mirror of
https://github.com/hwchase17/langchain.git
synced 2025-05-04 14:48:07 +00:00
0caef3cde7
- **Description:** Change instances of RunnableMap to RunnableParallel, as that should be the one used going forward. This makes it consistent across the codebase.
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
from operator import itemgetter
|
|
from typing import List, Optional, Tuple
|
|
|
|
from langchain.chat_models import ChatOpenAI
|
|
from langchain.embeddings import HuggingFaceEmbeddings
|
|
from langchain.schema import BaseMessage, format_document
|
|
from langchain.schema.output_parser import StrOutputParser
|
|
from langchain.schema.runnable import RunnableParallel, RunnablePassthrough
|
|
from langchain.vectorstores.elasticsearch import ElasticsearchStore
|
|
from pydantic import BaseModel, Field
|
|
|
|
from .connection import es_connection_details
|
|
from .prompts import CONDENSE_QUESTION_PROMPT, DOCUMENT_PROMPT, LLM_CONTEXT_PROMPT
|
|
|
|
# Setup connecting to Elasticsearch
|
|
vectorstore = ElasticsearchStore(
|
|
**es_connection_details,
|
|
embedding=HuggingFaceEmbeddings(
|
|
model_name="all-MiniLM-L6-v2", model_kwargs={"device": "cpu"}
|
|
),
|
|
index_name="workplace-search-example",
|
|
)
|
|
retriever = vectorstore.as_retriever()
|
|
|
|
# Set up LLM to user
|
|
llm = ChatOpenAI(temperature=0)
|
|
|
|
|
|
def _combine_documents(
|
|
docs, document_prompt=DOCUMENT_PROMPT, document_separator="\n\n"
|
|
):
|
|
doc_strings = [format_document(doc, document_prompt) for doc in docs]
|
|
return document_separator.join(doc_strings)
|
|
|
|
|
|
def _format_chat_history(chat_history: List[Tuple]) -> str:
|
|
buffer = ""
|
|
for dialogue_turn in chat_history:
|
|
human = "Human: " + dialogue_turn[0]
|
|
ai = "Assistant: " + dialogue_turn[1]
|
|
buffer += "\n" + "\n".join([human, ai])
|
|
return buffer
|
|
|
|
|
|
class ChainInput(BaseModel):
|
|
chat_history: Optional[List[BaseMessage]] = Field(
|
|
description="Previous chat messages."
|
|
)
|
|
question: str = Field(..., description="The question to answer.")
|
|
|
|
|
|
_inputs = RunnableParallel(
|
|
standalone_question=RunnablePassthrough.assign(
|
|
chat_history=lambda x: _format_chat_history(x["chat_history"])
|
|
)
|
|
| CONDENSE_QUESTION_PROMPT
|
|
| llm
|
|
| StrOutputParser(),
|
|
)
|
|
|
|
_context = {
|
|
"context": itemgetter("standalone_question") | retriever | _combine_documents,
|
|
"question": lambda x: x["standalone_question"],
|
|
}
|
|
|
|
chain = _inputs | _context | LLM_CONTEXT_PROMPT | llm | StrOutputParser()
|
|
|
|
chain = chain.with_types(input_type=ChainInput)
|