mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-10 17:46:15 +00:00
bf5193bb99
Upgrade to using a literal for specifying the extra which is the
recommended approach in pydantic 2.
This works correctly also in pydantic v1.
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel, extra="forbid"):
x: int
Foo(x=5, y=1)
```
And
```python
from pydantic.v1 import BaseModel
class Foo(BaseModel):
x: int
class Config:
extra = "forbid"
Foo(x=5, y=1)
```
## Enum -> literal using grit pattern:
```
engine marzano(0.1)
language python
or {
`extra=Extra.allow` => `extra="allow"`,
`extra=Extra.forbid` => `extra="forbid"`,
`extra=Extra.ignore` => `extra="ignore"`
}
```
Resorted attributes in config and removed doc-string in case we will
need to deal with going back and forth between pydantic v1 and v2 during
the 0.3 release. (This will reduce merge conflicts.)
## Sort attributes in Config:
```
engine marzano(0.1)
language python
function sort($values) js {
return $values.text.split(',').sort().join("\n");
}
class_definition($name, $body) as $C where {
$name <: `Config`,
$body <: block($statements),
$values = [],
$statements <: some bubble($values) assignment() as $A where {
$values += $A
},
$body => sort($values),
}
```
124 lines
3.8 KiB
Python
124 lines
3.8 KiB
Python
from __future__ import annotations
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import concurrent.futures
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from typing import Any, Iterable, List, Optional
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import numpy as np
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from langchain_core.callbacks import CallbackManagerForRetrieverRun
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.retrievers import BaseRetriever
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def create_index(contexts: List[str], embeddings: Embeddings) -> np.ndarray:
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"""
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Create an index of embeddings for a list of contexts.
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Args:
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contexts: List of contexts to embed.
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embeddings: Embeddings model to use.
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Returns:
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Index of embeddings.
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"""
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with concurrent.futures.ThreadPoolExecutor() as executor:
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return np.array(list(executor.map(embeddings.embed_query, contexts)))
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class NanoPQRetriever(BaseRetriever):
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"""`NanoPQ retriever."""
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embeddings: Embeddings
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"""Embeddings model to use."""
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index: Any
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"""Index of embeddings."""
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texts: List[str]
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"""List of texts to index."""
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metadatas: Optional[List[dict]] = None
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"""List of metadatas corresponding with each text."""
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k: int = 4
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"""Number of results to return."""
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relevancy_threshold: Optional[float] = None
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"""Threshold for relevancy."""
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subspace: int = 4
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"""No of subspaces to be created, should be a multiple of embedding shape"""
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clusters: int = 128
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"""No of clusters to be created"""
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class Config:
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arbitrary_types_allowed = True
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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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embeddings: Embeddings,
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metadatas: Optional[List[dict]] = None,
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**kwargs: Any,
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) -> NanoPQRetriever:
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index = create_index(texts, embeddings)
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return cls(
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embeddings=embeddings,
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index=index,
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texts=texts,
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metadatas=metadatas,
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**kwargs,
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)
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@classmethod
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def from_documents(
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cls,
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documents: Iterable[Document],
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embeddings: Embeddings,
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**kwargs: Any,
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) -> NanoPQRetriever:
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texts, metadatas = zip(*((d.page_content, d.metadata) for d in documents))
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return cls.from_texts(
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texts=texts, embeddings=embeddings, metadatas=metadatas, **kwargs
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)
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def _get_relevant_documents(
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self, query: str, *, run_manager: CallbackManagerForRetrieverRun
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) -> List[Document]:
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try:
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from nanopq import PQ
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except ImportError:
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raise ImportError(
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"Could not import nanopq, please install with `pip install " "nanopq`."
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)
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query_embeds = np.array(self.embeddings.embed_query(query))
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try:
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pq = PQ(M=self.subspace, Ks=self.clusters, verbose=True).fit(
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self.index.astype("float32")
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)
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except AssertionError:
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error_message = (
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"Received params: training_sample={training_sample}, "
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"n_cluster={n_clusters}, subspace={subspace}, "
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"embedding_shape={embedding_shape}. Issue with the combination. "
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"Please retrace back to find the exact error"
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).format(
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training_sample=self.index.shape[0],
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n_clusters=self.clusters,
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subspace=self.subspace,
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embedding_shape=self.index.shape[1],
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)
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raise RuntimeError(error_message)
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index_code = pq.encode(vecs=self.index.astype("float32"))
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dt = pq.dtable(query=query_embeds.astype("float32"))
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dists = dt.adist(codes=index_code)
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sorted_ix = np.argsort(dists)
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top_k_results = [
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Document(
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page_content=self.texts[row],
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metadata=self.metadatas[row] if self.metadatas else {},
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)
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for row in sorted_ix[0 : self.k]
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]
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return top_k_results
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