Retrieval Augmented Generation (RAG) is a popular technique to get LLMs to provide answers that are grounded in a data source. When we use RAG, we use the user's question to search a knowledge base (like Azure AI Search), then pass along both the question and the relevant content to the LLM (gpt-3.5-turbo or gpt-4), with a directive to answer only according to the sources. In psuedo-code:
user_query = "what's in the Northwind Plus plan?"
user_query_vector = create_embedding(user_query, "ada-002")
results = search(user_query, user_query_vector)
response = create_chat_completion(system_prompt, user_query, results)
If the search function can find the right results in the index (assuming the answer is somewhere in the index), then the LLM can typically do a pretty good job of synthesizing the answer from the sources.
Unstructured queries
This simple RAG approach works best for "unstructured queries", like:
- What's in the Northwind Plus plan?
- What are the expectations of a product manager?
- What benefits are provided by the company?
When using Azure AI Search as the knowledge base, the search call will perform both a vector and keyword search, finding all the relevant document chunks that match the keywords and concepts in the query.
Structured queries
But you may find that users are instead asking more "structured" queries, like:
- Summarize the document called "perksplus.pdf"
- What are the topics in documents by Pamela Fox?
- Key points in most recent uploaded documents
We can think of them as structured queries, because they're trying to filter on specific metadata about a document. You could imagine a world where you used a syntax to specify that metadata filtering, like:
- Summarize the document title:perksplus.pdf
- Topics in documents author:PamelaFox
- Key points time:2weeks
We don't want to actually introduce a query syntax to a a RAG chat application if we don't need to, since only power users tend to use specialized query syntax, and we'd ideally have our RAG just do the right thing in that situation.
Using function calling in RAG
Fortunately, we can use the OpenAI function-calling feature to recognize that a user's query would benefit from a more structured search, and perform that search instead.
If you've never used function calling before, it's an alternative way of asking an OpenAI GPT model to respond to a chat completion request. In addition to sending our usual system prompt, chat history, and user message, we also send along a list of possible functions that could be called to answer the question. We can define those in JSON or as a Pydantic model dumped to JSON. Then, when the response comes back from the model, we can see what function it decided to call, and with what parameters. At that point, we can actually call that function, if it exists, or just use that information in our code in some other way.
To use function calling in RAG, we first need to introduce an LLM pre-processing step to handle user queries, as I described in my previous blog post. That will give us an opportunity to intercept the query before we even perform the search step of RAG.
For that pre-processing step, we can start off with a function to handle the general case of unstructured queries:
tools: List[ChatCompletionToolParam] = [
{
"type": "function",
"function": {
"name": "search_sources",
"description": "Retrieve sources from the Azure AI Search index",
"parameters": {
"type": "object",
"properties": {
"search_query": {
"type": "string",
"description": "Query string to retrieve documents from azure search eg: 'Health care plan'",
}
},
"required": ["search_query"],
},
},
}
]Then we send off a request to the chat completion API, letting it know it can use that function.
chat_completion: ChatCompletion = self.openai_client.chat.completions.create(
messages=messages,
model=model,
temperature=0.0,
max_tokens=100,
n=1,
tools=tools,
tool_choice="auto",
)When the response comes back, we process it to see if the model decided to call the function, and extract the search_query parameter if so.
response_message = chat_completion.choices[0].message
if response_message.tool_calls:
for tool in response_message.tool_calls:
if tool.type != "function":
continue
function = tool.function
if function.name == "search_sources":
arg = json.loads(function.arguments)
search_query = arg.get("search_query", self.NO_RESPONSE)
If the model didn't include the function call in its response, that's not a big deal as we just fall back to using the user's original query as the search query. We proceed with the rest of the RAG flow as usual, sending the original question with whatever results came back in our final LLM call.
Adding more functions for structured queries
Now that we've introduced one function into the RAG flow, we can more easily add additional functions to recognize structured queries. For example, this function recognizes when a user wants to search by a particular filename:
{
"type": "function",
"function": {
"name": "search_by_filename",
"description": "Retrieve a specific filename from the Azure AI Search index",
"parameters": {
"type": "object",
"properties": {
"filename": {
"type": "string",
"description": "The filename, like 'PerksPlus.pdf'",
}
},
"required": ["filename"],
},
},
},We need to extend the function parsing code to extract the filename argument:
if function.name == "search_by_filename":
arg = json.loads(function.arguments)
filename = arg.get("filename", "")
filename_filter = filenameThen we can decide how to use that filename filter. In the case of Azure AI search, I build a filter that checks that a particular index field matches the filename argument, and pass that to my search call. If using a relational database, it'd become an additional WHERE clause.
Simply by adding that function, I was able to get much better answers to questions in my RAG app like 'Summarize the document called "perksplus.pdf"', since my search results were truly limited to chunks from that file. You can see my full code changes to add this function to our RAG starter app repo in this PR.
Considerations
This can be a very powerful technique, but as with all things LLM, there are gotchas:
- Function definitions add to your prompt token count, increasing cost.
- There may be times where the LLM doesn't decide to return the function call, even when you thought it should have.
- The more functions you add, the more likely the LLM will get confused about which one to pick, especially if functions are similar to each other. You can try to make it more clear to the LLM by prompt engineering the function name and description, or even providing few shots.
Here are additional approaches you can try:
- Content expansion: Store metadata inside the indexed field and compute the embedding based on both the metadata and content. For example, the content field could have "filename:perksplus.pdf text:The perks are...".
- Add metadata as separate fields in the search index, and append those to the content sent to the LLM. For example, you could put "Last modified: 2 weeks ago" in each chunk sent to the LLM, if you were trying to help it's ability to answer questions about recency. This is similar to the content expansion approach, but the metadata isn't included when calculating the embedding. You could also compute embeddings separately for each metadata field, and do a multi-vector search.
- Add filters to the UI of your RAG chat application, as part of the chat box or a sidebar of settings.
- Use fine-tuning on a model to help it realize when it should call particular functions or respond a certain way. You could even teach it to use a structured query syntax, and remove the functions entirely from your call. This is a last resort, however, since fine-tuning is costly and time-consuming.
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