switching between llms

application/app.py

@@ -1,56 +1,68 @@
 import os
-import pickle
-import dotenv
-import datetime
-from flask import Flask, request, render_template
-# os.environ["LANGCHAIN_HANDLER"] = "langchain"
-import faiss
 
+import dotenv
+import requests
+from flask import Flask, request, render_template
 from langchain import FAISS
 from langchain import OpenAI, VectorDBQA, HuggingFaceHub, Cohere
 from langchain.chains.question_answering import load_qa_chain
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceHubEmbeddings, CohereEmbeddings, HuggingFaceInstructEmbeddings
 from langchain.prompts import PromptTemplate
-import requests
-from langchain.embeddings import OpenAIEmbeddings
 
-# from manifest import Manifest
-# from langchain.llms.manifest import ManifestWrapper
+# os.environ["LANGCHAIN_HANDLER"] = "langchain"
 
-# manifest = Manifest(
-#     client_name = "huggingface",
-#     client_connection = "http://127.0.0.1:5000"
-# )
+if os.getenv("LLM_NAME") is not None:
+    llm_choice = os.getenv("LLM_NAME")
+else:
+    llm_choice = "openai"
 
+if os.getenv("EMBEDDINGS_NAME") is not None:
+    embeddings_choice = os.getenv("EMBEDDINGS_NAME")
+else:
+    embeddings_choice = "openai_text-embedding-ada-002"
+
+
+
+if llm_choice == "manifest":
+    from manifest import Manifest
+    from langchain.llms.manifest import ManifestWrapper
+
+    manifest = Manifest(
+        client_name="huggingface",
+        client_connection="http://127.0.0.1:5000"
+    )
 
 # Redirect PosixPath to WindowsPath on Windows
 import platform
+
 if platform.system() == "Windows":
     import pathlib
+
     temp = pathlib.PosixPath
     pathlib.PosixPath = pathlib.WindowsPath
 
 # loading the .env file
 dotenv.load_dotenv()
 
-
 with open("combine_prompt.txt", "r") as f:
     template = f.read()
 
-# check if OPENAI_API_KEY is set
-if os.getenv("OPENAI_API_KEY") is not None:
+if os.getenv("API_KEY") is not None:
     api_key_set = True
-
 else:
     api_key_set = False
-
-
+if os.getenv("EMBEDDINGS_KEY") is not None:
+    embeddings_key_set = True
+else:
+    embeddings_key_set = False
 
 app = Flask(__name__)
 
 
 @app.route("/")
 def home():
-    return render_template("index.html", api_key_set=api_key_set)
+    return render_template("index.html", api_key_set=api_key_set, llm_choice=llm_choice,
+                           embeddings_choice=embeddings_choice)
 
 
 @app.route("/api/answer", methods=["POST"])
@@ -60,7 +72,14 @@ def api_answer():
     if not api_key_set:
         api_key = data["api_key"]
     else:
-        api_key = os.getenv("OPENAI_API_KEY")
+        api_key = os.getenv("API_KEY")
+    if not embeddings_key_set:
+        embeddings_key = data["embeddings_key"]
+    else:
+        embeddings_key = os.getenv("EMBEDDINGS_KEY")
+
+    print(embeddings_key)
+    print(api_key)
 
     # check if the vectorstore is set
     if "active_docs" in data:
@@ -70,24 +89,32 @@ def api_answer():
     else:
         vectorstore = ""
 
-
     # loading the index and the store and the prompt template
-    docsearch = FAISS.load_local(vectorstore, OpenAIEmbeddings(openai_api_key=api_key))
+    # Note if you have used other embeddings than OpenAI, you need to change the embeddings
+    if embeddings_choice == "openai_text-embedding-ada-002":
+        docsearch = FAISS.load_local(vectorstore, OpenAIEmbeddings(openai_api_key=embeddings_key))
+    elif embeddings_choice == "huggingface_sentence-transformers/all-mpnet-base-v2":
+        docsearch = FAISS.load_local(vectorstore, HuggingFaceHubEmbeddings())
+    elif embeddings_choice == "huggingface_hkunlp/instructor-large":
+        docsearch = FAISS.load_local(vectorstore, HuggingFaceInstructEmbeddings())
+    elif embeddings_choice == "cohere_medium":
+        docsearch = FAISS.load_local(vectorstore, CohereEmbeddings(cohere_api_key=embeddings_key))
 
     # create a prompt template
     c_prompt = PromptTemplate(input_variables=["summaries", "question"], template=template)
-    # create a chain with the prompt template and the store
 
+    if llm_choice == "openai":
+        llm = OpenAI(openai_api_key=api_key, temperature=0)
+    elif llm_choice == "manifest":
+        llm = ManifestWrapper(client=manifest, llm_kwargs={"temperature": 0.001, "max_tokens": 2048})
+    elif llm_choice == "huggingface":
+        llm = HuggingFaceHub(repo_id="bigscience/bloom", huggingfacehub_api_token=api_key)
+    elif llm_choice == "cohere":
+        llm = Cohere(model="command-xlarge-nightly", cohere_api_key=api_key)
 
-    #llm = ManifestWrapper(client=manifest, llm_kwargs={"temperature": 0.001, "max_tokens": 2048})
-    llm = OpenAI(openai_api_key=api_key, temperature=0)
-    #llm = HuggingFaceHub(repo_id="bigscience/bloom", huggingfacehub_api_token=api_key)
-    # llm = Cohere(model="command-xlarge-nightly", cohere_api_key=api_key)
-
-    qa_chain = load_qa_chain(llm = llm, chain_type="map_reduce",
+    qa_chain = load_qa_chain(llm=llm, chain_type="map_reduce",
                              combine_prompt=c_prompt)
 
-
     chain = VectorDBQA(combine_documents_chain=qa_chain, vectorstore=docsearch, k=2)
 
     # fetch the answer
@@ -105,6 +132,7 @@ def api_answer():
     # }
     return result
 
+
 @app.route("/api/docs_check", methods=["POST"])
 def check_docs():
     # check if docs exist in a vectorstore folder
@@ -128,7 +156,8 @@ def check_docs():
         with open(vectorstore + "faiss_store.pkl", "wb") as f:
             f.write(r.content)
 
-        return {"status":  'loaded'}
+        return {"status": 'loaded'}
+
 
 # handling CORS
 @app.after_request