mirror of
https://github.com/MODSetter/SurfSense.git
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feat: gpt-researcher custom response.Now very close to perplexity.
This commit is contained in:
DESKTOP-RTLN3BA\$punk
parent
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commit
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5 changed files with 215 additions and 194 deletions
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@ -1,16 +1,35 @@
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#true if you wana run local setup with Ollama
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IS_LOCAL_SETUP = 'false'
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#Your Unstructured IO API Key. Use any value if running a local instance or file upload support isn't needed.
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#Your Unstructed IO API Key. Random value if you are using unstructured locally or dont want to upload files
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UNSTRUCTURED_API_KEY = ""
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#POSTGRES DB TO TRACK USERS
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POSTGRES_DATABASE_URL = "postgresql+psycopg2://postgres:postgres@localhost:5432/surfsense"
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# API KEY TO PREVENT USER REGISTRATION SPAM
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# API KEY TO VERIFY
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API_SECRET_KEY = "surfsense"
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# Your JWT secret and algorithm
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SECRET_KEY = "your_secret_key"
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ALGORITHM = "HS256"
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ACCESS_TOKEN_EXPIRE_MINUTES = 720
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ACCESS_TOKEN_EXPIRE_MINUTES = "720"
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# SEARCHE ENGINES TO USE - FUTURE FEATURE - LEAVE EMPTY FOR NOW
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TAVILY_API_KEY=""
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# UNCOMMENT THE RESPECTIVE BELOW LINES FOR LOCAL/OPENAI SETUP
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# For OpenAI LLM SETUP
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OPENAI_API_KEY="sk-proj-GHG....."
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FAST_LLM="openai:gpt-4o-mini"
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SMART_LLM="openai:gpt-4o-mini"
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EMBEDDING="openai:text-embedding-3-large"
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# For Local Setups
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# OPENAI_API_KEY="123"
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# OLLAMA_BASE_URL="http://localhost:11434"
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# FAST_LLM="ollama:qwen2.5:7b"
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# SMART_LLM="ollama:qwen2.5:7b"
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# EMBEDDING="ollama:qwen2.5:7b"
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# TEMPRATURE="0"
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@ -1,3 +1,7 @@
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import asyncio
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from datetime import datetime
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from typing import List
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from gpt_researcher import GPTResearcher
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from langchain_chroma import Chroma
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from langchain_ollama import OllamaLLM, OllamaEmbeddings
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from langchain_community.vectorstores.utils import filter_complex_metadata
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@ -14,12 +18,23 @@ from langchain_core.prompts import PromptTemplate
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import os
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from dotenv import load_dotenv
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from pydmodels import AIAnswer, Reference
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from database import SessionLocal
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from models import Documents, User
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from prompts import CONTEXT_ANSWER_PROMPT
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load_dotenv()
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IS_LOCAL_SETUP = os.environ.get("IS_LOCAL_SETUP")
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FAST_LLM = os.environ.get("FAST_LLM")
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EMBEDDING = os.environ.get("EMBEDDING")
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IS_LOCAL_SETUP = True if FAST_LLM.startswith("ollama") else False
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def extract_model_name(model_string: str) -> tuple[str, str]:
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part1, part2 = model_string.split(":", 1) # Split into two parts at the first colon
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return part2
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MODEL_NAME = extract_model_name(FAST_LLM)
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EMBEDDING_MODEL = extract_model_name(EMBEDDING)
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# Dependency
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def get_db():
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@ -35,12 +50,12 @@ class HIndices:
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"""
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"""
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self.username = username
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if(IS_LOCAL_SETUP == 'true'):
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self.llm = OllamaLLM(model="mistral-nemo",temperature=0)
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self.embeddings = OllamaEmbeddings(model="mistral-nemo")
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if(IS_LOCAL_SETUP == True):
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self.llm = OllamaLLM(model=MODEL_NAME,temperature=0)
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self.embeddings = OllamaEmbeddings(model=EMBEDDING_MODEL)
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else:
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self.llm = ChatOpenAI(temperature=0, model_name="gpt-4o-mini", api_key=api_key)
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self.embeddings = OpenAIEmbeddings(api_key=api_key)
