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),
}
```
126 lines
4.0 KiB
Python
126 lines
4.0 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 SVMRetriever(BaseRetriever):
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"""`SVM` retriever.
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Largely based on
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https://github.com/karpathy/randomfun/blob/master/knn_vs_svm.ipynb
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"""
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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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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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) -> SVMRetriever:
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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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) -> SVMRetriever:
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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 sklearn import svm
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except ImportError:
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raise ImportError(
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"Could not import scikit-learn, please install with `pip install "
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"scikit-learn`."
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)
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query_embeds = np.array(self.embeddings.embed_query(query))
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x = np.concatenate([query_embeds[None, ...], self.index])
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y = np.zeros(x.shape[0])
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y[0] = 1
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clf = svm.LinearSVC(
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class_weight="balanced", verbose=False, max_iter=10000, tol=1e-6, C=0.1
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)
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clf.fit(x, y)
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similarities = clf.decision_function(x)
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sorted_ix = np.argsort(-similarities)
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# svm.LinearSVC in scikit-learn is non-deterministic.
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# if a text is the same as a query, there is no guarantee
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# the query will be in the first index.
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# this performs a simple swap, this works because anything
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# left of the 0 should be equivalent.
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zero_index = np.where(sorted_ix == 0)[0][0]
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if zero_index != 0:
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sorted_ix[0], sorted_ix[zero_index] = sorted_ix[zero_index], sorted_ix[0]
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denominator = np.max(similarities) - np.min(similarities) + 1e-6
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normalized_similarities = (similarities - np.min(similarities)) / denominator
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top_k_results = []
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for row in sorted_ix[1 : self.k + 1]:
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if (
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self.relevancy_threshold is None
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or normalized_similarities[row] >= self.relevancy_threshold
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):
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metadata = self.metadatas[row - 1] if self.metadatas else {}
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doc = Document(page_content=self.texts[row - 1], metadata=metadata)
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top_k_results.append(doc)
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return top_k_results
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