Add Support for Faiss Vector Search

**Is your feature request related to a problem? Please describe.** Currently, langchain-rust lacks native support for [faiss](https://github.com/facebookresearch/faiss), a highly efficient library for similarity search and clustering of dense vectors. This limits the ability to perform fast and scalable vector-based searches, which are essential for many applications like recommendation systems, natural language processing, and semantic search. Without Faiss integration, users have to rely on less optimized or external solutions, which can be cumbersome and inefficient. **Describe the solution you'd like** I would like to see native support for Faiss in langchain-rust, enabling users to perform vector similarity searches directly within the library. This could include: Integration with Faiss indexes for storing and querying vectors. Helper functions to create, update, and query Faiss indexes seamlessly. Compatibility with existing ``langchain-rust`` components, such as embeddings and retrieval systems. **Describe alternatives you've considered** langchain-rust As an alternative, I have considered using external libraries or services like Pinecone, Weaviate, or Annoy for vector search. However, these solutions often require additional infrastructure or dependencies, which can complicate deployment and increase overhead. Faiss, being a lightweight and highly optimized library, would provide a more integrated and efficient solution for Rust users. **Additional context** Faiss is widely used in the machine learning community for its performance and scalability, and its integration into langchain-rust would significantly enhance the library's capabilities. This feature would be particularly useful for developers working on applications involving large-scale semantic search, recommendation systems, or any use case requiring efficient nearest-neighbor search in high-dimensional spaces.