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self.llm = ChatOpenAI(temperature=0, model_name=MODEL_NAME, api_key=api_key)
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self.embeddings = OpenAIEmbeddings(api_key=api_key,model=EMBEDDING_MODEL)
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self.summary_store = Chroma(
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collection_name="summary_store",
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@ -92,18 +107,9 @@ class HIndices:
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report_chain = report_prompt | self.llm
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if(IS_LOCAL_SETUP == 'true'):
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# Local LLMS suck at summaries so need this slow and painful procedure
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text_splitter = SemanticChunker(embeddings=self.embeddings)
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chunks = text_splitter.split_documents([doc])
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combined_summary = ""
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for i, chunk in enumerate(chunks):
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print("GENERATING SUMMARY FOR CHUNK "+ str(i))
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chunk_summary = report_chain.invoke({"document": chunk})
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combined_summary += "\n\n" + chunk_summary + "\n\n"
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response = combined_summary
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if(IS_LOCAL_SETUP == True):
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response = report_chain.invoke({"document": doc})
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metadict = {
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"page": page_no,
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@ -111,10 +117,10 @@ class HIndices:
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"search_space": search_space,
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}
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# metadict['languages'] = metadict['languages'][0]
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metadict.update(doc.metadata)
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# metadict['languages'] = metadict['languages'][0]
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return Document(
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id=str(page_no),
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page_content=response,
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@ -177,17 +183,8 @@ class HIndices:
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report_chain = report_prompt | self.llm
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if(IS_LOCAL_SETUP == 'true'):
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# Local LLMS suck at summaries so need this slow and painful procedure
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text_splitter = SemanticChunker(embeddings=self.embeddings)
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chunks = text_splitter.split_documents([doc])
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combined_summary = ""
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for i, chunk in enumerate(chunks):
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print("GENERATING SUMMARY FOR CHUNK "+ str(i))
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chunk_summary = report_chain.invoke({"document": chunk})
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combined_summary += "\n\n" + chunk_summary + "\n\n"
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response = combined_summary
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if(IS_LOCAL_SETUP == True):
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response = report_chain.invoke({"document": doc})
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return Document(
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id=str(page_no),
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@ -205,6 +202,7 @@ class HIndices:
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"VisitedWebPageVisitDurationInMilliseconds": doc.metadata['VisitedWebPageVisitDurationInMilliseconds'],
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}
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)
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else:
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response = report_chain.invoke({"document": doc})
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@ -230,19 +228,6 @@ class HIndices:
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Creates and Saves/Updates docs in hierarchical indices and postgres table
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"""
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# DocumentPgEntry = []
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# searchspace = db.query(SearchSpace).filter(SearchSpace.search_space == search_space).first()
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# for doc in documents:
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# pgdocmeta = stringify(doc.metadata)
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# if(searchspace):
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# DocumentPgEntry.append(Documents(file_type='WEBPAGE',title=doc.metadata.VisitedWebPageTitle,search_space=search_space, document_metadata=pgdocmeta, page_content=doc.page_content))
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# else:
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# DocumentPgEntry.append(Documents(file_type='WEBPAGE',title=doc.metadata.VisitedWebPageTitle,search_space=SearchSpace(search_space=search_space), document_metadata=pgdocmeta, page_content=doc.page_content))
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prev_doc_idx = len(documents) + 1
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# #Save docs in PG
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user = db.query(User).filter(User.username == self.username).first()
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@ -262,22 +247,20 @@ class HIndices:
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else:
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batch_summaries = [self.summarize_file_doc(page_no = i + summary_last_id, doc=doc, search_space=search_space) for i, doc in enumerate(documents)]
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# batch_summaries = [summarize_doc(i + summary_last_id, doc) for i, doc in enumerate(documents)]
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summaries.extend(batch_summaries)
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detailed_chunks = []
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for i, summary in enumerate(summaries):
