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feat(module): add param ids to ElasticVectorSearch.from_texts method (#7425)

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# add param ids to ElasticVectorSearch.from_texts method.

- Description: add param ids to ElasticVectorSearch.from_texts method.
- Issue: NA. It seems `add_texts` already supports passing in document
ids, but param `ids` is omitted in `from_texts` classmethod,
- Dependencies: None,
- Tag maintainer: @rlancemartin, @eyurtsev please have a look, thanks

```
    # ElasticVectorSearch add_texts
    def add_texts(
        self,
        texts: Iterable[str],
        metadatas: Optional[List[dict]] = None,
        refresh_indices: bool = True,
        ids: Optional[List[str]] = None,
        **kwargs: Any,
    ) -> List[str]:
        ...

```

```
    # ElasticVectorSearch from_texts
    @classmethod
    def from_texts(
        cls,
        texts: List[str],
        embedding: Embeddings,
        metadatas: Optional[List[dict]] = None,
        elasticsearch_url: Optional[str] = None,
        index_name: Optional[str] = None,
        refresh_indices: bool = True,
        **kwargs: Any,
    ) -> ElasticVectorSearch:

```


Co-authored-by: charosen <charosen@bupt.cn>
This commit is contained in:
charosen 2023-07-10 14:25:35 +08:00 committed by GitHub
parent a5fd8873b1
commit 50a9fcccb0
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1 changed files with 4 additions and 2 deletions
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6
langchain/vectorstores/elastic_vector_search.py
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@ -157,8 +157,8 @@ class ElasticVectorSearch(VectorStore, ABC):
self,
texts: Iterable[str],
metadatas: Optional[List[dict]] = None,
refresh_indices: bool = True,
ids: Optional[List[str]] = None,
refresh_indices: bool = True,
**kwargs: Any,
) -> List[str]:
"""Run more texts through the embeddings and add to the vectorstore.
@ -166,6 +166,7 @@ class ElasticVectorSearch(VectorStore, ABC):
Args:
texts: Iterable of strings to add to the vectorstore.
metadatas: Optional list of metadatas associated with the texts.
ids: Optional list of unique IDs.
refresh_indices: bool to refresh ElasticSearch indices
Returns:
@ -260,6 +261,7 @@ class ElasticVectorSearch(VectorStore, ABC):
texts: List[str],
embedding: Embeddings,
metadatas: Optional[List[dict]] = None,
ids: Optional[List[str]] = None,
elasticsearch_url: Optional[str] = None,
index_name: Optional[str] = None,
refresh_indices: bool = True,
@ -292,7 +294,7 @@ class ElasticVectorSearch(VectorStore, ABC):
index_name = index_name or uuid.uuid4().hex
vectorsearch = cls(elasticsearch_url, index_name, embedding, **kwargs)
vectorsearch.add_texts(
texts, metadatas=metadatas, refresh_indices=refresh_indices
texts, metadatas=metadatas, ids=ids, refresh_indices=refresh_indices
)
return vectorsearch