refactored to langchain

backend/requirements.txt

@@ -0,0 +1,120 @@
+aiohttp==3.9.5
+aiosignal==1.3.1
+annotated-types==0.7.0
+anyio==4.4.0
+asgiref==3.8.1
+attrs==23.2.0
+backoff==2.2.1
+bcrypt==4.2.0
+beautifulsoup4==4.12.3
+bs4==0.0.2
+build==1.2.1
+cachetools==5.4.0
+certifi==2024.7.4
+charset-normalizer==3.3.2
+chroma-hnswlib==0.7.6
+chromadb==0.5.5
+click==8.1.7
+colorama==0.4.6
+coloredlogs==15.0.1
+dataclasses-json==0.6.7
+Deprecated==1.2.14
+distro==1.9.0
+dnspython==2.6.1
+email_validator==2.2.0
+fastapi==0.111.1
+fastapi-cli==0.0.4
+filelock==3.15.4
+flatbuffers==24.3.25
+frozenlist==1.4.1
+fsspec==2024.6.1
+google-auth==2.32.0
+googleapis-common-protos==1.63.2
+greenlet==3.0.3
+groq==0.9.0
+grpcio==1.65.1
+h11==0.14.0
+httpcore==1.0.5
+httptools==0.6.1
+httpx==0.27.0
+huggingface-hub==0.24.2
+humanfriendly==10.0
+idna==3.7
+importlib_metadata==8.0.0
+importlib_resources==6.4.0
+Jinja2==3.1.4
+jsonpatch==1.33
+jsonpointer==3.0.0
+kubernetes==30.1.0
+langchain==0.2.11
+langchain-chroma==0.1.2
+langchain-community==0.2.10
+langchain-core==0.2.24
+langchain-groq==0.1.6
+langchain-text-splitters==0.2.2
+langsmith==0.1.93
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+marshmallow==3.21.3
+mdurl==0.1.2
+mmh3==4.1.0
+monotonic==1.6
+mpmath==1.3.0
+multidict==6.0.5
+mypy-extensions==1.0.0
+numpy==1.26.4
+oauthlib==3.2.2
+onnxruntime==1.18.1
+opentelemetry-api==1.26.0
+opentelemetry-exporter-otlp-proto-common==1.26.0
+opentelemetry-exporter-otlp-proto-grpc==1.26.0
+opentelemetry-instrumentation==0.47b0
+opentelemetry-instrumentation-asgi==0.47b0
+opentelemetry-instrumentation-fastapi==0.47b0
+opentelemetry-proto==1.26.0
+opentelemetry-sdk==1.26.0
+opentelemetry-semantic-conventions==0.47b0
+opentelemetry-util-http==0.47b0
+orjson==3.10.6
+overrides==7.7.0
+packaging==24.1
+posthog==3.5.0
+protobuf==4.25.4
+pyasn1==0.6.0
+pyasn1_modules==0.4.0
+pydantic==2.8.2
+pydantic_core==2.20.1
+Pygments==2.18.0
+PyPika==0.48.9
+pyproject_hooks==1.1.0
+pyreadline3==3.4.1
+python-dateutil==2.9.0.post0
+python-dotenv==1.0.1
+python-multipart==0.0.9
+PyYAML==6.0.1
+requests==2.32.3
+requests-oauthlib==2.0.0
+rich==13.7.1
+rsa==4.9
+setuptools==71.1.0
+shellingham==1.5.4
+six==1.16.0
+sniffio==1.3.1
+soupsieve==2.5
+SQLAlchemy==2.0.31
+starlette==0.37.2
+sympy==1.13.1
+tenacity==8.5.0
+tokenizers==0.19.1
+tqdm==4.66.4
+typer==0.12.3
+typing-inspect==0.9.0
+typing_extensions==4.12.2
+urllib3==2.2.2
+uvicorn==0.30.3
+watchfiles==0.22.0
+websocket-client==1.8.0
+websockets==12.0
+wrapt==1.16.0
+yarl==1.9.4
+zipp==3.19.2

backend/src/main.py

@@ -0,0 +1,61 @@
+import os
+import bs4
+from dotenv import load_dotenv
+from langchain_groq import ChatGroq
+from langchain import hub
+from langchain_chroma import Chroma
+from langchain_community.document_loaders import DirectoryLoader
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+# from langchain_openai import OpenAIEmbeddings
+from langchain_huggingface.embeddings import HuggingFaceEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+model_name = "BAAI/bge-small-en"
+model_kwargs = {"device": "cpu"}
+encode_kwargs = {"normalize_embeddings": True}
+
+embeddings = HuggingFaceEmbeddings()
+
+load_dotenv()
+os.environ["LANGCHAIN_TRACING_V2"] = "true"
+
+
+llm = ChatGroq(model="llama3-8b-8192")
+
+docs = DirectoryLoader(
+    path = "./data",
+    silent_errors=True,
+    show_progress=True,
+    use_multithreading=True,
+    ).load_documents()
+
+if os.path.exists("./chroma_db"):
+    vectorstore = Chroma.from_directory("./chroma_db")
+else:
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+    splits = text_splitter.split_documents(docs)
+    vectorstore = Chroma.from_documents(
+        documents=splits, 
+        embedding=embeddings, 
+        persist_directory="./chroma_db",
+    )
+
+retriever = vectorstore.as_retriever()
+prompt = hub.pull("rlm/rag-prompt")
+
+
+def format_docs(docs):
+    return "\n\n".join(doc.page_content for doc in docs)
+
+
+rag_chain = (
+    {"context": retriever | format_docs, "question": RunnablePassthrough()}
+    | prompt
+    | llm
+    | StrOutputParser()
+)
+
+response = rag_chain.invoke("What is ultimate RAG?")
+
+print(response)