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1 | 1 | from nemoguardrails import LLMRails, RailsConfig |
2 | | -from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, LLMPredictor |
3 | | -from llama_index.indices.query.base import BaseQueryEngine |
4 | 2 | from langchain.llms.base import BaseLLM |
5 | 3 |
|
6 | | -from typing import Callable |
| 4 | +from typing import Callable, Any, Coroutine |
7 | 5 |
|
8 | 6 | COLANG_CONFIG = """ |
9 | 7 | define user express greeting |
|
26 | 24 |
|
27 | 25 | # Question answering flow |
28 | 26 | define flow |
29 | | - user express question |
| 27 | + user ... |
30 | 28 | $answer = execute llama_index_query(query=$last_user_message) |
31 | 29 | bot $answer |
32 | 30 |
|
|
40 | 38 | """ |
41 | 39 |
|
42 | 40 |
|
43 | | -def _get_llama_index_query_engine(llm: BaseLLM): |
44 | | - docs = SimpleDirectoryReader( |
45 | | - input_files=["../examples/grounding_rail/kb/report.md"] |
46 | | - ).load_data() |
47 | | - llm_predictor = LLMPredictor(llm=llm) |
48 | | - index = GPTVectorStoreIndex.from_documents(docs, llm_predictor=llm_predictor) |
49 | | - default_query_engine = index.as_query_engine() |
50 | | - return default_query_engine |
51 | | - |
52 | | - |
53 | | -def _get_callable_query_engine( |
54 | | - query_engine: BaseQueryEngine |
55 | | -) -> Callable[[str], str]: |
56 | | - async def get_query_response(query: str) -> str: |
57 | | - return query_engine.query(query).response |
58 | | - |
59 | | - return get_query_response |
| 41 | +def demo(): |
| 42 | + try: |
| 43 | + import llama_index |
| 44 | + from llama_index.indices.query.base import BaseQueryEngine |
| 45 | + from llama_index.response.schema import StreamingResponse |
60 | 46 |
|
| 47 | + except ImportError: |
| 48 | + raise ImportError( |
| 49 | + "Could not import llama_index, please install it with " |
| 50 | + "`pip install llama_index`." |
| 51 | + ) |
61 | 52 |
|
62 | | -def demo(): |
63 | 53 | config = RailsConfig.from_content(COLANG_CONFIG, YAML_CONFIG) |
64 | 54 | app = LLMRails(config) |
65 | | - query_engine: BaseQueryEngine = _get_llama_index_query_engine(app.llm) |
| 55 | + |
| 56 | + def _get_llama_index_query_engine(llm: BaseLLM): |
| 57 | + docs = llama_index.SimpleDirectoryReader( |
| 58 | + input_files=["../examples/grounding_rail/kb/report.md"] |
| 59 | + ).load_data() |
| 60 | + llm_predictor = llama_index.LLMPredictor(llm=llm) |
| 61 | + index = llama_index.GPTVectorStoreIndex.from_documents( |
| 62 | + docs, llm_predictor=llm_predictor |
| 63 | + ) |
| 64 | + default_query_engine = index.as_query_engine() |
| 65 | + return default_query_engine |
| 66 | + |
| 67 | + def _get_callable_query_engine( |
| 68 | + query_engine: BaseQueryEngine, |
| 69 | + ) -> Callable[[str], Coroutine[Any, Any, str]]: |
| 70 | + async def get_query_response(query: str) -> str: |
| 71 | + response = query_engine.query(query) |
| 72 | + if isinstance(response, StreamingResponse): |
| 73 | + typed_response = response.get_response() |
| 74 | + else: |
| 75 | + typed_response = response |
| 76 | + response_str = typed_response.response |
| 77 | + if response_str is None: |
| 78 | + return "" |
| 79 | + return response_str |
| 80 | + |
| 81 | + return get_query_response |
| 82 | + |
| 83 | + query_engine = _get_llama_index_query_engine(app.llm) |
66 | 84 | app.register_action( |
67 | 85 | _get_callable_query_engine(query_engine), name="llama_index_query" |
68 | 86 | ) |
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