Updates to collections

app.py

@@ -10,9 +10,21 @@ from typing import List, Dict
 import os
 import tempfile
 
-# Initialize embeddings and vector store
+# Initialize embeddings
 embeddings = HuggingFaceEmbeddings()
-vector_store = Chroma(embedding_function=embeddings, persist_directory="./chroma_db")
+
+# Initialize separate vector stores for resumes and culture docs
+resume_store = Chroma(
+    collection_name="resumes",
+    embedding_function=embeddings,
+    persist_directory="./chroma_db"
+)
+
+culture_store = Chroma(
+    collection_name="culture_docs",
+    embedding_function=embeddings,
+    persist_directory="./chroma_db"
+)
 
 # Initialize LLM
 llm = ChatGroq(
@@ -74,7 +86,7 @@ def store_culture_docs(culture_files: List[tempfile._TemporaryFileWrapper]) -> s
         splits = text_splitter.split_documents(docs)
         all_docs.extend(splits)
     
-    vector_store.add_documents(all_docs, collection_name="culture_docs")
+    culture_store.add_documents(all_docs)
     return f"Successfully stored {len(all_docs)} culture document chunks"
 
 def store_resumes(resume_files: List[tempfile._TemporaryFileWrapper]) -> str:
@@ -94,7 +106,7 @@ def store_resumes(resume_files: List[tempfile._TemporaryFileWrapper]) -> str:
         splits = text_splitter.split_documents(docs)
         all_docs.extend(splits)
     
-    vector_store.add_documents(all_docs, collection_name="resumes")
+    resume_store.add_documents(all_docs)
     return f"Successfully stored {len(all_docs)} resume chunks"
 
 def analyze_candidates(job_description: str) -> str:
@@ -117,18 +129,15 @@ def analyze_candidates(job_description: str) -> str:
     
     skills = skills_chain.run({"job_description": job_description})
     
-    # Query vector store for matching resumes
-    results = vector_store.similarity_search(
+    # Query vector stores separately
+    results = resume_store.similarity_search(
         job_description,
-        k=5,
-        collection_name="resumes"
+        k=5
     )
     
-    # Get culture documentation
-    culture_docs = vector_store.similarity_search(
+    culture_docs = culture_store.similarity_search(
         job_description,
-        k=3,
-        collection_name="culture_docs"
+        k=3
     )
     
     # Analysis prompt template