update README.md

chains/modules/embeddings.py

-from langchain.embeddings.huggingface import HuggingFaceEmbeddings
-
-from typing import Any, List
-
-
-class MyEmbeddings(HuggingFaceEmbeddings):
-    def __init__(self, **kwargs: Any):
-        super().__init__(**kwargs)
-        
-    def embed_documents(self, texts: List[str]) -> List[List[float]]:
-        """Compute doc embeddings using a HuggingFace transformer model.
-
-        Args:
-            texts: The list of texts to embed.
-
-        Returns:
-            List of embeddings, one for each text.
-        """
-        texts = list(map(lambda x: x.replace("\n", " "), texts))
-        embeddings = self.client.encode(texts, normalize_embeddings=True)
-        return embeddings.tolist()
-
-    def embed_query(self, text: str) -> List[float]:
-        """Compute query embeddings using a HuggingFace transformer model.
-
-        Args:
-            text: The text to embed.
-
-        Returns:
-            Embeddings for the text.
-        """
-        text = text.replace("\n", " ")
-        embedding = self.client.encode(text, normalize_embeddings=True)
-        return embedding.tolist()

chains/modules/vectorstores.py

-from langchain.vectorstores import FAISS
-from typing import Any, Callable, List, Optional, Tuple, Dict
-from langchain.docstore.document import Document
-from langchain.docstore.base import Docstore
-
-from langchain.vectorstores.utils import maximal_marginal_relevance
-from langchain.embeddings.base import Embeddings
-import uuid
-from langchain.docstore.in_memory import InMemoryDocstore
-
-import numpy as np
-
-def dependable_faiss_import() -> Any:
-    """Import faiss if available, otherwise raise error."""
-    try:
-        import faiss
-    except ImportError:
-        raise ValueError(
-            "Could not import faiss python package. "
-            "Please install it with `pip install faiss` "
-            "or `pip install faiss-cpu` (depending on Python version)."
-        )
-    return faiss
-
-class FAISSVS(FAISS):
-    def __init__(self, 
-                 embedding_function: Callable[..., Any], 
-                 index: Any, 
-                 docstore: Docstore, 
-                 index_to_docstore_id: Dict[int, str]):
-        super().__init__(embedding_function, index, docstore, index_to_docstore_id)
-
-    def max_marginal_relevance_search_by_vector(
-        self, embedding: List[float], k: int = 4, fetch_k: int = 20, **kwargs: Any
-    ) -> List[Tuple[Document, float]]:
-        """Return docs selected using the maximal marginal relevance.
-
-        Maximal marginal relevance optimizes for similarity to query AND diversity
-        among selected documents.
-
-        Args:
-            embedding: Embedding to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
-            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-
-        Returns:
-            List of Documents with scores selected by maximal marginal relevance.
-        """
-        scores, indices = self.index.search(np.array([embedding], dtype=np.float32), fetch_k)
-        # -1 happens when not enough docs are returned.
-        embeddings = [self.index.reconstruct(int(i)) for i in indices[0] if i != -1]
-        mmr_selected = maximal_marginal_relevance(
-            np.array([embedding], dtype=np.float32), embeddings, k=k
-        )
-        selected_indices = [indices[0][i] for i in mmr_selected]
-        selected_scores = [scores[0][i] for i in mmr_selected]
-        docs = []
-        for i, score in zip(selected_indices, selected_scores):
-            if i == -1:
-                # This happens when not enough docs are returned.
-                continue
-            _id = self.index_to_docstore_id[i]
-            doc = self.docstore.search(_id)
-            if not isinstance(doc, Document):
-                raise ValueError(f"Could not find document for id {_id}, got {doc}")
-            docs.append((doc, score))
-        return docs
-
-    def max_marginal_relevance_search(
-        self,
-        query: str,
-        k: int = 4,
-        fetch_k: int = 20,
-        **kwargs: Any,
-    ) -> List[Tuple[Document, float]]:
-        """Return docs selected using the maximal marginal relevance.
-
-        Maximal marginal relevance optimizes for similarity to query AND diversity
-        among selected documents.
-
-        Args:
-            query: Text to look up documents similar to.
-            k: Number of Documents to return. Defaults to 4.
-            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
-
-        Returns:
-            List of Documents with scores selected by maximal marginal relevance.
-        """
-        embedding = self.embedding_function(query)
-        docs = self.max_marginal_relevance_search_by_vector(embedding, k, fetch_k)
-        return docs
-    
-    @classmethod
-    def __from(
-        cls,
-        texts: List[str],
-        embeddings: List[List[float]],
-        embedding: Embeddings,
-        metadatas: Optional[List[dict]] = None,
-        **kwargs: Any,
-    ) -> FAISS:
-        faiss = dependable_faiss_import()
-        index = faiss.IndexFlatIP(len(embeddings[0]))
-        index.add(np.array(embeddings, dtype=np.float32))
-
-        # # my code, for speeding up search
-        # quantizer = faiss.IndexFlatL2(len(embeddings[0]))
-        # index = faiss.IndexIVFFlat(quantizer, len(embeddings[0]), 100)
-        # index.train(np.array(embeddings, dtype=np.float32))
-        # index.add(np.array(embeddings, dtype=np.float32))
-
-        documents = []
-        for i, text in enumerate(texts):
-            metadata = metadatas[i] if metadatas else {}
-            documents.append(Document(page_content=text, metadata=metadata))
-        index_to_id = {i: str(uuid.uuid4()) for i in range(len(documents))}
-        docstore = InMemoryDocstore(
-            {index_to_id[i]: doc for i, doc in enumerate(documents)}
-        )
-        return cls(embedding.embed_query, index, docstore, index_to_id)
-