file_path = os.path.join(self.json_directory, filename)
+                with open(file_path, 'r') as file:
+                    json_data = json.load(file)
+                    extracted_data = {field: json_data.get(field) for field in self.desired_fields}
+                    eligibility_criteria = json_data.get("eligibility")
+                    if eligibility_criteria is not None:
+                        for index, criterion in enumerate(eligibility_criteria):
+                            metadata = {
+                                "nct_id": extracted_data['nct_id'],
+                                "idx": index + 1,
+                            }
+                            metadata["criteria_type"] = criterion["entities_data"][0]["field"]
+                            for i, entity in enumerate(criterion["entities_data"]):
+                                for key, value in entity.items():
+                                    if key != "field":
+                                        metadata[f"{key}_{i + 1}"] = value
+                            doc = Document(page_content=criterion["text"], metadata=metadata)
+                            self.docs.append(doc)
+
+    def build_vectorstore(self):
+        vectorstore = Chroma.from_documents(self.docs, embeddings, persist_directory="../../data/db/", collection_name="criteria")
+        vectorstore.persist()
+        vectorstore = None
+
+
+def main():
+    load_dotenv('../.env')
+    openai_access_key = os.getenv('OPENAI_ACCESS_KEY')
+    huggingface_token = os.getenv('HUGGINGFACEHUB_API_TOKEN')
+    json_directory = '../../data/trials_jsons/'
+    desired_fields_criteria = ["nct_id", "eligibility"]
+
+    criteriadb_builder = CriteriaDatabaseBuilder(json_directory, desired_fields_criteria)
+    criteriadb_builder.load_json_files()
+    criteriadb_builder.build_vectorstore()
+    
+    desired_fields_trials=["nct_id", "brief_title", "brief_summary", "condition", "gender", "minimum_age", "maximum_age", "phase"]
+    trialdb_builder = TrialDatabaseBuilder(json_directory, desired_fields_trials)
+    trialdb_builder.load_json_files()
+    trialdb_builder.build_vectorstore()
+
+
+if __name__ == "__main__":
+    main()
+    
+    
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