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| from langchain_openai import OpenAIEmbeddings, ChatOpenAI | |
| from qdrant_client import QdrantClient | |
| def setup_openai_embeddings(api_key): | |
| """Set up OpenAI embeddings.""" | |
| return OpenAIEmbeddings(model='text-embedding-3-small', openai_api_key=api_key) | |
| def setup_qdrant_client(url, api_key): | |
| """Set up Qdrant client.""" | |
| return QdrantClient(location=url, api_key=api_key) | |
| def format_document_metadata(docs): | |
| """Format metadata for each document.""" | |
| formatted_docs = [] | |
| for doc in docs: | |
| metadata_str = ', '.join(f"{key}: {value}" for key, value in doc.metadata.items()) | |
| doc_str = f"{doc.page_content}\nMetadata: {metadata_str}" | |
| formatted_docs.append(doc_str) | |
| return "\n\n".join(formatted_docs) | |
| def openai_llm(api_key: str): | |
| """Get a configured OpenAI language model.""" | |
| return ChatOpenAI(model_name="gpt-4o", temperature=0, openai_api_key=api_key) | |
| def delete_collection(collection_name, qdrant_url, qdrant_api_key): | |
| """Delete a Qdrant collection.""" | |
| client = setup_qdrant_client(qdrant_url, qdrant_api_key) | |
| try: | |
| client.delete_collection(collection_name=collection_name) | |
| except Exception as e: | |
| print("Failed to delete collection:", e) | |
| def is_document_embedded(filename): | |
| """Check if a document is already embedded. Actual implementation needed.""" | |
| return False |