Update main.py

main.py

@@ -50,40 +50,40 @@ llm_chain_model = LLMChain(prompt=prompt1, llm=llm)
 # for retriver
 
 
-def format_docs(docs):
-    return "\n\n".join(doc.page_content for doc in docs)
-
-model_name = "BAAI/bge-base-en-v1.5"
-model_kwargs = {"device":'cpu'}
-encode_kwargs = {'normalize_embeddings':True}
-
-hf = HuggingFaceEmbeddings(
-    model_name = model_name,
-    model_kwargs = model_kwargs,
-    encode_kwargs = encode_kwargs
-)
-
-
-vectorstore = Chroma(
-    collection_name="example_collection",
-    embedding_function=hf,
-    persist_directory="./chroma_langchain_db",  # Where to save data locally, remove if not neccesary
-)
-
-retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 6})
-template = """you are the financial ai assistant
-{context}
-Question: {question}
-Helpful Answer:"""
-custom_rag_prompt = PromptTemplate.from_template(template)
-
-rag_chain = (
-    {"context": retriever | format_docs, "question": RunnablePassthrough()}
-    | custom_rag_prompt
-    | llm
-    | StrOutputParser()
-)
-print("retriver done")
+# def format_docs(docs):
+#     return "\n\n".join(doc.page_content for doc in docs)
+
+# model_name = "BAAI/bge-base-en-v1.5"
+# model_kwargs = {"device":'cpu'}
+# encode_kwargs = {'normalize_embeddings':True}
+
+# hf = HuggingFaceEmbeddings(
+#     model_name = model_name,
+#     model_kwargs = model_kwargs,
+#     encode_kwargs = encode_kwargs
+# )
+
+
+# vectorstore = Chroma(
+#     collection_name="example_collection",
+#     embedding_function=hf,
+#     persist_directory="./chroma_langchain_db",  # Where to save data locally, remove if not neccesary
+# )
+
+# retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 6})
+# template = """you are the financial ai assistant
+# {context}
+# Question: {question}
+# Helpful Answer:"""
+# custom_rag_prompt = PromptTemplate.from_template(template)
+
+# rag_chain = (
+#     {"context": retriever | format_docs, "question": RunnablePassthrough()}
+#     | custom_rag_prompt
+#     | llm
+#     | StrOutputParser()
+# )
+# print("retriver done")
 
 from fastapi import FastAPI
 
@@ -106,7 +106,7 @@ class Item(BaseModel):
 
 @app.post("/prompt/")
 def create_item(item: Item):
-    message_response = rag_chain.invoke(item.get('question'))
+    message_response = llm_chain_model.run(item.get('question'))
     return {"item": item, "message": "LLm response", 'response': message_response}