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# Semantic chucking for better contexual comprression
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# Semantic chucking for better contexual compression
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text_splitter = SemanticChunker(embeddings=self.embeddings)
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chunks = text_splitter.split_documents([documents[i]])
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user.documents[-(len(summaries) - i)].desc_vector_start = detail_id_counter
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user.documents[-(len(summaries) - i)].desc_vector_end = detail_id_counter + len(chunks)
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# summary_entry = db.query(Documents).filter(Documents.id == int(user.documents[-1].id)).first()
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# summary_entry.desc_vector_start = detail_id_counter
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# summary_entry.desc_vector_end = detail_id_counter + len(chunks)
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db.commit()
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@ -290,6 +273,30 @@ class HIndices:
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"page": summary.metadata['page'],
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})
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if(files_type == 'WEBPAGE'):
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ieee_content = (
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f"=======================================DOCUMENT METADATA==================================== \n"
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f"Source: {chunk.metadata['VisitedWebPageURL']} \n"
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f"Title: {chunk.metadata['VisitedWebPageTitle']} \n"
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f"Visited Date and Time : {chunk.metadata['VisitedWebPageDateWithTimeInISOString']} \n"
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f"============================DOCUMENT PAGE CONTENT CHUNK===================================== \n"
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f"Page Content Chunk: \n\n{chunk.page_content}\n\n"
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f"===================================================================================== \n"
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)
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else:
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ieee_content = (
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f"=======================================DOCUMENT METADATA==================================== \n"
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f"Source: {chunk.metadata['filename']} \n"
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f"Title: {chunk.metadata['filename']} \n"
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f"Visited Date and Time : {datetime.now()} \n"
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f"============================DOCUMENT PAGE CONTENT CHUNK===================================== \n"
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f"Page Content Chunk: \n\n{chunk.page_content}\n\n"
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f"===================================================================================== \n"
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)
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chunk.page_content = ieee_content
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detail_id_counter += 1
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detailed_chunks.extend(chunks)
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@ -313,33 +320,67 @@ class HIndices:
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return "success"
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def is_query_answerable(self, query, context):
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prompt = PromptTemplate(
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template="""You are a grader assessing relevance of a retrieved document to a user question. \n
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Here is the retrieved document: \n\n {context} \n\n
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Here is the user question: {question} \n
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If the document contains keyword(s) or semantic meaning related to the user question, grade it as relevant. \n
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Give a binary score 'yes' or 'no' score to indicate whether the document is relevant to the question.
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Only return 'yes' or 'no'""",
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input_variables=["context", "question"],
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)
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def summary_vector_search(self,query, search_space='GENERAL'):
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top_summaries_compressor = FlashrankRerank(top_n=20)
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ans_chain = prompt | self.llm
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finalans = ans_chain.invoke({"question": query, "context": context})
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if(IS_LOCAL_SETUP == 'true'):
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return finalans
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else:
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return finalans.content
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def local_search(self, query, search_space='GENERAL'):
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top_summaries_compressor = FlashrankRerank(top_n=5)
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details_compressor = FlashrankRerank(top_n=30)
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top_summaries_retreiver = ContextualCompressionRetriever(
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base_compressor=top_summaries_compressor, base_retriever=self.summary_store.as_retriever(search_kwargs={'filter': {'search_space': search_space}})
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)
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return top_summaries_retreiver.invoke(query)
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def deduplicate_references_and_update_answer(self, answer: str, references: List[Reference]) -> tuple[str, List[Reference]]:
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"""
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Deduplicates references and updates the answer text to maintain correct reference numbering.
