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https://github.com/hwchase17/langchain.git
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add example of memory and returning retrieved docs (#8830)
This commit is contained in:
Harrison Chase
GitHub
parent
4a7ebb7184
commit
2bb1d256f3
@ -22,7 +22,7 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 68,
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"execution_count": 7,
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"id": "f60a5d0f",
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@ -572,7 +572,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"execution_count": 8,
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"id": "7d007db6",
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"metadata": {},
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"outputs": [],
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@ -589,25 +589,29 @@
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},
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{
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"cell_type": "code",
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"execution_count": 70,
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"execution_count": 16,
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"id": "5c32cc89",
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"metadata": {},
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"outputs": [],
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"source": [
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"conversational_qa_chain = RunnableMap({\n",
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" \"standalone_question\": {\n",
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" \"question\": lambda x: x[\"question\"],\n",
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" \"chat_history\": lambda x: _format_chat_history(x['chat_history'])\n",
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" } | CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0) | StrOutputParser(),\n",
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"}) | {\n",
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"_inputs = RunnableMap(\n",
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" {\n",
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" \"standalone_question\": {\n",
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" \"question\": lambda x: x[\"question\"],\n",
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" \"chat_history\": lambda x: _format_chat_history(x['chat_history'])\n",
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" } | CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0) | StrOutputParser(),\n",
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" }\n",
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")\n",
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"_context = {\n",
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" \"context\": itemgetter(\"standalone_question\") | retriever | _combine_documents,\n",
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" \"question\": lambda x: x[\"standalone_question\"]\n",
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"} | ANSWER_PROMPT | ChatOpenAI()"
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"}\n",
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"conversational_qa_chain = _inputs | _context | ANSWER_PROMPT | ChatOpenAI()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 71,
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"execution_count": 17,
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"id": "135c8205",
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"metadata": {},
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"outputs": [
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@ -624,7 +628,7 @@
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"AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 71,
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -638,7 +642,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 62,
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"execution_count": 15,
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"id": "424e7e7a",
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"metadata": {},
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"outputs": [
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@ -655,7 +659,7 @@
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"AIMessage(content='Harrison worked at Kensho.', additional_kwargs={}, example=False)"
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]
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},
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"execution_count": 62,
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -667,6 +671,149 @@
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"})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c5543183",
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"metadata": {},
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"source": [
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"### With Memory and returning source documents\n",
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"\n",
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"This shows how to use memory with the above. For memory, we need to manage that outside at the memory. For returning the retrieved documents, we just need to pass them through all the way."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "e31dd17c",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.memory import ConversationBufferMemory"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"id": "d4bffe94",
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"metadata": {},
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"outputs": [],
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"source": [
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"memory = ConversationBufferMemory(return_messages=True, output_key=\"answer\", input_key=\"question\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"id": "733be985",
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"metadata": {},
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"outputs": [],
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"source": [
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"# First we add a step to load memory\n",
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"# This needs to be a RunnableMap because its the first input\n",
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"loaded_memory = RunnableMap(\n",
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" {\n",
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" \"question\": itemgetter(\"question\"),\n",
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" \"memory\": memory.load_memory_variables,\n",
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" }\n",
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")\n",
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"# Next we add a step to expand memory into the variables\n",
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"expanded_memory = {\n",
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" \"question\": itemgetter(\"question\"),\n",
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" \"chat_history\": lambda x: x[\"memory\"][\"history\"]\n",
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"}\n",
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"\n",
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"# Now we calculate the standalone question\n",
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"standalone_question = {\n",
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" \"standalone_question\": {\n",
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" \"question\": lambda x: x[\"question\"],\n",
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" \"chat_history\": lambda x: _format_chat_history(x['chat_history'])\n",
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" } | CONDENSE_QUESTION_PROMPT | ChatOpenAI(temperature=0) | StrOutputParser(),\n",
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"}\n",
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"# Now we retrieve the documents\n",
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"retrieved_documents = {\n",
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" \"docs\": itemgetter(\"standalone_question\") | retriever,\n",
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" \"question\": lambda x: x[\"standalone_question\"]\n",
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"}\n",
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"# Now we construct the inputs for the final prompt\n",
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"final_inputs = {\n",
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" \"context\": lambda x: _combine_documents(x[\"docs\"]),\n",
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" \"question\": itemgetter(\"question\")\n",
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"}\n",
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"# And finally, we do the part that returns the answers\n",
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"answer = {\n",
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" \"answer\": final_inputs | ANSWER_PROMPT | ChatOpenAI(),\n",
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" \"docs\": itemgetter(\"docs\"),\n",
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"}\n",
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"# And now we put it all together!\n",
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"final_chain = loaded_memory | expanded_memory | standalone_question | retrieved_documents | answer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"id": "806e390c",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Number of requested results 4 is greater than number of elements in index 1, updating n_results = 1\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'answer': AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False),\n",
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" 'docs': [Document(page_content='harrison worked at kensho', metadata={})]}"
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]
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},
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"execution_count": 46,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"inputs = {\"question\": \"where did harrison work?\"}\n",
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"result = final_chain.invoke(inputs)\n",
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"result"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"id": "977399fd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Note that the memory does not save automatically\n",
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"# This will be improved in the future\n",
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"# For now you need to save it yourself\n",
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"memory.save_context(inputs, {\"answer\": result[\"answer\"].content})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"id": "f94f7de4",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'history': [HumanMessage(content='where did harrison work?', additional_kwargs={}, example=False),\n",
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" AIMessage(content='Harrison was employed at Kensho.', additional_kwargs={}, example=False)]}"
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]
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},
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"execution_count": 48,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"memory.load_memory_variables({})"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0f2bf8d3",
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@ -1397,7 +1544,9 @@
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"id": "179d3c03",
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"## Conversational Retrieval With Memory"
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]
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}
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],
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"metadata": {
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@ -1416,7 +1565,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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"version": "3.10.1"
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}
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},
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"nbformat": 4,
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