feat(*): rename

README.md

@@ -13,6 +13,8 @@ pinned: false
 
 A clone of the amazing https://huggingface.co/spaces/Ekimetrics/climate-question-answering.
 
+We abstracted the code so it's easy to build another tool based on another domain.
+
 ## Build vector index
 
 ```bash

{climateqa → anyqa}/chains.py

@@ -7,8 +7,8 @@ from langchain.chains import QAWithSourcesChain
 from langchain.chains import TransformChain, SequentialChain
 from langchain.chains.qa_with_sources import load_qa_with_sources_chain
 
-from climateqa.prompts import answer_prompt, reformulation_prompt
-from climateqa.custom_retrieval_chain import CustomRetrievalQAWithSourcesChain
+from anyqa.prompts import answer_prompt, reformulation_prompt
+from anyqa.custom_retrieval_chain import CustomRetrievalQAWithSourcesChain
 
 
 def load_qa_chain_with_docs(llm):
@@ -20,7 +20,7 @@ def load_qa_chain_with_docs(llm):
     ```
     output = chain({
         "question":query,
-        "audience":"experts climate scientists",
+        "audience":"experts scientists",
         "docs":docs,
         "language":"English",
     })
@@ -54,18 +54,18 @@ def load_qa_chain_with_text(llm):
     return qa_chain
 
 
-def load_climateqa_chain(retriever, llm_reformulation, llm_answer):
+def load_qa_chain(retriever, llm_reformulation, llm_answer):
     reformulation_chain = load_reformulation_chain(llm_reformulation)
     answer_chain = load_qa_chain_with_retriever(retriever, llm_answer)
 
-    climateqa_chain = SequentialChain(
+    qa_chain = SequentialChain(
         chains=[reformulation_chain, answer_chain],
         input_variables=["query", "audience"],
         output_variables=["answer", "question", "language", "source_documents"],
         return_all=True,
         verbose=True,
     )
-    return climateqa_chain
+    return qa_chain
 
 
 def load_reformulation_chain(llm):
@@ -112,6 +112,6 @@ def load_qa_chain_with_retriever(retriever, llm):
         retriever=retriever,
         return_source_documents=True,
         verbose=True,
-        fallback_answer="**⚠️ No relevant passages found in the climate science reports (IPCC and IPBES), you may want to ask a more specific question (specifying your question on climate issues).**",
+        fallback_answer="**⚠️ No relevant passages found in the sources, you may want to ask a more specific question.**",
     )
     return answer_chain

app.py

@@ -7,14 +7,14 @@ from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
 
 # ClimateQ&A imports
-from climateqa.embeddings import EMBEDDING_MODEL_NAME
-from climateqa.llm import get_llm
-from climateqa.qa_logging import log
-from climateqa.chains import load_qa_chain_with_text
-from climateqa.chains import load_reformulation_chain
-from climateqa.vectorstore import get_vectorstore
-from climateqa.retriever import QARetriever
-from climateqa.prompts import audience_prompts
+from anyqa.embeddings import EMBEDDING_MODEL_NAME
+from anyqa.llm import get_llm
+from anyqa.qa_logging import log
+from anyqa.chains import load_qa_chain_with_text
+from anyqa.chains import load_reformulation_chain
+from anyqa.vectorstore import get_vectorstore
+from anyqa.retriever import QARetriever
+from anyqa.prompts import audience_prompts
 
 # Load environment variables in local mode
 try: