mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-11 10:06:05 +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),
}
```
133 lines
4.3 KiB
Python
133 lines
4.3 KiB
Python
from __future__ import annotations
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from copy import deepcopy
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from typing import Any, Dict, List, Optional, Sequence, Union
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from langchain_core.callbacks.base import Callbacks
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from langchain_core.documents import BaseDocumentCompressor, Document
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from langchain_core.pydantic_v1 import root_validator
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from langchain_core.utils import get_from_dict_or_env
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class VolcengineRerank(BaseDocumentCompressor):
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"""Document compressor that uses `Volcengine Rerank API`."""
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client: Any = None
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"""Volcengine client to use for compressing documents."""
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ak: Optional[str] = None
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"""Access Key ID.
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https://www.volcengine.com/docs/84313/1254553"""
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sk: Optional[str] = None
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"""Secret Access Key.
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https://www.volcengine.com/docs/84313/1254553"""
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region: str = "api-vikingdb.volces.com"
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"""https://www.volcengine.com/docs/84313/1254488. """
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host: str = "cn-beijing"
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"""https://www.volcengine.com/docs/84313/1254488. """
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top_n: Optional[int] = 3
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"""Number of documents to return."""
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class Config:
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allow_population_by_field_name = True
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arbitrary_types_allowed = True
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extra = "forbid"
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@root_validator(pre=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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if not values.get("client"):
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try:
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from volcengine.viking_db import VikingDBService
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except ImportError:
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raise ImportError(
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"Could not import volcengine python package. "
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"Please install it with `pip install volcengine` "
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"or `pip install --user volcengine`."
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)
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values["ak"] = get_from_dict_or_env(values, "ak", "VOLC_API_AK")
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values["sk"] = get_from_dict_or_env(values, "sk", "VOLC_API_SK")
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values["client"] = VikingDBService(
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host="api-vikingdb.volces.com",
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region="cn-beijing",
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scheme="https",
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connection_timeout=30,
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socket_timeout=30,
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ak=values["ak"],
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sk=values["sk"],
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)
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return values
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def rerank(
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self,
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documents: Sequence[Union[str, Document, dict]],
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query: str,
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*,
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top_n: Optional[int] = -1,
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) -> List[Dict[str, Any]]:
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"""Returns an ordered list of documents ordered by their relevance to the provided query.
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Args:
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query: The query to use for reranking.
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documents: A sequence of documents to rerank.
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top_n : The number of results to return. If None returns all results.
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Defaults to self.top_n.
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""" # noqa: E501
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if len(documents) == 0: # to avoid empty api call
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return []
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docs = [
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{
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"query": query,
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"content": doc.page_content if isinstance(doc, Document) else doc,
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}
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for doc in documents
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]
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from volcengine.viking_db import VikingDBService
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client: VikingDBService = self.client
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results = client.batch_rerank(docs)
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result_dicts = []
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for index, score in enumerate(results):
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result_dicts.append({"index": index, "relevance_score": score})
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result_dicts.sort(key=lambda x: x["relevance_score"], reverse=True)
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top_n = top_n if (top_n is None or top_n > 0) else self.top_n
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return result_dicts[:top_n]
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def compress_documents(
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self,
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documents: Sequence[Document],
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query: str,
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callbacks: Optional[Callbacks] = None,
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) -> Sequence[Document]:
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"""
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Compress documents using Volcengine's rerank API.
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Args:
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documents: A sequence of documents to compress.
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query: The query to use for compressing the documents.
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callbacks: Callbacks to run during the compression process.
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Returns:
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A sequence of compressed documents.
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"""
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compressed = []
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for res in self.rerank(documents, query):
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doc = documents[res["index"]]
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doc_copy = Document(doc.page_content, metadata=deepcopy(doc.metadata))
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doc_copy.metadata["relevance_score"] = res["relevance_score"]
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compressed.append(doc_copy)
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return compressed
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