Merge pull request #20 from lfoppiano/fix-conversational-memory

Fix conversational memory

document_qa/document_qa_engine.py

@@ -5,10 +5,12 @@
 
 from document_qa.grobid_processors import GrobidProcessor
 from grobid_client.grobid_client import GrobidClient
-from langchain.chains import create_extraction_chain
-from langchain.chains.question_answering import load_qa_chain
+from langchain.chains import create_extraction_chain, ConversationChain, ConversationalRetrievalChain
+from langchain.chains.question_answering import load_qa_chain, stuff_prompt, refine_prompts, map_reduce_prompt, \
+    map_rerank_prompt
 from langchain.prompts import SystemMessagePromptTemplate, HumanMessagePromptTemplate, ChatPromptTemplate
 from langchain.retrievers import MultiQueryRetriever
+from langchain.schema import Document
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import Chroma
 from tqdm import tqdm
@@ -22,15 +24,28 @@ class DocumentQAEngine:
     embeddings_map_from_md5 = {}
     embeddings_map_to_md5 = {}
 
+    default_prompts = {
+        'stuff': stuff_prompt,
+        'refine': refine_prompts,
+        "map_reduce": map_reduce_prompt,
+        "map_rerank": map_rerank_prompt
+    }
+
     def __init__(self,
                  llm,
                  embedding_function,
                  qa_chain_type="stuff",
                  embeddings_root_path=None,
                  grobid_url=None,
+                 memory=None
                  ):
         self.embedding_function = embedding_function
         self.llm = llm
+        # if memory:
+        #     prompt = self.default_prompts[qa_chain_type].PROMPT_SELECTOR.get_prompt(llm)
+        #     self.chain = load_qa_chain(llm, chain_type=qa_chain_type, prompt=prompt, memory=memory)
+        # else:
+        self.memory = memory
         self.chain = load_qa_chain(llm, chain_type=qa_chain_type)
 
         if embeddings_root_path is not None:
@@ -86,14 +101,14 @@ def get_filename_from_md5(self, md5):
         return self.embeddings_map_from_md5[md5]
 
     def query_document(self, query: str, doc_id, output_parser=None, context_size=4, extraction_schema=None,
-                       verbose=False, memory=None) -> (
+                       verbose=False) -> (
             Any, str):
         # self.load_embeddings(self.embeddings_root_path)
 
         if verbose:
             print(query)
 
-        response = self._run_query(doc_id, query, context_size=context_size, memory=memory)
+        response = self._run_query(doc_id, query, context_size=context_size)
         response = response['output_text'] if 'output_text' in response else response
 
         if verbose:
@@ -143,21 +158,21 @@ def _parse_json(self, response, output_parser):
 
         return parsed_output
 
-    def _run_query(self, doc_id, query, context_size=4, memory=None):
+    def _run_query(self, doc_id, query, context_size=4):
         relevant_documents = self._get_context(doc_id, query, context_size)
-        if memory:
-            return self.chain.run(input_documents=relevant_documents,
-                                  question=query)
-        else:
-            return self.chain.run(input_documents=relevant_documents,
-                                  question=query,
-                                  memory=memory)
-        # return self.chain({"input_documents": relevant_documents, "question": prompt_chat_template}, return_only_outputs=True)
+        response = self.chain.run(input_documents=relevant_documents,
+                              question=query)
+
+        if self.memory:
+            self.memory.save_context({"input": query}, {"output": response})
+        return response
 
     def _get_context(self, doc_id, query, context_size=4):
         db = self.embeddings_dict[doc_id]
         retriever = db.as_retriever(search_kwargs={"k": context_size})
         relevant_documents = retriever.get_relevant_documents(query)
+        if self.memory and len(self.memory.buffer_as_messages) > 0:
+            relevant_documents.append(Document(page_content="Previous conversation:\n{}\n\n".format(self.memory.buffer_as_str)))
         return relevant_documents
 
     def get_all_context_by_document(self, doc_id):
@@ -239,11 +254,15 @@ def create_memory_embeddings(self, pdf_path, doc_id=None, chunk_size=500, perc_o
             hash = metadata[0]['hash']
 
         if hash not in self.embeddings_dict.keys():
-            self.embeddings_dict[hash] = Chroma.from_texts(texts, embedding=self.embedding_function, metadatas=metadata,
+            self.embeddings_dict[hash] = Chroma.from_texts(texts,
+                                                           embedding=self.embedding_function,
+                                                           metadatas=metadata,
                                                            collection_name=hash)
         else:
             self.embeddings_dict[hash].delete(ids=self.embeddings_dict[hash].get()['ids'])
-            self.embeddings_dict[hash] = Chroma.from_texts(texts, embedding=self.embedding_function, metadatas=metadata,
+            self.embeddings_dict[hash] = Chroma.from_texts(texts,
+                                                           embedding=self.embedding_function,
+                                                           metadatas=metadata,
                                                            collection_name=hash)
 
         self.embeddings_root_path = None

streamlit_app.py

@@ -5,6 +5,7 @@
 
 import dotenv
 from grobid_quantities.quantities import QuantitiesAPI
+from langchain.callbacks import PromptLayerCallbackHandler
 from langchain.llms.huggingface_hub import HuggingFaceHub
 from langchain.memory import ConversationBufferWindowMemory
 
@@ -80,6 +81,7 @@ def clear_memory():
 
 # @st.cache_resource
 def init_qa(model, api_key=None):
+    ## For debug add: callbacks=[PromptLayerCallbackHandler(pl_tags=["langchain", "chatgpt", "document-qa"])])
     if model == 'chatgpt-3.5-turbo':
         if api_key:
             chat = ChatOpenAI(model_name="gpt-3.5-turbo",
@@ -108,7 +110,7 @@ def init_qa(model, api_key=None):
         st.stop()
         return
 
-    return DocumentQAEngine(chat, embeddings, grobid_url=os.environ['GROBID_URL'])
+    return DocumentQAEngine(chat, embeddings, grobid_url=os.environ['GROBID_URL'], memory=st.session_state['memory'])
 
 
 @st.cache_resource
@@ -316,8 +318,7 @@ def play_old_messages():
     elif mode == "LLM":
         with st.spinner("Generating response..."):
             _, text_response = st.session_state['rqa'][model].query_document(question, st.session_state.doc_id,
-                                                                             context_size=context_size,
-                                                                             memory=st.session_state.memory)
+                                                                             context_size=context_size)
 
     if not text_response:
         st.error("Something went wrong. Contact Luca Foppiano ([email protected]) to report the issue.")
@@ -336,11 +337,11 @@ def play_old_messages():
             st.write(text_response)
         st.session_state.messages.append({"role": "assistant", "mode": mode, "content": text_response})
 
-        for id in range(0, len(st.session_state.messages), 2):
-            question = st.session_state.messages[id]['content']
-            if len(st.session_state.messages) > id + 1:
-                answer = st.session_state.messages[id + 1]['content']
-                st.session_state.memory.save_context({"input": question}, {"output": answer})
+        # if len(st.session_state.messages) > 1:
+        #     last_answer = st.session_state.messages[len(st.session_state.messages)-1]
+        #     if last_answer['role'] == "assistant":
+        #         last_question = st.session_state.messages[len(st.session_state.messages)-2]
+        #         st.session_state.memory.save_context({"input": last_question['content']}, {"output": last_answer['content']})
 
 elif st.session_state.loaded_embeddings and st.session_state.doc_id:
     play_old_messages()