mirror of
https://github.com/hwchase17/langchain
synced 2024-11-02 09:40:22 +00:00
ed58eeb9c5
Moved the following modules to new package langchain-community in a backwards compatible fashion: ``` mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community mv langchain/langchain/adapters community/langchain_community mv langchain/langchain/callbacks community/langchain_community/callbacks mv langchain/langchain/chat_loaders community/langchain_community mv langchain/langchain/chat_models community/langchain_community mv langchain/langchain/document_loaders community/langchain_community mv langchain/langchain/docstore community/langchain_community mv langchain/langchain/document_transformers community/langchain_community mv langchain/langchain/embeddings community/langchain_community mv langchain/langchain/graphs community/langchain_community mv langchain/langchain/llms community/langchain_community mv langchain/langchain/memory/chat_message_histories community/langchain_community mv langchain/langchain/retrievers community/langchain_community mv langchain/langchain/storage community/langchain_community mv langchain/langchain/tools community/langchain_community mv langchain/langchain/utilities community/langchain_community mv langchain/langchain/vectorstores community/langchain_community mv langchain/langchain/agents/agent_toolkits community/langchain_community mv langchain/langchain/cache.py community/langchain_community ``` Moved the following to core ``` mv langchain/langchain/utils/json_schema.py core/langchain_core/utils mv langchain/langchain/utils/html.py core/langchain_core/utils mv langchain/langchain/utils/strings.py core/langchain_core/utils cat langchain/langchain/utils/env.py >> core/langchain_core/utils/env.py rm langchain/langchain/utils/env.py ``` See .scripts/community_split/script_integrations.sh for all changes
72 lines
2.0 KiB
Python
72 lines
2.0 KiB
Python
"""Wrapper around Embedchain Retriever."""
|
|
|
|
from __future__ import annotations
|
|
|
|
from typing import Any, Iterable, List, Optional
|
|
|
|
from langchain_core.callbacks import CallbackManagerForRetrieverRun
|
|
from langchain_core.documents import Document
|
|
from langchain_core.retrievers import BaseRetriever
|
|
|
|
|
|
class EmbedchainRetriever(BaseRetriever):
|
|
"""`Embedchain` retriever."""
|
|
|
|
client: Any
|
|
"""Embedchain Pipeline."""
|
|
|
|
@classmethod
|
|
def create(cls, yaml_path: Optional[str] = None) -> EmbedchainRetriever:
|
|
"""
|
|
Create a EmbedchainRetriever from a YAML configuration file.
|
|
|
|
Args:
|
|
yaml_path: Path to the YAML configuration file. If not provided,
|
|
a default configuration is used.
|
|
|
|
Returns:
|
|
An instance of EmbedchainRetriever.
|
|
|
|
"""
|
|
from embedchain import Pipeline
|
|
|
|
# Create an Embedchain Pipeline instance
|
|
if yaml_path:
|
|
client = Pipeline.from_config(yaml_path=yaml_path)
|
|
else:
|
|
client = Pipeline()
|
|
return cls(client=client)
|
|
|
|
def add_texts(
|
|
self,
|
|
texts: Iterable[str],
|
|
) -> List[str]:
|
|
"""Run more texts through the embeddings and add to the retriever.
|
|
|
|
Args:
|
|
texts: Iterable of strings/URLs to add to the retriever.
|
|
|
|
Returns:
|
|
List of ids from adding the texts into the retriever.
|
|
"""
|
|
ids = []
|
|
for text in texts:
|
|
_id = self.client.add(text)
|
|
ids.append(_id)
|
|
return ids
|
|
|
|
def _get_relevant_documents(
|
|
self, query: str, *, run_manager: CallbackManagerForRetrieverRun
|
|
) -> List[Document]:
|
|
res = self.client.search(query)
|
|
|
|
docs = []
|
|
for r in res:
|
|
docs.append(
|
|
Document(
|
|
page_content=r["context"],
|
|
metadata={"source": r["source"], "document_id": r["document_id"]},
|
|
)
|
|
)
|
|
return docs
|