mirror of
https://github.com/hwchase17/langchain.git
synced 2025-06-22 14:49:29 +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
4.2 KiB
Python
124 lines
4.2 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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import requests
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from langchain_core.callbacks 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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JINA_API_URL: str = "https://api.jina.ai/v1/rerank"
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class JinaRerank(BaseDocumentCompressor):
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"""Document compressor that uses `Jina Rerank API`."""
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session: Any = None
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"""Requests session to communicate with API."""
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top_n: Optional[int] = 3
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"""Number of documents to return."""
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model: str = "jina-reranker-v1-base-en"
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"""Model to use for reranking."""
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jina_api_key: Optional[str] = None
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"""Jina API key. Must be specified directly or via environment variable
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JINA_API_KEY."""
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user_agent: str = "langchain"
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"""Identifier for the application making the request."""
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class Config:
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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 exists in environment."""
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jina_api_key = get_from_dict_or_env(values, "jina_api_key", "JINA_API_KEY")
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user_agent = values.get("user_agent", "langchain")
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session = requests.Session()
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session.headers.update(
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{
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"Authorization": f"Bearer {jina_api_key}",
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"Accept-Encoding": "identity",
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"Content-type": "application/json",
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"user-agent": user_agent,
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}
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)
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values["session"] = session
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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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model: Optional[str] = None,
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top_n: Optional[int] = -1,
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max_chunks_per_doc: Optional[int] = None,
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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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model: The model to use for re-ranking. Default to self.model.
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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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max_chunks_per_doc : The maximum number of chunks derived from a document.
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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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doc.page_content if isinstance(doc, Document) else doc for doc in documents
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]
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model = model or self.model
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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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data = {
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"query": query,
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"documents": docs,
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"model": model,
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"top_n": top_n,
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}
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resp = self.session.post(
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JINA_API_URL,
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json=data,
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).json()
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if "results" not in resp:
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raise RuntimeError(resp["detail"])
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results = resp["results"]
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result_dicts = []
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for res in results:
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result_dicts.append(
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{"index": res["index"], "relevance_score": res["relevance_score"]}
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)
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return result_dicts
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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 Jina'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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