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Args:
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answer: The text containing reference citations
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references: List of Reference objects
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Returns:
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tuple: (updated_answer, deduplicated_references)
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"""
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# Track unique references and create ID mapping using a dictionary comprehension
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unique_refs = {}
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id_mapping = {
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ref.id: unique_refs.setdefault(
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ref.url, Reference(id=str(len(unique_refs) + 1), title=ref.title, url=ref.url)
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).id
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for ref in references
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}
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# Apply new mappings to the answer text
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updated_answer = answer
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for old_id, new_id in sorted(id_mapping.items(), key=lambda x: len(x[0]), reverse=True):
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updated_answer = updated_answer.replace(f'[{old_id}]', f'[{new_id}]')
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return updated_answer, list(unique_refs.values())
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async def get_vectorstore_report(self, query: str, report_type: str, report_source: str, documents: List[Document]) -> str:
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researcher = GPTResearcher(query=query, report_type=report_type, report_source=report_source, documents=documents, report_format="IEEE")
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await researcher.conduct_research()
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report = await researcher.write_report()
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return report
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async def get_web_report(self, query: str, report_type: str, report_source: str) -> str:
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researcher = GPTResearcher(query=query, report_type=report_type, report_source=report_source, report_format="IEEE")
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await researcher.conduct_research()
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report = await researcher.write_report()
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return report
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def new_search(self, query, search_space='GENERAL'):
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report_type = "custom_report"
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report_source = "langchain_documents"
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contextdocs = []
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top_summaries_compressor = FlashrankRerank(top_n=5)
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details_compressor = FlashrankRerank(top_n=50)
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top_summaries_retreiver = ContextualCompressionRetriever(
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base_compressor=top_summaries_compressor, base_retriever=self.summary_store.as_retriever(search_kwargs={'filter': {'search_space': search_space}})#
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)
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top_summaries_compressed_docs = top_summaries_retreiver.invoke(query)
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for summary in top_summaries_compressed_docs:
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@ -354,61 +395,40 @@ class HIndices:
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query
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)
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contextdocs = top_summaries_compressed_docs + detailed_compressed_docs
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contextdocs.extend(detailed_compressed_docs)
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context_to_answer = ""
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for i, doc in enumerate(contextdocs):
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content = f":DOCUMENT {str(i)}\n"
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content += f"=======================================METADATA==================================== \n"
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content += f"{doc.metadata} \n"
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content += f"===================================================================================== \n"
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content += f"DOCUMENT CONTENT: \n\n {doc.page_content} \n\n"
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content += f"===================================================================================== \n"
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custom_prompt = """
