Spaces:
Sleeping
Sleeping
File size: 3,431 Bytes
a4522e9 210160b a4522e9 210160b a4522e9 210160b a4522e9 210160b a4522e9 210160b a4522e9 210160b 5123c13 cf7e13a 5123c13 a4522e9 210160b a4522e9 210160b a4522e9 210160b a4522e9 210160b a4522e9 210160b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 |
try:
from langchain_community.vectorstores import Chroma
except:
from langchain_community.vectorstores import Chroma
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import ConversationBufferWindowMemory
# Import the necessary libraries.
from langchain_core.prompts import ChatPromptTemplate
from langchain_groq import ChatGroq
# Initialize a ChatGroq object with a temperature of 0 and the "mixtral-8x7b-32768" model.
llm = ChatGroq(temperature=0, model_name="llama3-70b-8192",api_key='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')
from langchain_community.embeddings import SentenceTransformerEmbeddings
embeddings = SentenceTransformerEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2", model_kwargs={"trust_remote_code":True})
memory = ConversationBufferWindowMemory(
memory_key="history", k=3, return_only_outputs=True
)
query_text="what did alice say to rabbit"
# Prepare the DB.
#embedding_function = OpenAIEmbeddings() # main
CHROMA_PATH = "chroma8"
# call the chroma generated in a directory
db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embeddings)
# Search the DB for similar documents to the query.
results = db.similarity_search_with_relevance_scores(query_text, k=2)
if len(results) == 0 or results[0][1] < 0.5:
print(f"Unable to find matching results.")
from langchain import PromptTemplate
query_text = "when did alice see mad hatter"
results = db.similarity_search_with_relevance_scores(query_text, k=3)
if len(results) == 0 or results[0][1] < 0.5:
print(f"Unable to find matching results.")
context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results ])
template = """
The following is a conversation between a human an AI. Answer question based only on the conversation.
Current conversation:
{history}
"""
s="""
\n question: {input}
\n answer:""".strip()
prompt = PromptTemplate(input_variables=["history", "input"], template=template+context_text+'\n'+s)
chain = ConversationChain(
llm=llm,
prompt=prompt,
memory=memory,
verbose=True,
)
# Generate a response from the Llama model
def get_llama_response(message: str, history: list) -> str:
"""
Generates a conversational response from the Llama model.
Parameters:
message (str): User's input message.
history (list): Past conversation history.
Returns:
str: Generated response from the Llama model.
"""
query_text =message
results = db.similarity_search_with_relevance_scores(query_text, k=2)
if len(results) == 0 or results[0][1] < 0.5:
print(f"Unable to find matching results.")
context_text = "\n\n---\n\n".join([doc.page_content for doc, _score in results ])
template = """
The following is a conversation between a human an AI. Answer question based only on the conversation.
Current conversation:
{history}
"""
s="""
\n question: {input}
\n answer:""".strip()
prompt = PromptTemplate(input_variables=["history", "input"], template=template+context_text+'\n'+s)
#print(template)
chain.prompt=prompt
res = chain.predict(input=query_text)
return res
#return response.strip()
import gradio as gr
iface = gr.Interface(fn=get_llama_response, inputs=gr.Textbox(),
outputs="textbox")
iface.launch(share=True)
|