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299 lines
11 KiB
Python
299 lines
11 KiB
Python
"""Wrapper around FAISS vector database."""
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from __future__ import annotations
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import pickle
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import uuid
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from pathlib import Path
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from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
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import numpy as np
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from langchain.docstore.base import AddableMixin, Docstore
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from langchain.docstore.document import Document
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from langchain.docstore.in_memory import InMemoryDocstore
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from langchain.embeddings.base import Embeddings
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from langchain.vectorstores.base import VectorStore
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from langchain.vectorstores.utils import maximal_marginal_relevance
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def dependable_faiss_import() -> Any:
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"""Import faiss if available, otherwise raise error."""
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try:
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import faiss
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except ImportError:
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raise ValueError(
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"Could not import faiss python package. "
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"Please it install it with `pip install faiss` "
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"or `pip install faiss-cpu` (depending on Python version)."
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)
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return faiss
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class FAISS(VectorStore):
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"""Wrapper around FAISS vector database.
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To use, you should have the ``faiss`` python package installed.
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Example:
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.. code-block:: python
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from langchain import FAISS
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faiss = FAISS(embedding_function, index, docstore)
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"""
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def __init__(
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self,
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embedding_function: Callable,
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index: Any,
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docstore: Docstore,
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index_to_docstore_id: Dict[int, str],
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):
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"""Initialize with necessary components."""
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self.embedding_function = embedding_function
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self.index = index
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self.docstore = docstore
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self.index_to_docstore_id = index_to_docstore_id
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Run more texts through the embeddings and add to the vectorstore.
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Args:
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texts: Iterable of strings to add to the vectorstore.
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metadatas: Optional list of metadatas associated with the texts.
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Returns:
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List of ids from adding the texts into the vectorstore.
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"""
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if not isinstance(self.docstore, AddableMixin):
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raise ValueError(
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"If trying to add texts, the underlying docstore should support "
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f"adding items, which {self.docstore} does not"
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)
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# Embed and create the documents.
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embeddings = [self.embedding_function(text) for text in texts]
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documents = []
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for i, text in enumerate(texts):
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metadata = metadatas[i] if metadatas else {}
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documents.append(Document(page_content=text, metadata=metadata))
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# Add to the index, the index_to_id mapping, and the docstore.
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starting_len = len(self.index_to_docstore_id)
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self.index.add(np.array(embeddings, dtype=np.float32))
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# Get list of index, id, and docs.
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full_info = [
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(starting_len + i, str(uuid.uuid4()), doc)
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for i, doc in enumerate(documents)
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]
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# Add information to docstore and index.
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self.docstore.add({_id: doc for _, _id, doc in full_info})
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index_to_id = {index: _id for index, _id, _ in full_info}
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self.index_to_docstore_id.update(index_to_id)
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return [_id for _, _id, _ in full_info]
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def similarity_search_with_score_by_vector(
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self, embedding: List[float], k: int = 4
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) -> List[Tuple[Document, float]]:
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"""Return docs most similar to query.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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Returns:
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List of Documents most similar to the query and score for each
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"""
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scores, indices = self.index.search(np.array([embedding], dtype=np.float32), k)
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docs = []
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for j, i in enumerate(indices[0]):
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if i == -1:
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# This happens when not enough docs are returned.
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continue
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_id = self.index_to_docstore_id[i]
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doc = self.docstore.search(_id)
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if not isinstance(doc, Document):
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raise ValueError(f"Could not find document for id {_id}, got {doc}")
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docs.append((doc, scores[0][j]))
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return docs
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def similarity_search_with_score(
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self, query: str, k: int = 4
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) -> List[Tuple[Document, float]]:
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"""Return docs most similar to query.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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Returns:
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List of Documents most similar to the query and score for each
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"""
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embedding = self.embedding_function(query)
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docs = self.similarity_search_with_score_by_vector(embedding, k)
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return docs
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def similarity_search_by_vector(
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self, embedding: List[float], k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Return docs most similar to embedding vector.
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Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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Returns:
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List of Documents most similar to the embedding.
