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map_reduce

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Alex
2023-03-30 12:44:25 +01:00
parent ab46ba521f
commit b6dfb2c856
4 changed files with 42 additions and 26 deletions
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40
application/app.py
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@@ -10,7 +10,8 @@ from celery.result import AsyncResult
from flask import Flask, request, render_template, send_from_directory, jsonify
from langchain import FAISS
from langchain import VectorDBQA, HuggingFaceHub, Cohere, OpenAI
from langchain.chains import ChatVectorDBChain
from langchain.chains import LLMChain, ConversationalRetrievalChain
from langchain.chains.conversational_retrieval.prompts import CONDENSE_QUESTION_PROMPT
from langchain.chains.question_answering import load_qa_chain
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceHubEmbeddings, CohereEmbeddings, \
@@ -188,14 +189,15 @@ def api_answer():
llm = Cohere(model="command-xlarge-nightly", cohere_api_key=api_key)
if llm_choice == "openai_chat":
chain = ChatVectorDBChain.from_llm(
llm=llm,
vectorstore=docsearch,
#prompt=p_chat_combine,
qa_prompt=p_chat_combine,
top_k_docs_for_context=3,
return_source_documents=False)
result = chain({"question": question, "chat_history": []})
question_generator = LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT)
doc_chain = load_qa_chain(llm, chain_type="map_reduce", combine_prompt=p_chat_combine)
chain = ConversationalRetrievalChain(
retriever=docsearch.as_retriever(k=2),
question_generator=question_generator,
combine_docs_chain=doc_chain,
)
chat_history = []
result = chain({"question": question, "chat_history": chat_history})
else:
qa_chain = load_qa_chain(llm=llm, chain_type="map_reduce",
combine_prompt=c_prompt, question_prompt=q_prompt)
@@ -289,16 +291,16 @@ def combined_json():
# get json from https://d3dg1063dc54p9.cloudfront.net/combined.json
data = [{
"name": 'default',
"language": 'default',
"version": '',
"description": 'default',
"fullName": 'default',
"date": 'default',
"docLink": 'default',
"model": embeddings_choice,
"location": "local"
}]
"name": 'default',
"language": 'default',
"version": '',
"description": 'default',
"fullName": 'default',
"date": 'default',
"docLink": 'default',
"model": embeddings_choice,
"location": "local"
}]
# structure: name, language, version, description, fullName, date, docLink
# append data from vectors_collection
for index in vectors_collection.find({'user': user}):