Update src/utils/text_qa.py

src/utils/text_qa.py

@@ -1,91 +1,91 @@
-import os
-from typing import List
-from langchain_groq import ChatGroq
-from langchain.prompts import PromptTemplate
-from langchain_community.vectorstores.qdrant import Qdrant
-from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
-from qdrant_client import QdrantClient
-#from langchain_community.chat_models import ChatOllama
-
-
-#import chainlit as cl
-from langchain.chains import RetrievalQA
-
-# bring in our GROQ_API_KEY
-from dotenv import load_dotenv
-load_dotenv()
-
-
-llamaparse_api_key = os.getenv("LLAMA_CLOUD_API_KEY")
-qdrant_url = os.getenv("QDRANT_URL ")
-qdrant_api_key = os.getenv("QDRANT_API_KEY")
-groq_api_key = os.getenv("GROQ_API_KEY")
-
-custom_prompt_template = """Use the following pieces of information to answer the user's question.
-If you don't know the answer, just say that you don't know,if it is out of context say that it is out of context and also try to provide the answer and don't be rude.
-
-Context: {context}
-Question: {question}
-
-Only return the helpful answer below and nothing else.
-Helpful answer:
-"""
-
-def set_custom_prompt():
-    """
-    Prompt template for QA retrieval for each vectorstore
-    """
-    prompt = PromptTemplate(template=custom_prompt_template,
-                            input_variables=['context', 'question'])
-    return prompt
-
-
-chat_model = ChatGroq(temperature=0, model_name="mixtral-8x7b-32768",api_key=groq_api_key) # type: ignore
-#chat_model = ChatGroq(temperature=0, model_name="Llama2-70b-4096")
-#chat_model = ChatOllama(model="llama2", request_timeout=30.0)
-
-#client = QdrantClient(api_key=qdrant_api_key, url=qdrant_url,)
-
-
-def retrieval_qa_chain(llm, prompt, vectorstore):
-    qa_chain = RetrievalQA.from_chain_type(
-        llm=llm,
-        chain_type="stuff",
-        retriever=vectorstore.as_retriever(search_kwargs={'k': 2}),
-        return_source_documents=True,
-        chain_type_kwargs={'prompt': prompt}
-    )
-    return qa_chain
-
-
-def qa_bot(qdrant):
-    embeddings = FastEmbedEmbeddings() # type: ignore
-    vectorstore = qdrant
-    llm = chat_model
-    qa_prompt=set_custom_prompt()
-    qa = retrieval_qa_chain(llm, qa_prompt, vectorstore)
-    return qa
-
-#---------------------------------------------------------------------#
- 
-#qdrant_cloud_api_key="your_qdrant_cloud_api_key"
-#qdrant_url="your_qdrant_url"
-
-#qdrant_cloud = Qdrant.from_documents(
-#    docs,
-#   embeddings,
-#   url=qdrant_url,
-#   prefer_grpc=True,
-#   api_key=qdrant_cloud_api_key,
-#  collection_name="qdrant_cloud_documents",
-#)
-
-#---------------------------------------------------------------------#
-#query="how to make coffee"
-#print(query)
-
-#chain = qa_bot()
-#response = chain.invoke(query,)
-
-#print(response['result'])
-
+import os
+from typing import List
+from langchain_groq import ChatGroq
+from langchain.prompts import PromptTemplate
+from langchain_community.vectorstores.qdrant import Qdrant
+from langchain_community.embeddings.fastembed import FastEmbedEmbeddings
+from qdrant_client import QdrantClient
+#from langchain_community.chat_models import ChatOllama
+
+
+#import chainlit as cl
+from langchain.chains import RetrievalQA
+
+# bring in our GROQ_API_KEY
+from dotenv import load_dotenv
+load_dotenv()
+
+
+llamaparse_api_key = os.getenv("LLAMA_CLOUD_API_KEY")
+qdrant_url = os.getenv("QDRANT_URL ")
+qdrant_api_key = os.getenv("QDRANT_API_KEY")
+groq_api_key = os.getenv("GROQ_API_KEY")
+
+custom_prompt_template = """Use the following pieces of information to answer the user's question.
+If you don't know the answer, just say that you don't know,if it is out of context say that it is out of context and also try to provide the answer and don't be rude.
+
+Context: {context}
+Question: {question}
+
+Only return the helpful answer below and nothing else.
+Helpful answer:
+"""
+
+def set_custom_prompt():
+    """
+    Prompt template for QA retrieval for each vectorstore
+    """
+    prompt = PromptTemplate(template=custom_prompt_template,
+                            input_variables=['context', 'question'])
+    return prompt
+
+
+chat_model = ChatGroq(temperature=0, model_name="mixtral-8x7b-32768",api_key=groq_api_key) # type: ignore
+#chat_model = ChatGroq(temperature=0, model_name="Llama2-70b-4096")
+#chat_model = ChatOllama(model="llama2", request_timeout=30.0)
+
+#client = QdrantClient(api_key=qdrant_api_key, url=qdrant_url,)
+
+
+def retrieval_qa_chain(llm, prompt, vectorstore):
+    qa_chain = RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=vectorstore.as_retriever(search_kwargs={'k': 2}),
+        return_source_documents=True,
+        chain_type_kwargs={'prompt': prompt}
+    )
+    return qa_chain
+
+
+def qa_bot(qdrant):
+    embeddings = FastEmbedEmbeddings() # type: ignore
+    vectorstore = qdrant
+    llm = chat_model
+    qa_prompt=set_custom_prompt()
+    qa = retrieval_qa_chain(llm, qa_prompt, vectorstore)
+    return qa
+
+#---------------------------------------------------------------------#
+ 
+#qdrant_cloud_api_key="your_qdrant_cloud_api_key"
+#qdrant_url="your_qdrant_url"
+
+#qdrant_cloud = Qdrant.from_documents(
+#    docs,
+#   embeddings,
+#   url=qdrant_url,
+#   prefer_grpc=True,
+#   api_key=qdrant_cloud_api_key,
+#  collection_name="qdrant_cloud_documents",
+#)
+
+#---------------------------------------------------------------------#
+#query="how to make coffee"
+#print(query)
+
+chain = qa_bot()
+#response = chain.invoke(query,)
+
+#print(response['result'])
+