mirror of
https://github.com/hwchase17/langchain
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63343b4987
Description: adds support for langchain_cohere --------- Co-authored-by: Harry M <127103098+harry-cohere@users.noreply.github.com> Co-authored-by: Erick Friis <erick@langchain.dev>
93 lines
2.8 KiB
Python
93 lines
2.8 KiB
Python
from __future__ import annotations
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from typing import TYPE_CHECKING, Any, Dict, List
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from langchain_core.callbacks import (
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AsyncCallbackManagerForRetrieverRun,
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CallbackManagerForRetrieverRun,
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)
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from langchain_core.documents import Document
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.messages import HumanMessage
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from langchain_core.pydantic_v1 import Field
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from langchain_core.retrievers import BaseRetriever
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if TYPE_CHECKING:
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from langchain_core.messages import BaseMessage
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def _get_docs(response: Any) -> List[Document]:
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docs = (
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[]
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if "documents" not in response.generation_info
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else [
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Document(page_content=doc["snippet"], metadata=doc)
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for doc in response.generation_info["documents"]
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]
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)
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docs.append(
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Document(
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page_content=response.message.content,
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metadata={
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"type": "model_response",
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"citations": response.generation_info["citations"],
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"search_results": response.generation_info["search_results"],
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"search_queries": response.generation_info["search_queries"],
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"token_count": response.generation_info["token_count"],
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},
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)
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)
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return docs
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class CohereRagRetriever(BaseRetriever):
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"""Cohere Chat API with RAG."""
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connectors: List[Dict] = Field(default_factory=lambda: [{"id": "web-search"}])
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"""
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When specified, the model's reply will be enriched with information found by
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querying each of the connectors (RAG). These will be returned as langchain
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documents.
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Currently only accepts {"id": "web-search"}.
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"""
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llm: BaseChatModel
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"""Cohere ChatModel to use."""
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class Config:
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"""Configuration for this pydantic object."""
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arbitrary_types_allowed = True
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"""Allow arbitrary types."""
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def _get_relevant_documents(
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self, query: str, *, run_manager: CallbackManagerForRetrieverRun, **kwargs: Any
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) -> List[Document]:
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messages: List[List[BaseMessage]] = [[HumanMessage(content=query)]]
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res = self.llm.generate(
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messages,
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connectors=self.connectors,
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callbacks=run_manager.get_child(),
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**kwargs,
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).generations[0][0]
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return _get_docs(res)
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async def _aget_relevant_documents(
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self,
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query: str,
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*,
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run_manager: AsyncCallbackManagerForRetrieverRun,
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**kwargs: Any,
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) -> List[Document]:
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messages: List[List[BaseMessage]] = [[HumanMessage(content=query)]]
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res = (
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await self.llm.agenerate(
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messages,
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connectors=self.connectors,
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callbacks=run_manager.get_child(),
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**kwargs,
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)
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).generations[0][0]
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return _get_docs(res)
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