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Please answer the following user query in the format shown below, using in-text citations and IEEE-style references based on the provided documents.
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USER QUERY : """+ query +"""
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context_to_answer += content
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Ensure the answer includes:
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- A detailed yet concise explanation with IEEE-style in-text citations (e.g., [1], [2]).
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- A list of non-duplicated sources at the end, following IEEE format. Hyperlink each source using: [Website Name](URL).
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- Where applicable, provide sources in the text to back up key points.
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content = ""
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Ensure your response is structured something like this (here user query : Explain the impact of artificial intelligence on modern healthcare.):
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---
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**Answer:**
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Artificial intelligence (AI) has significantly transformed modern healthcare by enhancing diagnostic accuracy, personalizing patient care, and optimizing operational efficiency. AI algorithms can analyze vast datasets to identify patterns that may be missed by human practitioners, leading to improved diagnostic outcomes [1]. For instance, AI systems have been deployed in radiology to detect anomalies in medical imaging with high precision [2]. Moreover, AI-driven tools facilitate personalized treatment plans by considering individual patient data, thereby improving patient outcomes [3].
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if(self.is_query_answerable(query=query, context=context_to_answer).lower() == 'yes'):
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ans_chain = CONTEXT_ANSWER_PROMPT | self.llm
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**References:**
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1. (2024, October 23). [Highly Effective Prompt for Summarizing — GPT-4 Optimized: r/ChatGPT.](https://www.reddit.com/r/ChatGPT/comments/13na8yp/highly_effective_prompt_for_summarizing_gpt4/)
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2. (2024, October 23). [MODSetter/SurfSense: Personal AI Assistant for Internet Surfers and Researchers.](https://github.com/MODSetter/SurfSense)
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3. filename.pdf
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finalans = ans_chain.invoke({"query": query, "context": context_to_answer})
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---
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if(IS_LOCAL_SETUP == 'true'):
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return finalans
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else:
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return finalans.content
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else:
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continue
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"""
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return "I couldn't find any answer"
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local_report = asyncio.run(self.get_vectorstore_report(query=custom_prompt, report_type=report_type, report_source=report_source, documents=contextdocs))
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def global_search(self,query, search_space='GENERAL'):
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top_summaries_compressor = FlashrankRerank(top_n=20)
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# web_report = asyncio.run(get_web_report(query=custom_prompt, report_type=report_type, report_source="web"))
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top_summaries_retreiver = ContextualCompressionRetriever(
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base_compressor=top_summaries_compressor, base_retriever=self.summary_store.as_retriever(search_kwargs={'filter': {'search_space': search_space}})
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)
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# structured_llm = self.llm.with_structured_output(AIAnswer)
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top_summaries_compressed_docs = top_summaries_retreiver.invoke(query)
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# out = structured_llm.invoke("Extract exact(i.e without changing) answer string and references information from : \n\n\n" + local_report)
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context_to_answer = ""
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for i, doc in enumerate(top_summaries_compressed_docs):
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content = f":DOCUMENT {str(i)}\n"
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content += f"=======================================METADATA==================================== \n"
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content += f"{doc.metadata} \n"
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content += f"===================================================================================== \n"
