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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+pet.pdf filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,108 @@
+from langchain import PromptTemplate, LLMChain
+from langchain.llms import CTransformers
+import os
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.chains import RetrievalQA
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from io import BytesIO
+from langchain.document_loaders import PyPDFLoader
+import gradio as gr
+
+
+local_llm = "TheBloke/zephyr-7B-beta-GGUF"
+model_file = "zephyr-7b-beta.Q4_0.gguf"
+
+config = {
+'max_new_tokens': 1024,
+'repetition_penalty': 1.1,
+'temperature': 0.1,
+'top_k': 50,
+'top_p': 0.9,
+'stream': True,
+'threads': int(os.cpu_count() / 2)
+}
+
+llm = CTransformers(
+    model=local_llm,
+    model_file=model_file,
+    model_type="mistral",
+    lib="avx2", #for CPU use
+    **config
+)
+
+print("LLM Initialized...")
+
+
+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, don't try to make up an answer.
+
+Context: {context}
+Question: {question}
+
+Only return the helpful answer below and nothing else.
+Helpful answer:
+"""
+
+model_name = "BAAI/bge-large-en"
+model_kwargs = {'device': 'cpu'}
+encode_kwargs = {'normalize_embeddings': False}
+embeddings = HuggingFaceBgeEmbeddings(
+    model_name=model_name,
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs
+)
+
+
+prompt = PromptTemplate(template=prompt_template, input_variables=['context', 'question'])
+load_vector_store = Chroma(persist_directory="stores/pet_cosine", embedding_function=embeddings)
+retriever = load_vector_store.as_retriever(search_kwargs={"k":1})
+# query = "what is the fastest speed for a greyhound dog?"
+# semantic_search = retriever.get_relevant_documents(query)
+# print(semantic_search)
+
+print("######################################################################")
+
+chain_type_kwargs = {"prompt": prompt}
+
+# qa = RetrievalQA.from_chain_type(
+#     llm=llm,
+#     chain_type="stuff",
+#     retriever=retriever,
+#     return_source_documents = True,
+#     chain_type_kwargs= chain_type_kwargs,
+#     verbose=True
+# )
+
+# response = qa(query)
+
+# print(response)
+
+sample_prompts = ["what is the fastest speed for a greyhound dog?", "Why should we not feed chocolates to the dogs?", "Name two factors which might contribute to why some dogs might get scared?"]
+
+def get_response(input):
+  query = input
+  chain_type_kwargs = {"prompt": prompt}
+  qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever, return_source_documents=True, chain_type_kwargs=chain_type_kwargs, verbose=True)
+  response = qa(query)
+  return response
+
+input = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+
+iface = gr.Interface(fn=get_response, 
+             inputs=input, 
+             outputs="text",
+             title="My Dog PetCare Bot",
+             description="This is a RAG implementation based on Zephyr 7B Beta LLM.",
+             examples=sample_prompts,
+             allow_screenshot=False,
+             allow_flagging=False
+             )
+
+iface.launch()          

ingest.py

@@ -0,0 +1,23 @@
+import os
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.embeddings import HuggingFaceBgeEmbeddings
+from langchain.document_loaders import PyPDFLoader
+
+model_name = "BAAI/bge-large-en"
+model_kwargs = {'device': 'cpu'}
+encode_kwargs = {'normalize_embeddings': False}
+embeddings = HuggingFaceBgeEmbeddings(
+    model_name=model_name,
+    model_kwargs=model_kwargs,
+    encode_kwargs=encode_kwargs
+)
+
+loader = PyPDFLoader("pet.pdf")
+documents = loader.load()
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+texts = text_splitter.split_documents(documents)
+
+vector_store = Chroma.from_documents(texts, embeddings, collection_metadata={"hnsw:space": "cosine"}, persist_directory="stores/pet_cosine")
+
+print("Vector Store Created.......")

pet.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:09130a774b5f6d864cbed5b14b88f7f6bb84e39f647d218aa54b2f89a5cf0a0f
+size 2451167

requirements.txt

@@ -0,0 +1,8 @@
+chainlit 
+ctransformers
+torch
+sentence_transformers
+chromadb 
+langchain
+pypdf 
+PyPDF2