122 lines
4.7 KiB
Python
122 lines
4.7 KiB
Python
from langchain.vectorstores import FAISS
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from typing import Any, Callable, List, Optional, Tuple, Dict
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from langchain.docstore.document import Document
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from langchain.docstore.base import Docstore
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from langchain.vectorstores.utils import maximal_marginal_relevance
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from langchain.embeddings.base import Embeddings
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import uuid
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from langchain.docstore.in_memory import InMemoryDocstore
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import numpy as np
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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 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 FAISSVS(FAISS):
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def __init__(self,
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embedding_function: Callable[..., Any],
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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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super().__init__(embedding_function, index, docstore, index_to_docstore_id)
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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, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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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 with scores selected by maximal marginal relevance.
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"""
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scores, 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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selected_scores = [scores[0][i] for i in mmr_selected]
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docs = []
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for i, score in zip(selected_indices, selected_scores):
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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, score))
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return docs
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def max_marginal_relevance_search(
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self,
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query: str,
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k: int = 4,
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fetch_k: int = 20,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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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 with scores 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(
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cls,
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texts: List[str],
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embeddings: List[List[float]],
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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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faiss = dependable_faiss_import()
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index = faiss.IndexFlatIP(len(embeddings[0]))
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index.add(np.array(embeddings, dtype=np.float32))
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# # my code, for speeding up search
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# quantizer = faiss.IndexFlatL2(len(embeddings[0]))
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# index = faiss.IndexIVFFlat(quantizer, len(embeddings[0]), 100)
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# index.train(np.array(embeddings, dtype=np.float32))
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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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