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content += f"DOCUMENT CONTENT: \n\n {doc.page_content} \n\n"
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content += f"===================================================================================== \n"
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# mod_out = self.deduplicate_references_and_update_answer(answer=out.answer, references=out.references)
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context_to_answer += content
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ans_chain = CONTEXT_ANSWER_PROMPT | self.llm
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finalans = ans_chain.invoke({"query": query, "context": context_to_answer})
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if(IS_LOCAL_SETUP == 'true'):
|
||||
return finalans, top_summaries_compressed_docs
|
||||
else:
|
||||
return finalans.content, top_summaries_compressed_docs
|
||||
return local_report
|
||||
|
||||
|
|
@ -23,6 +23,19 @@ class DocMeta(BaseModel):
|
|||
# VisitedWebPageVisitDurationInMilliseconds: Optional[int] = Field(default=None, description="VisitedWebPageVisitDurationInMilliseconds of Document"),
|
||||
# VisitedWebPageContent: Optional[str] = Field(default=None, description="Visited WebPage Content in markdown of Document")
|
||||
|
||||
|
||||
|
||||
class Reference(BaseModel):
|
||||
id: str = Field(..., description="reference no")
|
||||
title: str = Field(..., description="reference title")
|
||||
url: str = Field(..., description="reference url")
|
||||
|
||||
|
||||
class AIAnswer(BaseModel):
|
||||
answer: str = Field(..., description="Given Answer including its intext citation no's like [1], [2] etc.")
|
||||
references: List[Reference] = Field(..., description="References")
|
||||
|
||||
|
||||
class DocWithContent(BaseModel):
|
||||
DocMetadata: Optional[str] = Field(default=None, description="Document Metadata")
|
||||
Content: Optional[str] = Field(default=None, description="Document Page Content")
|
||||
|
|
|
|||
|
|
@ -19,3 +19,9 @@ flashrank
|
|||
psycopg2
|
||||
unstructured-client
|
||||
langchain-unstructured
|
||||
langgraph
|
||||
gpt_researcher
|
||||
langgraph-cli
|
||||
weasyprint
|
||||
json5
|
||||
loguru
|
||||
|
|
@ -5,14 +5,13 @@ from langchain_core.documents import Document
|
|||
from langchain_ollama import OllamaLLM
|
||||
from langchain_openai import ChatOpenAI
|
||||
from sqlalchemy import insert
|
||||
from prompts import CONTEXT_ANSWER_PROMPT, DATE_TODAY, SUBQUERY_DECOMPOSITION_PROMT
|
||||
from prompts import DATE_TODAY
|
||||
from pydmodels import ChatToUpdate, DescriptionResponse, DocWithContent, DocumentsToDelete, NewUserChat, UserCreate, UserQuery, RetrivedDocList, UserQueryResponse, UserQueryWithChatHistory
|
||||
from langchain_core.messages import HumanMessage, SystemMessage, AIMessage
|
||||
from langchain_unstructured import UnstructuredLoader
|
||||
|
||||
#Heirerical Indices class
|
||||
from HIndices import HIndices
|
||||
|
||||
from Utils.stringify import stringify
|
||||
|
||||
# Auth Libs
|
||||
|
|
@ -31,13 +30,20 @@ import os
|
|||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
|
||||
IS_LOCAL_SETUP = os.environ.get("IS_LOCAL_SETUP")
|
||||
FAST_LLM = os.environ.get("FAST_LLM")
|
||||
IS_LOCAL_SETUP = True if FAST_LLM.startswith("ollama") else False
|
||||
ACCESS_TOKEN_EXPIRE_MINUTES = int(os.environ.get("ACCESS_TOKEN_EXPIRE_MINUTES"))
|
||||
ALGORITHM = os.environ.get("ALGORITHM")
|
||||
API_SECRET_KEY = os.environ.get("API_SECRET_KEY")
|
||||
SECRET_KEY = os.environ.get("SECRET_KEY")
|
||||
UNSTRUCTURED_API_KEY = os.environ.get("UNSTRUCTURED_API_KEY")
|
||||
|
||||
def extract_model_name(model_string: str) -> tuple[str, str]:
|
||||
part1, part2 = model_string.split(":", 1) # Split into two parts at the first colon
|
||||
return part2
|
||||
|
||||
MODEL_NAME = extract_model_name(FAST_LLM)
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
# Dependency
|
||||
|
|
@ -71,6 +77,7 @@ async def upload_files(files: list[UploadFile], token: str = Depends(oauth2_sche
|
|||
chunking_strategy="basic",
|
||||
max_characters=90000,
|
||||
include_orig_elements=False,
|
||||
strategy="fast",
|
||||
)
|
||||
|
||||
filedocs = loader.load()
|
||||
|
|
@ -117,7 +124,7 @@ async def upload_files(files: list[UploadFile], token: str = Depends(oauth2_sche
|
|||
db.commit()
|
||||
|
||||
# Create hierarchical indices
|
||||
if IS_LOCAL_SETUP == 'true':
|
||||
if IS_LOCAL_SETUP == True:
|
||||
index = HIndices(username=username)
|
||||
else:
|
||||
index = HIndices(username=username, api_key=api_key)
|
||||
|
|
@ -145,60 +152,21 @@ def get_user_query_response(data: UserQuery, response_model=UserQueryResponse):
|
|||
query = data.query
|
||||
search_space = data.search_space
|
||||
|
||||
if(IS_LOCAL_SETUP == 'true'):
|
||||
sub_query_llm = OllamaLLM(model="mistral-nemo",temperature=0)
|
||||
qa_llm = OllamaLLM(model="mistral-nemo",temperature=0)
|
||||
else:
|
||||
sub_query_llm = ChatOpenAI(temperature=0, model_name="gpt-4o-mini", api_key=data.openaikey)
|
||||
qa_llm = ChatOpenAI(temperature=0.5, model_name="gpt-4o-mini", api_key=data.openaikey)
|
||||
|
||||
|
||||
|
||||
# Create an LLMChain for sub-query decomposition
|
||||
subquery_decomposer_chain = SUBQUERY_DECOMPOSITION_PROMT | sub_query_llm
|
||||
|
||||
#Experimental
|
||||
def decompose_query(original_query: str):
|
||||
"""
|
||||
Decompose the original query into simpler sub-queries.