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"""
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docs_and_scores = self.similarity_search_with_score_by_vector(embedding, k)
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return [doc for doc, _ in docs_and_scores]
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def similarity_search(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Return docs most similar to query.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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Returns:
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List of Documents most similar to the query.
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"""
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docs_and_scores = self.similarity_search_with_score(query, k)
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return [doc for doc, _ in docs_and_scores]
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def max_marginal_relevance_search_by_vector(
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self, embedding: List[float], k: int = 4, fetch_k: int = 20
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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_, indices = self.index.search(np.array([embedding], dtype=np.float32), fetch_k)
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# -1 happens when not enough docs are returned.
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embeddings = [self.index.reconstruct(int(i)) for i in indices[0] if i != -1]
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mmr_selected = maximal_marginal_relevance(
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np.array([embedding], dtype=np.float32), embeddings, k=k
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)
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selected_indices = [indices[0][i] for i in mmr_selected]
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docs = []
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for i in selected_indices:
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if i == -1:
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# This happens when not enough docs are returned.
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continue
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_id = self.index_to_docstore_id[i]
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doc = self.docstore.search(_id)
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if not isinstance(doc, Document):
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raise ValueError(f"Could not find document for id {_id}, got {doc}")
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docs.append(doc)
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return docs
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def max_marginal_relevance_search(
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self, query: str, k: int = 4, fetch_k: int = 20
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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embedding = self.embedding_function(query)
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docs = self.max_marginal_relevance_search_by_vector(embedding, k, fetch_k)
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return docs
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@classmethod
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def from_texts(
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cls,
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> FAISS:
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"""Construct FAISS wrapper from raw documents.
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This is a user friendly interface that:
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1. Embeds documents.
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2. Creates an in memory docstore
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3. Initializes the FAISS database
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This is intended to be a quick way to get started.
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Example:
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.. code-block:: python
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from langchain import FAISS
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from langchain.embeddings import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings()
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faiss = FAISS.from_texts(texts, embeddings)
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"""
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faiss = dependable_faiss_import()
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embeddings = embedding.embed_documents(texts)
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index = faiss.IndexFlatL2(len(embeddings[0]))
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index.add(np.array(embeddings, dtype=np.float32))
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documents = []
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for i, text in enumerate(texts):
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metadata = metadatas[i] if metadatas else {}
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documents.append(Document(page_content=text, metadata=metadata))
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index_to_id = {i: str(uuid.uuid4()) for i in range(len(documents))}
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docstore = InMemoryDocstore(
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{index_to_id[i]: doc for i, doc in enumerate(documents)}
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)
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return cls(embedding.embed_query, index, docstore, index_to_id)
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def save_local(self, folder_path: str) -> None:
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"""Save FAISS index, docstore, and index_to_docstore_id to disk.
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Args:
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folder_path: folder path to save index, docstore,
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and index_to_docstore_id to.
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"""
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path = Path(folder_path)
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path.mkdir(exist_ok=True, parents=True)
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# save index separately since it is not picklable
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faiss = dependable_faiss_import()
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faiss.write_index(self.index, str(path / "index.faiss"))
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# save docstore and index_to_docstore_id
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with open(path / "index.pkl", "wb") as f:
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pickle.dump((self.docstore, self.index_to_docstore_id), f)
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@classmethod
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def load_local(cls, folder_path: str, embeddings: Embeddings) -> FAISS:
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"""Load FAISS index, docstore, and index_to_docstore_id to disk.
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Args:
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folder_path: folder path to load index, docstore,
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and index_to_docstore_id from.
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embeddings: Embeddings to use when generating queries
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"""
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path = Path(folder_path)
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# load index separately since it is not picklable
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faiss = dependable_faiss_import()
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index = faiss.read_index(str(path / "index.faiss"))
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# load docstore and index_to_docstore_id
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with open(path / "index.pkl", "rb") as f:
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docstore, index_to_docstore_id = pickle.load(f)
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return cls(embeddings.embed_query, index, docstore, index_to_docstore_id)
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