diff --git a/README.md b/README.md
index 8b8254e..7369db3 100644
--- a/README.md
+++ b/README.md
@@ -255,7 +255,7 @@ Web UI 可以实现如下功能：
- [x] VUE 前端
## 项目交流群
-
+
🎉 langchain-ChatGLM 项目微信交流群，如果你也对本项目感兴趣，欢迎加入群聊参与讨论交流。
diff --git a/chains/modules/embeddings.py b/chains/modules/embeddings.py
deleted file mode 100644
index 3abeddf..0000000
--- a/chains/modules/embeddings.py
+++ /dev/null
@@ -1,34 +0,0 @@
-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()
diff --git a/chains/modules/vectorstores.py b/chains/modules/vectorstores.py
deleted file mode 100644
index da89775..0000000
--- a/chains/modules/vectorstores.py
+++ /dev/null
@@ -1,121 +0,0 @@
-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)
-
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