| from langchain_pinecone import PineconeVectorStore |
| from src.helper import download_embeddings |
| from dotenv import load_dotenv |
|
|
| load_dotenv() |
|
|
| embedding_model = download_embeddings() |
|
|
| INDEX_NAME = "assessment-recommender-agent" |
|
|
| vectorstore = PineconeVectorStore.from_existing_index(index_name=INDEX_NAME, embedding=embedding_model) |
|
|
| def get_retriever(k=10): |
| return vectorstore.as_retriever(search_kwargs={"k": k}) |
|
|
| def get_metadata_retriever(job_level=None, assessment_focus=None, languages=None, k=10): |
| if job_level and assessment_focus : |
| if languages: |
| return vectorstore.as_retriever(search_kwargs={"k": k, |
| "filter" : { |
| "job_levels": {"$in": job_level} if job_level else None, |
| "languages": {"$in": languages} if languages else None, |
| "keys": {"$in": assessment_focus} if assessment_focus else None |
| }}) |
| else: |
| return vectorstore.as_retriever(search_kwargs={"k": k, |
| "filter" : { |
| "job_levels": {"$in": job_level} if job_level else None, |
| "keys": {"$in": assessment_focus} if assessment_focus else None |
| }}) |
| else: |
| return None |