|
||||
|
||||
Args:
|
||||
original_query (str): The original complex query
|
||||
|
||||
Returns:
|
||||
List[str]: A list of simpler sub-queries
|
||||
"""
|
||||
if(IS_LOCAL_SETUP == 'true'):
|
||||
response = subquery_decomposer_chain.invoke(original_query)
|
||||
else:
|
||||
response = subquery_decomposer_chain.invoke(original_query).content
|
||||
|
||||
sub_queries = [q.strip() for q in response.split('\n') if q.strip() and not q.strip().startswith('Sub-queries:')]
|
||||
return sub_queries
|
||||
|
||||
|
||||
# Create Heirarical Indecices
|
||||
if(IS_LOCAL_SETUP == 'true'):
|
||||
if(IS_LOCAL_SETUP == True):
|
||||
index = HIndices(username=username)
|
||||
else:
|
||||
index = HIndices(username=username,api_key=data.openaikey)
|
||||
|
||||
|
||||
|
||||
# For Those Who Want HyDe Questions
|
||||
# sub_queries = decompose_query(query)
|
||||
|
||||
#Implement HyDe over it if you crazy
|
||||
sub_queries = []
|
||||
sub_queries.append(query)
|
||||
|
||||
duplicate_related_summary_docs = []
|
||||
context_to_answer = ""
|
||||
for sub_query in sub_queries:
|
||||
localreturn = index.local_search(query=sub_query, search_space=search_space)
|
||||
globalreturn, related_summary_docs = index.global_search(query=sub_query, search_space=search_space)
|
||||
|
||||
context_to_answer += localreturn + "\n\n" + globalreturn
|
||||
|
||||
# I know this is not the best way to do it, but I am too lazy to change it now
|
||||
related_summary_docs = index.summary_vector_search(query=sub_query, search_space=search_space)
|
||||
duplicate_related_summary_docs.extend(related_summary_docs)
|
||||
|
||||
|
||||
|
|
@ -223,15 +191,10 @@ def get_user_query_response(data: UserQuery, response_model=UserQueryResponse):
|
|||
returnDocs.append(entry)
|
||||
|
||||
|
||||
ans_chain = CONTEXT_ANSWER_PROMPT | qa_llm
|
||||
|
||||
finalans = ans_chain.invoke({"query": query, "context": context_to_answer})
|
||||
finalans = index.new_search(query=query, search_space=search_space)
|
||||
|
||||
|
||||
if(IS_LOCAL_SETUP == 'true'):
|
||||
return UserQueryResponse(response=finalans, relateddocs=returnDocs)
|
||||
else:
|
||||
return UserQueryResponse(response=finalans.content, relateddocs=returnDocs)
|
||||
return UserQueryResponse(response=finalans, relateddocs=returnDocs)
|
||||
|
||||
|
||||
except JWTError:
|
||||
|
|
@ -310,7 +273,7 @@ def save_data(apires: RetrivedDocList, db: Session = Depends(get_db)):
|
|||
db.commit()
|
||||
|
||||
# Create hierarchical indices
|
||||
if IS_LOCAL_SETUP == 'true':
|
||||
if IS_LOCAL_SETUP == True:
|
||||
index = HIndices(username=username)
|
||||
else:
|
||||
index = HIndices(username=username, api_key=apires.openaikey)
|
||||
|
|
@ -336,10 +299,10 @@ def doc_chat_with_history(data: UserQueryWithChatHistory, response_model=Descrip
|
|||
if username is None:
|
||||
raise HTTPException(status_code=403, detail="Token is invalid or expired")
|
||||
|
||||
if(IS_LOCAL_SETUP == 'true'):
|
||||
llm = OllamaLLM(model="mistral-nemo",temperature=0)
|
||||
if(IS_LOCAL_SETUP == True):
|
||||
llm = OllamaLLM(model=MODEL_NAME,temperature=0)
|
||||
else:
|
||||
llm = ChatOpenAI(temperature=0, model_name="gpt-4o-mini", api_key=data.openaikey)
|
||||
llm = ChatOpenAI(temperature=0, model_name=MODEL_NAME, api_key=data.openaikey)
|
||||
|
||||
chatHistory = []
|
||||
|
||||
|
|
@ -365,7 +328,7 @@ def doc_chat_with_history(data: UserQueryWithChatHistory, response_model=Descrip
|
|||
|
||||
response = descriptionchain.invoke({"input": data.query})
|
||||
|
||||
if(IS_LOCAL_SETUP == 'true'):
|
||||
if(IS_LOCAL_SETUP == True):
|
||||
return DescriptionResponse(response=response)
|
||||
else:
|
||||
return DescriptionResponse(response=response.content)
|
||||
|
|
@ -384,7 +347,7 @@ def delete_all_related_data(data: DocumentsToDelete, db: Session = Depends(get_d
|
|||
if username is None:
|
||||
raise HTTPException(status_code=403, detail="Token is invalid or expired")
|
||||
|
||||
if(IS_LOCAL_SETUP == 'true'):
|
||||
if(IS_LOCAL_SETUP == True):
|
||||
index = HIndices(username=username)
|
||||
else:
|
||||
index = HIndices(username=username,api_key=data.openaikey)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue