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"""FastAPI app creation, logger configuration and main API routes."""
import logging
from fastapi import Depends, FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
from injector import Injector
from llama_index.core.callbacks import CallbackManager
from llama_index.core.callbacks.global_handlers import create_global_handler
from llama_index.core.settings import Settings as LlamaIndexSettings
from private_gpt.server.chat.chat_router import chat_router
from private_gpt.server.chunks.chunks_router import chunks_router
from private_gpt.server.completions.completions_router import completions_router
from private_gpt.server.embeddings.embeddings_router import embeddings_router
from private_gpt.server.health.health_router import health_router
from private_gpt.server.ingest.ingest_router import ingest_router
from private_gpt.settings.settings import Settings
logger = logging.getLogger(__name__)
def create_app(root_injector: Injector) -> FastAPI:
# Start the API
async def bind_injector_to_request(request: Request) -> None:
request.state.injector = root_injector
app = FastAPI(dependencies=[Depends(bind_injector_to_request)])
app.include_router(completions_router)
app.include_router(chat_router)
app.include_router(chunks_router)
app.include_router(ingest_router)
app.include_router(embeddings_router)
app.include_router(health_router)
# Add LlamaIndex simple observability
global_handler = create_global_handler("simple")
LlamaIndexSettings.callback_manager = CallbackManager([global_handler])
settings = root_injector.get(Settings)
if settings.server.cors.enabled:
logger.debug("Setting up CORS middleware")
app.add_middleware(
CORSMiddleware,
allow_credentials=settings.server.cors.allow_credentials,
allow_origins=settings.server.cors.allow_origins,
allow_origin_regex=settings.server.cors.allow_origin_regex,
allow_methods=settings.server.cors.allow_methods,
allow_headers=settings.server.cors.allow_headers,
)
if settings.ui.enabled:
logger.debug("Importing the UI module")
try:
from private_gpt.ui.ui import PrivateGptUi
except ImportError as e:
raise ImportError(
"UI dependencies not found, install with `poetry install --extras ui`"
) from e
ui = root_injector.get(PrivateGptUi)
ui.mount_in_app(app, settings.ui.path)
return app
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"llama_index.core.callbacks.CallbackManager",
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import json
import os
from typing import Dict, List, Optional, Type
from loguru import logger
from datastore.datastore import DataStore
from models.models import (
DocumentChunk,
DocumentChunkMetadata,
DocumentChunkWithScore,
DocumentMetadataFilter,
Query,
QueryResult,
QueryWithEmbedding,
)
from llama_index.indices.base import BaseGPTIndex
from llama_index.indices.vector_store.base import GPTVectorStoreIndex
from llama_index.indices.query.schema import QueryBundle
from llama_index.response.schema import Response
from llama_index.data_structs.node_v2 import Node, DocumentRelationship, NodeWithScore
from llama_index.indices.registry import INDEX_STRUCT_TYPE_TO_INDEX_CLASS
from llama_index.data_structs.struct_type import IndexStructType
from llama_index.indices.response.builder import ResponseMode
INDEX_STRUCT_TYPE_STR = os.environ.get(
"LLAMA_INDEX_TYPE", IndexStructType.SIMPLE_DICT.value
)
INDEX_JSON_PATH = os.environ.get("LLAMA_INDEX_JSON_PATH", None)
QUERY_KWARGS_JSON_PATH = os.environ.get("LLAMA_QUERY_KWARGS_JSON_PATH", None)
RESPONSE_MODE = os.environ.get("LLAMA_RESPONSE_MODE", ResponseMode.NO_TEXT.value)
EXTERNAL_VECTOR_STORE_INDEX_STRUCT_TYPES = [
IndexStructType.DICT,
IndexStructType.WEAVIATE,
IndexStructType.PINECONE,
IndexStructType.QDRANT,
IndexStructType.CHROMA,
IndexStructType.VECTOR_STORE,
]
def _create_or_load_index(
index_type_str: Optional[str] = None,
index_json_path: Optional[str] = None,
index_type_to_index_cls: Optional[dict[str, Type[BaseGPTIndex]]] = None,
) -> BaseGPTIndex:
"""Create or load index from json path."""
index_json_path = index_json_path or INDEX_JSON_PATH
index_type_to_index_cls = (
index_type_to_index_cls or INDEX_STRUCT_TYPE_TO_INDEX_CLASS
)
index_type_str = index_type_str or INDEX_STRUCT_TYPE_STR
index_type = IndexStructType(index_type_str)
if index_type not in index_type_to_index_cls:
raise ValueError(f"Unknown index type: {index_type}")
if index_type in EXTERNAL_VECTOR_STORE_INDEX_STRUCT_TYPES:
raise ValueError("Please use vector store directly.")
index_cls = index_type_to_index_cls[index_type]
if index_json_path is None:
return index_cls(nodes=[]) # Create empty index
else:
return index_cls.load_from_disk(index_json_path) # Load index from disk
def _create_or_load_query_kwargs(
query_kwargs_json_path: Optional[str] = None,
) -> Optional[dict]:
"""Create or load query kwargs from json path."""
query_kwargs_json_path = query_kwargs_json_path or QUERY_KWARGS_JSON_PATH
query_kargs: Optional[dict] = None
if query_kwargs_json_path is not None:
with open(INDEX_JSON_PATH, "r") as f:
query_kargs = json.load(f)
return query_kargs
def _doc_chunk_to_node(doc_chunk: DocumentChunk, source_doc_id: str) -> Node:
"""Convert document chunk to Node"""
return Node(
doc_id=doc_chunk.id,
text=doc_chunk.text,
embedding=doc_chunk.embedding,
extra_info=doc_chunk.metadata.dict(),
relationships={DocumentRelationship.SOURCE: source_doc_id},
)
def _query_with_embedding_to_query_bundle(query: QueryWithEmbedding) -> QueryBundle:
return QueryBundle(
query_str=query.query,
embedding=query.embedding,
)
def _source_node_to_doc_chunk_with_score(
node_with_score: NodeWithScore,
) -> DocumentChunkWithScore:
node = node_with_score.node
if node.extra_info is not None:
metadata = DocumentChunkMetadata(**node.extra_info)
else:
metadata = DocumentChunkMetadata()
return DocumentChunkWithScore(
id=node.doc_id,
text=node.text,
score=node_with_score.score if node_with_score.score is not None else 1.0,
metadata=metadata,
)
def _response_to_query_result(
response: Response, query: QueryWithEmbedding
) -> QueryResult:
results = [
_source_node_to_doc_chunk_with_score(node) for node in response.source_nodes
]
return QueryResult(
query=query.query,
results=results,
)
class LlamaDataStore(DataStore):
def __init__(
self, index: Optional[BaseGPTIndex] = None, query_kwargs: Optional[dict] = None
):
self._index = index or _create_or_load_index()
self._query_kwargs = query_kwargs or _create_or_load_query_kwargs()
async def _upsert(self, chunks: Dict[str, List[DocumentChunk]]) -> List[str]:
"""
Takes in a list of list of document chunks and inserts them into the database.
Return a list of document ids.
"""
doc_ids = []
for doc_id, doc_chunks in chunks.items():
logger.debug(f"Upserting {doc_id} with {len(doc_chunks)} chunks")
nodes = [
_doc_chunk_to_node(doc_chunk=doc_chunk, source_doc_id=doc_id)
for doc_chunk in doc_chunks
]
self._index.insert_nodes(nodes)
doc_ids.append(doc_id)
return doc_ids
async def _query(
self,
queries: List[QueryWithEmbedding],
) -> List[QueryResult]:
"""
Takes in a list of queries with embeddings and filters and
returns a list of query results with matching document chunks and scores.
"""
query_result_all = []
for query in queries:
if query.filter is not None:
logger.warning("Filters are not supported yet, ignoring for now.")
query_bundle = _query_with_embedding_to_query_bundle(query)
# Setup query kwargs
if self._query_kwargs is not None:
query_kwargs = self._query_kwargs
else:
query_kwargs = {}
# TODO: support top_k for other indices
if isinstance(self._index, GPTVectorStoreIndex):
query_kwargs["similarity_top_k"] = query.top_k
response = await self._index.aquery(
query_bundle, response_mode=RESPONSE_MODE, **query_kwargs
)
query_result = _response_to_query_result(response, query)
query_result_all.append(query_result)
return query_result_all
async def delete(
self,
ids: Optional[List[str]] = None,
filter: Optional[DocumentMetadataFilter] = None,
delete_all: Optional[bool] = None,
) -> bool:
"""
Removes vectors by ids, filter, or everything in the datastore.
Returns whether the operation was successful.
"""
if delete_all:
logger.warning("Delete all not supported yet.")
return False
if filter is not None:
logger.warning("Filters are not supported yet.")
return False
if ids is not None:
for id_ in ids:
try:
self._index.delete(id_)
except NotImplementedError:
# NOTE: some indices does not support delete yet.
logger.warning(f"{type(self._index)} does not support delete yet.")
return False
return True
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import os
import weaviate
from llama_index.storage.storage_context import StorageContext
from llama_index.vector_stores import WeaviateVectorStore
from llama_index.query_engine.retriever_query_engine import RetrieverQueryEngine
from llama_index.callbacks.base import CallbackManager
from llama_index import (
LLMPredictor,
ServiceContext,
StorageContext,
VectorStoreIndex,
)
import chainlit as cl
from llama_index.llms import LocalAI
from llama_index.embeddings import HuggingFaceEmbedding
import yaml
# Load the configuration file
with open("config.yaml", "r") as ymlfile:
cfg = yaml.safe_load(ymlfile)
# Get the values from the configuration file or set the default values
temperature = cfg['localAI'].get('temperature', 0)
model_name = cfg['localAI'].get('modelName', "gpt-3.5-turbo")
api_base = cfg['localAI'].get('apiBase', "http://local-ai.default")
api_key = cfg['localAI'].get('apiKey', "stub")
streaming = cfg['localAI'].get('streaming', True)
weaviate_url = cfg['weviate'].get('url', "http://weviate.default")
index_name = cfg['weviate'].get('index', "AIChroma")
query_mode = cfg['query'].get('mode', "hybrid")
topK = cfg['query'].get('topK', 1)
alpha = cfg['query'].get('alpha', 0.0)
embed_model_name = cfg['embedding'].get('model', "BAAI/bge-small-en-v1.5")
chunk_size = cfg['query'].get('chunkSize', 1024)
embed_model = HuggingFaceEmbedding(model_name=embed_model_name)
llm = LocalAI(temperature=temperature, model_name=model_name, api_base=api_base, api_key=api_key, streaming=streaming)
llm.globally_use_chat_completions = True;
client = weaviate.Client(weaviate_url)
vector_store = WeaviateVectorStore(weaviate_client=client, index_name=index_name)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
@cl.on_chat_start
async def factory():
llm_predictor = LLMPredictor(
llm=llm
)
service_context = ServiceContext.from_defaults(embed_model=embed_model, callback_manager=CallbackManager([cl.LlamaIndexCallbackHandler()]), llm_predictor=llm_predictor, chunk_size=chunk_size)
index = VectorStoreIndex.from_vector_store(
vector_store,
storage_context=storage_context,
service_context=service_context
)
query_engine = index.as_query_engine(vector_store_query_mode=query_mode, similarity_top_k=topK, alpha=alpha, streaming=True)
cl.user_session.set("query_engine", query_engine)
@cl.on_message
async def main(message: cl.Message):
query_engine = cl.user_session.get("query_engine")
response = await cl.make_async(query_engine.query)(message.content)
response_message = cl.Message(content="")
for token in response.response_gen:
await response_message.stream_token(token=token)
if response.response_txt:
response_message.content = response.response_txt
await response_message.send()
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import typer
import uuid
from typing import Optional, List, Any
import os
import numpy as np
from memgpt.utils import is_valid_url, printd
from memgpt.data_types import EmbeddingConfig
from memgpt.credentials import MemGPTCredentials
from memgpt.constants import MAX_EMBEDDING_DIM, EMBEDDING_TO_TOKENIZER_MAP, EMBEDDING_TO_TOKENIZER_DEFAULT
# from llama_index.core.base.embeddings import BaseEmbedding
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import Document as LlamaIndexDocument
# from llama_index.core.base.embeddings import BaseEmbedding
# from llama_index.core.embeddings import BaseEmbedding
# from llama_index.core.base.embeddings.base import BaseEmbedding
# from llama_index.bridge.pydantic import PrivateAttr
# from llama_index.embeddings.base import BaseEmbedding
# from llama_index.embeddings.huggingface_utils import format_text
import tiktoken
def parse_and_chunk_text(text: str, chunk_size: int) -> List[str]:
parser = SentenceSplitter(chunk_size=chunk_size)
llama_index_docs = [LlamaIndexDocument(text=text)]
nodes = parser.get_nodes_from_documents(llama_index_docs)
return [n.text for n in nodes]
def truncate_text(text: str, max_length: int, encoding) -> str:
# truncate the text based on max_length and encoding
encoded_text = encoding.encode(text)[:max_length]
return encoding.decode(encoded_text)
def check_and_split_text(text: str, embedding_model: str) -> List[str]:
"""Split text into chunks of max_length tokens or less"""
if embedding_model in EMBEDDING_TO_TOKENIZER_MAP:
encoding = tiktoken.get_encoding(EMBEDDING_TO_TOKENIZER_MAP[embedding_model])
else:
print(f"Warning: couldn't find tokenizer for model {embedding_model}, using default tokenizer {EMBEDDING_TO_TOKENIZER_DEFAULT}")
encoding = tiktoken.get_encoding(EMBEDDING_TO_TOKENIZER_DEFAULT)
num_tokens = len(encoding.encode(text))
# determine max length
if hasattr(encoding, "max_length"):
# TODO(fix) this is broken
max_length = encoding.max_length
else:
# TODO: figure out the real number
printd(f"Warning: couldn't find max_length for tokenizer {embedding_model}, using default max_length 8191")
max_length = 8191
# truncate text if too long
if num_tokens > max_length:
print(f"Warning: text is too long ({num_tokens} tokens), truncating to {max_length} tokens.")
# First, apply any necessary formatting
formatted_text = format_text(text, embedding_model)
# Then truncate
text = truncate_text(formatted_text, max_length, encoding)
return [text]
class EmbeddingEndpoint:
"""Implementation for OpenAI compatible endpoint"""
# """ Based off llama index https://github.com/run-llama/llama_index/blob/a98bdb8ecee513dc2e880f56674e7fd157d1dc3a/llama_index/embeddings/text_embeddings_inference.py """
# _user: str = PrivateAttr()
# _timeout: float = PrivateAttr()
# _base_url: str = PrivateAttr()
def __init__(
self,
model: str,
base_url: str,
user: str,
timeout: float = 60.0,
**kwargs: Any,
):
if not is_valid_url(base_url):
raise ValueError(
f"Embeddings endpoint was provided an invalid URL (set to: '{base_url}'). Make sure embedding_endpoint is set correctly in your MemGPT config."
)
self.model_name = model
self._user = user
self._base_url = base_url
self._timeout = timeout
def _call_api(self, text: str) -> List[float]:
if not is_valid_url(self._base_url):
raise ValueError(
f"Embeddings endpoint does not have a valid URL (set to: '{self._base_url}'). Make sure embedding_endpoint is set correctly in your MemGPT config."
)
import httpx
headers = {"Content-Type": "application/json"}
json_data = {"input": text, "model": self.model_name, "user": self._user}
with httpx.Client() as client:
response = client.post(
f"{self._base_url}/embeddings",
headers=headers,
json=json_data,
timeout=self._timeout,
)
response_json = response.json()
if isinstance(response_json, list):
# embedding directly in response
embedding = response_json
elif isinstance(response_json, dict):
# TEI embedding packaged inside openai-style response
try:
embedding = response_json["data"][0]["embedding"]
except (KeyError, IndexError):
raise TypeError(f"Got back an unexpected payload from text embedding function, response=\n{response_json}")
else:
# unknown response, can't parse
raise TypeError(f"Got back an unexpected payload from text embedding function, response=\n{response_json}")
return embedding
def get_text_embedding(self, text: str) -> List[float]:
return self._call_api(text)
def default_embedding_model():
# default to hugging face model running local
# warning: this is a terrible model
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
os.environ["TOKENIZERS_PARALLELISM"] = "False"
model = "BAAI/bge-small-en-v1.5"
return HuggingFaceEmbedding(model_name=model)
def query_embedding(embedding_model, query_text: str):
"""Generate padded embedding for querying database"""
query_vec = embedding_model.get_text_embedding(query_text)
query_vec = np.array(query_vec)
query_vec = np.pad(query_vec, (0, MAX_EMBEDDING_DIM - query_vec.shape[0]), mode="constant").tolist()
return query_vec
def embedding_model(config: EmbeddingConfig, user_id: Optional[uuid.UUID] = None):
"""Return LlamaIndex embedding model to use for embeddings"""
endpoint_type = config.embedding_endpoint_type
# TODO refactor to pass credentials through args
credentials = MemGPTCredentials.load()
if endpoint_type == "openai":
assert credentials.openai_key is not None
from llama_index.embeddings.openai import OpenAIEmbedding
additional_kwargs = {"user_id": user_id} if user_id else {}
model = OpenAIEmbedding(
api_base=config.embedding_endpoint,
api_key=credentials.openai_key,
additional_kwargs=additional_kwargs,
)
return model
elif endpoint_type == "azure":
assert all(
[
credentials.azure_key is not None,
credentials.azure_embedding_endpoint is not None,
credentials.azure_version is not None,
]
)
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
# https://learn.microsoft.com/en-us/azure/ai-services/openai/reference#embeddings
model = "text-embedding-ada-002"
deployment = credentials.azure_embedding_deployment if credentials.azure_embedding_deployment is not None else model
return AzureOpenAIEmbedding(
model=model,
deployment_name=deployment,
api_key=credentials.azure_key,
azure_endpoint=credentials.azure_endpoint,
api_version=credentials.azure_version,
)
elif endpoint_type == "hugging-face":
return EmbeddingEndpoint(
model=config.embedding_model,
base_url=config.embedding_endpoint,
user=user_id,
)
else:
return default_embedding_model()
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"llama_index.embeddings.huggingface.HuggingFaceEmbedding",
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"llama_index.core.Document",
"llama_index.embeddings.openai.OpenAIEmbedding"
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import logging
import os
from typing import Optional
from typing import Type
import openai
from langchain.chat_models import ChatOpenAI
from llama_index import VectorStoreIndex, LLMPredictor, ServiceContext
from llama_index.vector_stores.types import ExactMatchFilter, MetadataFilters
from pydantic import BaseModel, Field
from superagi.config.config import get_config
from superagi.llms.base_llm import BaseLlm
from superagi.resource_manager.llama_vector_store_factory import LlamaVectorStoreFactory
from superagi.tools.base_tool import BaseTool
from superagi.types.vector_store_types import VectorStoreType
from superagi.vector_store.chromadb import ChromaDB
class QueryResource(BaseModel):
"""Input for QueryResource tool."""
query: str = Field(..., description="the search query to search resources")
class QueryResourceTool(BaseTool):
"""
Read File tool
Attributes:
name : The name.
description : The description.
args_schema : The args schema.
"""
name: str = "QueryResource"
args_schema: Type[BaseModel] = QueryResource
description: str = "Tool searches resources content and extracts relevant information to perform the given task." \
"Tool is given preference over other search/read file tools for relevant data." \
"Resources content is taken from the files: {summary}"
agent_id: int = None
llm: Optional[BaseLlm] = None
def _execute(self, query: str):
openai.api_key = self.llm.get_api_key()
os.environ["OPENAI_API_KEY"] = self.llm.get_api_key()
llm_predictor_chatgpt = LLMPredictor(llm=ChatOpenAI(temperature=0, model_name=self.llm.get_model(),
openai_api_key=get_config("OPENAI_API_KEY")))
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor_chatgpt)
vector_store_name = VectorStoreType.get_vector_store_type(
self.get_tool_config(key="RESOURCE_VECTOR_STORE") or "Redis")
vector_store_index_name = self.get_tool_config(key="RESOURCE_VECTOR_STORE_INDEX_NAME") or "super-agent-index"
logging.info(f"vector_store_name {vector_store_name}")
logging.info(f"vector_store_index_name {vector_store_index_name}")
vector_store = LlamaVectorStoreFactory(vector_store_name, vector_store_index_name).get_vector_store()
logging.info(f"vector_store {vector_store}")
as_query_engine_args = dict(
filters=MetadataFilters(
filters=[
ExactMatchFilter(
key="agent_id",
value=str(self.agent_id)
)
]
)
)
if vector_store_name == VectorStoreType.CHROMA:
as_query_engine_args["chroma_collection"] = ChromaDB.create_collection(
collection_name=vector_store_index_name)
index = VectorStoreIndex.from_vector_store(vector_store=vector_store, service_context=service_context)
query_engine = index.as_query_engine(
**as_query_engine_args
)
try:
response = query_engine.query(query)
except ValueError as e:
logging.error(f"ValueError {e}")
response = "Document not found"
return response
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"llama_index.VectorStoreIndex.from_vector_store",
"llama_index.ServiceContext.from_defaults"
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'''
Below helper functions are implemented in this script:
build_sentence_window_index - VectorStore Index for Sentence window RAG technique
get_sentence_window_query_engine - query enginer for the above index
build_automerging_index - VectorStore Index for Auto-merging RAG technique
get_automerging_query_engine - query enginer for the above index
Evaluation function:
get_prebuilt_trulens_recorder - evaluation function with all the feedback functions
'''
import os
import numpy as np
from llama_index import ServiceContext, VectorStoreIndex, StorageContext, load_index_from_storage
from llama_index.node_parser import SentenceWindowNodeParser, HierarchicalNodeParser, get_leaf_nodes
from llama_index.indices.postprocessor import MetadataReplacementPostProcessor, SentenceTransformerRerank
from llama_index.retrievers import AutoMergingRetriever
from llama_index.query_engine import RetrieverQueryEngine
from trulens_eval import Feedback, TruLlama
from trulens_eval import OpenAI as fOpenAI
from trulens_eval.feedback import Groundedness
############################################################################## Function 1 ###########################################################
def build_sentence_window_index(
documents,
llm,
embed_model="local:BAAI/bge-small-en-v1.5",
sentence_window_size=3,
save_dir="sentence_index",
):
# create the sentence window node parser w/ default settings
node_parser = SentenceWindowNodeParser.from_defaults(
window_size=sentence_window_size,
window_metadata_key="window",
original_text_metadata_key="original_text",
)
sentence_context = ServiceContext.from_defaults(
llm=llm,
embed_model=embed_model,
node_parser=node_parser,
)
if not os.path.exists(save_dir):
sentence_index = VectorStoreIndex.from_documents(
documents, service_context=sentence_context
)
sentence_index.storage_context.persist(persist_dir=save_dir)
else:
sentence_index = load_index_from_storage(
StorageContext.from_defaults(persist_dir=save_dir),
service_context=sentence_context,
)
return sentence_index
############################################################################## Function 2 ###########################################################
def get_sentence_window_query_engine(
sentence_index, similarity_top_k=6, rerank_top_n=2
):
# define postprocessors
postproc = MetadataReplacementPostProcessor(target_metadata_key="window")
rerank = SentenceTransformerRerank(
top_n=rerank_top_n, model="BAAI/bge-reranker-base"
)
sentence_window_engine = sentence_index.as_query_engine(
similarity_top_k=similarity_top_k, node_postprocessors=[postproc, rerank]
)
return sentence_window_engine
############################################################################## Function 3 ###########################################################
def build_automerging_index(
documents,
llm,
embed_model="local:BAAI/bge-small-en-v1.5",
save_dir="merging_index",
chunk_sizes=None
):
# chunk sizes for all the layers (factor of 4)
chunk_sizes = chunk_sizes or [2048, 512, 128]
# Hierarchical node parser to parse the tree nodes (parent and children)
node_parser = HierarchicalNodeParser.from_defaults(chunk_sizes=chunk_sizes)
# getting all intermediate and parent nodes
nodes = node_parser.get_nodes_from_documents(documents)
# getting only the leaf nodes
leaf_nodes = get_leaf_nodes(nodes)
# required service context to initialize both llm and embed model
merging_context = ServiceContext.from_defaults(
llm=llm,
embed_model=embed_model
)
# storage context to store the intermediate and parent nodes in a docstore, because the index is built only on the leaf nodes
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
if not os.path.exists(save_dir):
automerging_index = VectorStoreIndex(
leaf_nodes, storage_context=storage_context, service_context=merging_context
)
automerging_index.storage_context.persist(persist_dir=save_dir)
else:
automerging_index = load_index_from_storage(
StorageContext.from_defaults(persist_dir=save_dir),
service_context=merging_context
)
return automerging_index
############################################################################## Function 4 ###########################################################
def get_automerging_query_engine(
automerging_index,
similarity_top_k=12,
rerank_top_n=6,
):
# retriever is used to merge the child nodes into the parent nodes
base_retriever = automerging_index.as_retriever(similarity_top_k=similarity_top_k)
retriever = AutoMergingRetriever(
base_retriever, automerging_index.storage_context, verbose=True
)
# Ranking is used to select top k relevant chunks from similarity_top_k
rerank = SentenceTransformerRerank(
top_n=rerank_top_n, model='BAAI/bge-reranker-base'
)
# getting query engine with the above mentioned retiriever and reranker
automerging_engine = RetrieverQueryEngine.from_args(
retriever, node_postprocessors=[rerank]
)
return automerging_engine
############################################################################## Function 5 ###########################################################
def get_prebuilt_trulens_recorder(query_engine, app_id):
# Feedback functions
# Answer Relevance
provider = fOpenAI()
f_qa_relevance = Feedback(
provider.relevance_with_cot_reasons,
name="Answer Relevance"
).on_input_output()
# Context Relevance
context_selection = TruLlama.select_source_nodes().node.text
f_qs_relevance = (
Feedback(provider.qs_relevance,
name="Context Relevance")
.on_input()
.on(context_selection)
.aggregate(np.mean)
)
# Groundedness
grounded = Groundedness(groundedness_provider=provider)
f_groundedness = (
Feedback(grounded.groundedness_measure_with_cot_reasons,
name="Groundedness"
)
.on(context_selection)
.on_output()
.aggregate(grounded.grounded_statements_aggregator)
)
tru_recorder = TruLlama(
query_engine,
app_id=app_id,
feedbacks = [
f_qa_relevance,
f_qs_relevance,
f_groundedness
]
)
return tru_recorder
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from typing import Callable, List
def split_text_keep_separator(text: str, separator: str) -> List[str]:
"""Split text with separator and keep the separator at the end of each split."""
parts = text.split(separator)
result = [separator + s if i > 0 else s for i, s in enumerate(parts)]
return [s for s in result if s]
def split_by_sep(sep: str, keep_sep: bool = True) -> Callable[[str], List[str]]:
"""Split text by separator."""
if keep_sep:
return lambda text: split_text_keep_separator(text, sep)
else:
return lambda text: text.split(sep)
def split_by_char() -> Callable[[str], List[str]]:
"""Split text by character."""
return lambda text: list(text)
def split_by_sentence_tokenizer() -> Callable[[str], List[str]]:
import os
import nltk
from llama_index.utils import get_cache_dir
cache_dir = get_cache_dir()
nltk_data_dir = os.environ.get("NLTK_DATA", cache_dir)
# update nltk path for nltk so that it finds the data
if nltk_data_dir not in nltk.data.path:
nltk.data.path.append(nltk_data_dir)
try:
nltk.data.find("tokenizers/punkt")
except LookupError:
nltk.download("punkt", download_dir=nltk_data_dir)
tokenizer = nltk.tokenize.PunktSentenceTokenizer()
# get the spans and then return the sentences
# using the start index of each span
# instead of using end, use the start of the next span if available
def split(text: str) -> List[str]:
spans = list(tokenizer.span_tokenize(text))
sentences = []
for i, span in enumerate(spans):
start = span[0]
if i < len(spans) - 1:
end = spans[i + 1][0]
else:
end = len(text)
sentences.append(text[start:end])
return sentences
return split
def split_by_regex(regex: str) -> Callable[[str], List[str]]:
"""Split text by regex."""
import re
return lambda text: re.findall(regex, text)
def split_by_phrase_regex() -> Callable[[str], List[str]]:
"""Split text by phrase regex.
This regular expression will split the sentences into phrases,
where each phrase is a sequence of one or more non-comma,
non-period, and non-semicolon characters, followed by an optional comma,
period, or semicolon. The regular expression will also capture the
delimiters themselves as separate items in the list of phrases.
"""
regex = "[^,.;。]+[,.;。]?"
return split_by_regex(regex)
| [
"llama_index.utils.get_cache_dir"
] | [((876, 891), 'llama_index.utils.get_cache_dir', 'get_cache_dir', ([], {}), '()\n', (889, 891), False, 'from llama_index.utils import get_cache_dir\n'), ((912, 950), 'os.environ.get', 'os.environ.get', (['"""NLTK_DATA"""', 'cache_dir'], {}), "('NLTK_DATA', cache_dir)\n", (926, 950), False, 'import os\n'), ((1252, 1290), 'nltk.tokenize.PunktSentenceTokenizer', 'nltk.tokenize.PunktSentenceTokenizer', ([], {}), '()\n', (1288, 1290), False, 'import nltk\n'), ((1062, 1098), 'nltk.data.path.append', 'nltk.data.path.append', (['nltk_data_dir'], {}), '(nltk_data_dir)\n', (1083, 1098), False, 'import nltk\n'), ((1117, 1151), 'nltk.data.find', 'nltk.data.find', (['"""tokenizers/punkt"""'], {}), "('tokenizers/punkt')\n", (1131, 1151), False, 'import nltk\n'), ((1985, 2008), 're.findall', 're.findall', (['regex', 'text'], {}), '(regex, text)\n', (1995, 2008), False, 'import re\n'), ((1184, 1234), 'nltk.download', 'nltk.download', (['"""punkt"""'], {'download_dir': 'nltk_data_dir'}), "('punkt', download_dir=nltk_data_dir)\n", (1197, 1234), False, 'import nltk\n')] |
#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import torch
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from sqlalchemy import make_url
from llama_index.vector_stores.postgres import PGVectorStore
# from llama_index.llms.llama_cpp import LlamaCPP
import psycopg2
from pathlib import Path
from llama_index.readers.file import PyMuPDFReader
from llama_index.core.schema import NodeWithScore
from typing import Optional
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core import QueryBundle
from llama_index.core.retrievers import BaseRetriever
from typing import Any, List
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.vector_stores import VectorStoreQuery
import argparse
def load_vector_database(username, password):
db_name = "example_db"
host = "localhost"
password = password
port = "5432"
user = username
# conn = psycopg2.connect(connection_string)
conn = psycopg2.connect(
dbname="postgres",
host=host,
password=password,
port=port,
user=user,
)
conn.autocommit = True
with conn.cursor() as c:
c.execute(f"DROP DATABASE IF EXISTS {db_name}")
c.execute(f"CREATE DATABASE {db_name}")
vector_store = PGVectorStore.from_params(
database=db_name,
host=host,
password=password,
port=port,
user=user,
table_name="llama2_paper",
embed_dim=384, # openai embedding dimension
)
return vector_store
def load_data(data_path):
loader = PyMuPDFReader()
documents = loader.load(file_path=data_path)
text_parser = SentenceSplitter(
chunk_size=1024,
# separator=" ",
)
text_chunks = []
# maintain relationship with source doc index, to help inject doc metadata in (3)
doc_idxs = []
for doc_idx, doc in enumerate(documents):
cur_text_chunks = text_parser.split_text(doc.text)
text_chunks.extend(cur_text_chunks)
doc_idxs.extend([doc_idx] * len(cur_text_chunks))
from llama_index.core.schema import TextNode
nodes = []
for idx, text_chunk in enumerate(text_chunks):
node = TextNode(
text=text_chunk,
)
src_doc = documents[doc_idxs[idx]]
node.metadata = src_doc.metadata
nodes.append(node)
return nodes
class VectorDBRetriever(BaseRetriever):
"""Retriever over a postgres vector store."""
def __init__(
self,
vector_store: PGVectorStore,
embed_model: Any,
query_mode: str = "default",
similarity_top_k: int = 2,
) -> None:
"""Init params."""
self._vector_store = vector_store
self._embed_model = embed_model
self._query_mode = query_mode
self._similarity_top_k = similarity_top_k
super().__init__()
def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]:
"""Retrieve."""
query_embedding = self._embed_model.get_query_embedding(
query_bundle.query_str
)
vector_store_query = VectorStoreQuery(
query_embedding=query_embedding,
similarity_top_k=self._similarity_top_k,
mode=self._query_mode,
)
query_result = self._vector_store.query(vector_store_query)
nodes_with_scores = []
for index, node in enumerate(query_result.nodes):
score: Optional[float] = None
if query_result.similarities is not None:
score = query_result.similarities[index]
nodes_with_scores.append(NodeWithScore(node=node, score=score))
return nodes_with_scores
def completion_to_prompt(completion):
return f"<|system|>\n</s>\n<|user|>\n{completion}</s>\n<|assistant|>\n"
# Transform a list of chat messages into zephyr-specific input
def messages_to_prompt(messages):
prompt = ""
for message in messages:
if message.role == "system":
prompt += f"<|system|>\n{message.content}</s>\n"
elif message.role == "user":
prompt += f"<|user|>\n{message.content}</s>\n"
elif message.role == "assistant":
prompt += f"<|assistant|>\n{message.content}</s>\n"
# ensure we start with a system prompt, insert blank if needed
if not prompt.startswith("<|system|>\n"):
prompt = "<|system|>\n</s>\n" + prompt
# add final assistant prompt
prompt = prompt + "<|assistant|>\n"
return prompt
def main(args):
embed_model = HuggingFaceEmbedding(model_name=args.embedding_model_path)
# Use custom LLM in BigDL
from bigdl.llm.llamaindex.llms import BigdlLLM
llm = BigdlLLM(
model_name=args.model_path,
tokenizer_name=args.model_path,
context_window=512,
max_new_tokens=args.n_predict,
generate_kwargs={"temperature": 0.7, "do_sample": False},
model_kwargs={},
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
device_map="xpu",
)
vector_store = load_vector_database(username=args.user, password=args.password)
nodes = load_data(data_path=args.data)
for node in nodes:
node_embedding = embed_model.get_text_embedding(
node.get_content(metadata_mode="all")
)
node.embedding = node_embedding
vector_store.add(nodes)
# query_str = "Can you tell me about the key concepts for safety finetuning"
query_str = "Explain about the training data for Llama 2"
query_embedding = embed_model.get_query_embedding(query_str)
# construct vector store query
query_mode = "default"
# query_mode = "sparse"
# query_mode = "hybrid"
vector_store_query = VectorStoreQuery(
query_embedding=query_embedding, similarity_top_k=2, mode=query_mode
)
# returns a VectorStoreQueryResult
query_result = vector_store.query(vector_store_query)
# print("Retrieval Results: ")
# print(query_result.nodes[0].get_content())
nodes_with_scores = []
for index, node in enumerate(query_result.nodes):
score: Optional[float] = None
if query_result.similarities is not None:
score = query_result.similarities[index]
nodes_with_scores.append(NodeWithScore(node=node, score=score))
retriever = VectorDBRetriever(
vector_store, embed_model, query_mode="default", similarity_top_k=1
)
query_engine = RetrieverQueryEngine.from_args(retriever, llm=llm)
# query_str = "How does Llama 2 perform compared to other open-source models?"
query_str = args.question
response = query_engine.query(query_str)
print("------------RESPONSE GENERATION---------------------")
print(str(response))
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='LlamaIndex BigdlLLM Example')
parser.add_argument('-m','--model-path', type=str, required=True,
help='the path to transformers model')
parser.add_argument('-q', '--question', type=str, default='How does Llama 2 perform compared to other open-source models?',
help='qustion you want to ask.')
parser.add_argument('-d','--data',type=str, default='./data/llama2.pdf',
help="the data used during retrieval")
parser.add_argument('-u', '--user', type=str, required=True,
help="user name in the database postgres")
parser.add_argument('-p','--password', type=str, required=True,
help="the password of the user in the database")
parser.add_argument('-e','--embedding-model-path',default="BAAI/bge-small-en",
help="the path to embedding model path")
parser.add_argument('-n','--n-predict', type=int, default=32,
help='max number of predict tokens')
args = parser.parse_args()
main(args) | [
"llama_index.core.node_parser.SentenceSplitter",
"llama_index.embeddings.huggingface.HuggingFaceEmbedding",
"llama_index.readers.file.PyMuPDFReader",
"llama_index.core.schema.TextNode",
"llama_index.vector_stores.postgres.PGVectorStore.from_params",
"llama_index.core.query_engine.RetrieverQueryEngine.from_args",
"llama_index.core.schema.NodeWithScore",
"llama_index.core.vector_stores.VectorStoreQuery"
] | [((1521, 1612), 'psycopg2.connect', 'psycopg2.connect', ([], {'dbname': '"""postgres"""', 'host': 'host', 'password': 'password', 'port': 'port', 'user': 'user'}), "(dbname='postgres', host=host, password=password, port=port,\n user=user)\n", (1537, 1612), False, 'import psycopg2\n'), ((1841, 1982), 'llama_index.vector_stores.postgres.PGVectorStore.from_params', 'PGVectorStore.from_params', ([], {'database': 'db_name', 'host': 'host', 'password': 'password', 'port': 'port', 'user': 'user', 'table_name': '"""llama2_paper"""', 'embed_dim': '(384)'}), "(database=db_name, host=host, password=password,\n port=port, user=user, table_name='llama2_paper', embed_dim=384)\n", (1866, 1982), False, 'from llama_index.vector_stores.postgres import PGVectorStore\n'), ((2137, 2152), 'llama_index.readers.file.PyMuPDFReader', 'PyMuPDFReader', ([], {}), '()\n', (2150, 2152), False, 'from llama_index.readers.file import PyMuPDFReader\n'), ((2222, 2255), 'llama_index.core.node_parser.SentenceSplitter', 'SentenceSplitter', ([], {'chunk_size': '(1024)'}), '(chunk_size=1024)\n', (2238, 2255), False, 'from llama_index.core.node_parser import SentenceSplitter\n'), ((5116, 5174), 'llama_index.embeddings.huggingface.HuggingFaceEmbedding', 'HuggingFaceEmbedding', ([], {'model_name': 'args.embedding_model_path'}), '(model_name=args.embedding_model_path)\n', (5136, 5174), False, 'from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n'), ((5271, 5583), 'bigdl.llm.llamaindex.llms.BigdlLLM', 'BigdlLLM', ([], {'model_name': 'args.model_path', 'tokenizer_name': 'args.model_path', 'context_window': '(512)', 'max_new_tokens': 'args.n_predict', 'generate_kwargs': "{'temperature': 0.7, 'do_sample': False}", 'model_kwargs': '{}', 'messages_to_prompt': 'messages_to_prompt', 'completion_to_prompt': 'completion_to_prompt', 'device_map': '"""xpu"""'}), "(model_name=args.model_path, tokenizer_name=args.model_path,\n context_window=512, max_new_tokens=args.n_predict, generate_kwargs={\n 'temperature': 0.7, 'do_sample': False}, model_kwargs={},\n messages_to_prompt=messages_to_prompt, completion_to_prompt=\n completion_to_prompt, device_map='xpu')\n", (5279, 5583), False, 'from bigdl.llm.llamaindex.llms import BigdlLLM\n'), ((6353, 6444), 'llama_index.core.vector_stores.VectorStoreQuery', 'VectorStoreQuery', ([], {'query_embedding': 'query_embedding', 'similarity_top_k': '(2)', 'mode': 'query_mode'}), '(query_embedding=query_embedding, similarity_top_k=2, mode=\n query_mode)\n', (6369, 6444), False, 'from llama_index.core.vector_stores import VectorStoreQuery\n'), ((7083, 7133), 'llama_index.core.query_engine.RetrieverQueryEngine.from_args', 'RetrieverQueryEngine.from_args', (['retriever'], {'llm': 'llm'}), '(retriever, llm=llm)\n', (7113, 7133), False, 'from llama_index.core.query_engine import RetrieverQueryEngine\n'), ((7428, 7494), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""LlamaIndex BigdlLLM Example"""'}), "(description='LlamaIndex BigdlLLM Example')\n", (7451, 7494), False, 'import argparse\n'), ((2759, 2784), 'llama_index.core.schema.TextNode', 'TextNode', ([], {'text': 'text_chunk'}), '(text=text_chunk)\n', (2767, 2784), False, 'from llama_index.core.schema import TextNode\n'), ((3682, 3800), 'llama_index.core.vector_stores.VectorStoreQuery', 'VectorStoreQuery', ([], {'query_embedding': 'query_embedding', 'similarity_top_k': 'self._similarity_top_k', 'mode': 'self._query_mode'}), '(query_embedding=query_embedding, similarity_top_k=self.\n _similarity_top_k, mode=self._query_mode)\n', (3698, 3800), False, 'from llama_index.core.vector_stores import VectorStoreQuery\n'), ((6893, 6930), 'llama_index.core.schema.NodeWithScore', 'NodeWithScore', ([], {'node': 'node', 'score': 'score'}), '(node=node, score=score)\n', (6906, 6930), False, 'from llama_index.core.schema import NodeWithScore\n'), ((4191, 4228), 'llama_index.core.schema.NodeWithScore', 'NodeWithScore', ([], {'node': 'node', 'score': 'score'}), '(node=node, score=score)\n', (4204, 4228), False, 'from llama_index.core.schema import NodeWithScore\n')] |
import os
import logging
import hashlib
import random
import uuid
import openai
from pathlib import Path
from llama_index import ServiceContext, GPTVectorStoreIndex, LLMPredictor, RssReader, SimpleDirectoryReader, StorageContext, load_index_from_storage
from llama_index.readers.schema.base import Document
from langchain.chat_models import ChatOpenAI
from azure.cognitiveservices.speech import SpeechConfig, SpeechSynthesizer, ResultReason, CancellationReason, SpeechSynthesisOutputFormat
from azure.cognitiveservices.speech.audio import AudioOutputConfig
from app.fetch_web_post import get_urls, get_youtube_transcript, scrape_website, scrape_website_by_phantomjscloud
from app.prompt import get_prompt_template
from app.util import get_language_code, get_youtube_video_id
OPENAI_API_KEY = os.environ.get('OPENAI_API_KEY')
SPEECH_KEY = os.environ.get('SPEECH_KEY')
SPEECH_REGION = os.environ.get('SPEECH_REGION')
openai.api_key = OPENAI_API_KEY
index_cache_web_dir = Path('/tmp/myGPTReader/cache_web/')
index_cache_file_dir = Path('/data/myGPTReader/file/')
index_cache_voice_dir = Path('/tmp/myGPTReader/voice/')
if not index_cache_web_dir.is_dir():
index_cache_web_dir.mkdir(parents=True, exist_ok=True)
if not index_cache_voice_dir.is_dir():
index_cache_voice_dir.mkdir(parents=True, exist_ok=True)
if not index_cache_file_dir.is_dir():
index_cache_file_dir.mkdir(parents=True, exist_ok=True)
llm_predictor = LLMPredictor(llm=ChatOpenAI(
temperature=0, model_name="gpt-3.5-turbo"))
service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor)
web_storage_context = StorageContext.from_defaults()
file_storage_context = StorageContext.from_defaults()
def get_unique_md5(urls):
urls_str = ''.join(sorted(urls))
hashed_str = hashlib.md5(urls_str.encode('utf-8')).hexdigest()
return hashed_str
def format_dialog_messages(messages):
return "\n".join(messages)
def get_document_from_youtube_id(video_id):
if video_id is None:
return None
transcript = get_youtube_transcript(video_id)
if transcript is None:
return None
return Document(transcript)
def remove_prompt_from_text(text):
return text.replace('chatGPT:', '').strip()
def get_documents_from_urls(urls):
documents = []
for url in urls['page_urls']:
document = Document(scrape_website(url))
documents.append(document)
if len(urls['rss_urls']) > 0:
rss_documents = RssReader().load_data(urls['rss_urls'])
documents = documents + rss_documents
if len(urls['phantomjscloud_urls']) > 0:
for url in urls['phantomjscloud_urls']:
document = Document(scrape_website_by_phantomjscloud(url))
documents.append(document)
if len(urls['youtube_urls']) > 0:
for url in urls['youtube_urls']:
video_id = get_youtube_video_id(url)
document = get_document_from_youtube_id(video_id)
if (document is not None):
documents.append(document)
else:
documents.append(Document(f"Can't get transcript from youtube video: {url}"))
return documents
def get_index_from_web_cache(name):
try:
index = load_index_from_storage(web_storage_context, index_id=name)
except Exception as e:
logging.error(e)
return None
return index
def get_index_from_file_cache(name):
try:
index = load_index_from_storage(file_storage_context, index_id=name)
except Exception as e:
logging.error(e)
return None
return index
def get_index_name_from_file(file: str):
file_md5_with_extension = str(Path(file).relative_to(index_cache_file_dir).name)
file_md5 = file_md5_with_extension.split('.')[0]
return file_md5
def get_answer_from_chatGPT(messages):
dialog_messages = format_dialog_messages(messages)
logging.info('=====> Use chatGPT to answer!')
logging.info(dialog_messages)
completion = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
messages=[{"role": "user", "content": dialog_messages}]
)
logging.info(completion.usage)
total_tokens = completion.usage.total_tokens
return completion.choices[0].message.content, total_tokens, None
def get_answer_from_llama_web(messages, urls):
dialog_messages = format_dialog_messages(messages)
lang_code = get_language_code(remove_prompt_from_text(messages[-1]))
combained_urls = get_urls(urls)
logging.info(combained_urls)
index_file_name = get_unique_md5(urls)
index = get_index_from_web_cache(index_file_name)
if index is None:
logging.info(f"=====> Build index from web!")
documents = get_documents_from_urls(combained_urls)
logging.info(documents)
index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context)
index.set_index_id(index_file_name)
index.storage_context.persist()
logging.info(
f"=====> Save index to disk path: {index_cache_web_dir / index_file_name}")
prompt = get_prompt_template(lang_code)
logging.info('=====> Use llama web with chatGPT to answer!')
logging.info('=====> dialog_messages')
logging.info(dialog_messages)
logging.info('=====> text_qa_template')
logging.info(prompt.prompt)
answer = index.as_query_engine(text_qa_template=prompt).query(dialog_messages)
total_llm_model_tokens = llm_predictor.last_token_usage
total_embedding_model_tokens = service_context.embed_model.last_token_usage
return answer, total_llm_model_tokens, total_embedding_model_tokens
def get_answer_from_llama_file(messages, file):
dialog_messages = format_dialog_messages(messages)
lang_code = get_language_code(remove_prompt_from_text(messages[-1]))
index_name = get_index_name_from_file(file)
index = get_index_from_file_cache(index_name)
if index is None:
logging.info(f"=====> Build index from file!")
documents = SimpleDirectoryReader(input_files=[file]).load_data()
index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context)
index.set_index_id(index_name)
index.storage_context.persist()
logging.info(
f"=====> Save index to disk path: {index_cache_file_dir / index_name}")
prompt = get_prompt_template(lang_code)
logging.info('=====> Use llama file with chatGPT to answer!')
logging.info('=====> dialog_messages')
logging.info(dialog_messages)
logging.info('=====> text_qa_template')
logging.info(prompt)
answer = answer = index.as_query_engine(text_qa_template=prompt).query(dialog_messages)
total_llm_model_tokens = llm_predictor.last_token_usage
total_embedding_model_tokens = service_context.embed_model.last_token_usage
return answer, total_llm_model_tokens, total_embedding_model_tokens
def get_text_from_whisper(voice_file_path):
with open(voice_file_path, "rb") as f:
transcript = openai.Audio.transcribe("whisper-1", f)
return transcript.text
lang_code_voice_map = {
'zh': ['zh-CN-XiaoxiaoNeural', 'zh-CN-XiaohanNeural', 'zh-CN-YunxiNeural', 'zh-CN-YunyangNeural'],
'en': ['en-US-JennyNeural', 'en-US-RogerNeural', 'en-IN-NeerjaNeural', 'en-IN-PrabhatNeural', 'en-AU-AnnetteNeural', 'en-AU-CarlyNeural', 'en-GB-AbbiNeural', 'en-GB-AlfieNeural'],
'ja': ['ja-JP-AoiNeural', 'ja-JP-DaichiNeural'],
'de': ['de-DE-AmalaNeural', 'de-DE-BerndNeural'],
}
def convert_to_ssml(text, voice_name=None):
try:
logging.info("=====> Convert text to ssml!")
logging.info(text)
text = remove_prompt_from_text(text)
lang_code = get_language_code(text)
if voice_name is None:
voice_name = random.choice(lang_code_voice_map[lang_code])
except Exception as e:
logging.warning(f"Error: {e}. Using default voice.")
voice_name = random.choice(lang_code_voice_map['zh'])
ssml = '<speak version="1.0" xmlns="http://www.w3.org/2001/10/synthesis" xml:lang="zh-CN">'
ssml += f'<voice name="{voice_name}">{text}</voice>'
ssml += '</speak>'
return ssml
def get_voice_file_from_text(text, voice_name=None):
speech_config = SpeechConfig(subscription=SPEECH_KEY, region=SPEECH_REGION)
speech_config.set_speech_synthesis_output_format(
SpeechSynthesisOutputFormat.Audio16Khz32KBitRateMonoMp3)
speech_config.speech_synthesis_language = "zh-CN"
file_name = f"{index_cache_voice_dir}{uuid.uuid4()}.mp3"
file_config = AudioOutputConfig(filename=file_name)
synthesizer = SpeechSynthesizer(
speech_config=speech_config, audio_config=file_config)
ssml = convert_to_ssml(text, voice_name)
result = synthesizer.speak_ssml_async(ssml).get()
if result.reason == ResultReason.SynthesizingAudioCompleted:
logging.info("Speech synthesized for text [{}], and the audio was saved to [{}]".format(
text, file_name))
elif result.reason == ResultReason.Canceled:
cancellation_details = result.cancellation_details
logging.info("Speech synthesis canceled: {}".format(
cancellation_details.reason))
if cancellation_details.reason == CancellationReason.Error:
logging.error("Error details: {}".format(
cancellation_details.error_details))
return file_name
| [
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"llama_index.ServiceContext.from_defaults",
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"llama_index.StorageContext.from_defaults",
"llama_index.SimpleDirectoryReader",
"llama_index.RssReader",
"llama_index.load_index_from_storage"
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"""Configuration."""
import streamlit as st
import os
### DEFINE BUILDER_LLM #####
## Uncomment the LLM you want to use to construct the meta agent
## OpenAI
from llama_index.llms import OpenAI
# set OpenAI Key - use Streamlit secrets
os.environ["OPENAI_API_KEY"] = st.secrets.openai_key
# load LLM
BUILDER_LLM = OpenAI(model="gpt-4-1106-preview")
# # Anthropic (make sure you `pip install anthropic`)
# from llama_index.llms import Anthropic
# # set Anthropic key
# os.environ["ANTHROPIC_API_KEY"] = st.secrets.anthropic_key
# BUILDER_LLM = Anthropic()
| [
"llama_index.llms.OpenAI"
] | [((316, 350), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'model': '"""gpt-4-1106-preview"""'}), "(model='gpt-4-1106-preview')\n", (322, 350), False, 'from llama_index.llms import OpenAI\n')] |
from memgpt.data_types import Passage, Document, EmbeddingConfig, Source
from memgpt.utils import create_uuid_from_string
from memgpt.agent_store.storage import StorageConnector, TableType
from memgpt.embeddings import embedding_model
from memgpt.data_types import Document, Passage
from typing import List, Iterator, Dict, Tuple, Optional
import typer
from llama_index.core import Document as LlamaIndexDocument
class DataConnector:
def generate_documents(self) -> Iterator[Tuple[str, Dict]]: # -> Iterator[Document]:
pass
def generate_passages(self, documents: List[Document], chunk_size: int = 1024) -> Iterator[Tuple[str, Dict]]: # -> Iterator[Passage]:
pass
def load_data(
connector: DataConnector,
source: Source,
embedding_config: EmbeddingConfig,
passage_store: StorageConnector,
document_store: Optional[StorageConnector] = None,
):
"""Load data from a connector (generates documents and passages) into a specified source_id, associatedw with a user_id."""
assert (
source.embedding_model == embedding_config.embedding_model
), f"Source and embedding config models must match, got: {source.embedding_model} and {embedding_config.embedding_model}"
assert (
source.embedding_dim == embedding_config.embedding_dim
), f"Source and embedding config dimensions must match, got: {source.embedding_dim} and {embedding_config.embedding_dim}."
# embedding model
embed_model = embedding_model(embedding_config)
# insert passages/documents
passages = []
passage_count = 0
document_count = 0
for document_text, document_metadata in connector.generate_documents():
# insert document into storage
document = Document(
id=create_uuid_from_string(f"{str(source.id)}_{document_text}"),
text=document_text,
metadata=document_metadata,
data_source=source.name,
user_id=source.user_id,
)
document_count += 1
if document_store:
document_store.insert(document)
# generate passages
for passage_text, passage_metadata in connector.generate_passages([document], chunk_size=embedding_config.embedding_chunk_size):
try:
embedding = embed_model.get_text_embedding(passage_text)
except Exception as e:
typer.secho(
f"Warning: Failed to get embedding for {passage_text} (error: {str(e)}), skipping insert into VectorDB.",
fg=typer.colors.YELLOW,
)
continue
passage = Passage(
id=create_uuid_from_string(f"{str(source.id)}_{passage_text}"),
text=passage_text,
doc_id=document.id,
metadata_=passage_metadata,
user_id=source.user_id,
data_source=source.name,
embedding_dim=source.embedding_dim,
embedding_model=source.embedding_model,
embedding=embedding,
)
passages.append(passage)
if len(passages) >= embedding_config.embedding_chunk_size:
# insert passages into passage store
passage_store.insert_many(passages)
passage_count += len(passages)
passages = []
if len(passages) > 0:
# insert passages into passage store
passage_store.insert_many(passages)
passage_count += len(passages)
return passage_count, document_count
class DirectoryConnector(DataConnector):
def __init__(self, input_files: List[str] = None, input_directory: str = None, recursive: bool = False, extensions: List[str] = None):
self.connector_type = "directory"
self.input_files = input_files
self.input_directory = input_directory
self.recursive = recursive
self.extensions = extensions
if self.recursive == True:
assert self.input_directory is not None, "Must provide input directory if recursive is True."
def generate_documents(self) -> Iterator[Tuple[str, Dict]]: # -> Iterator[Document]:
from llama_index.core import SimpleDirectoryReader
if self.input_directory is not None:
reader = SimpleDirectoryReader(
input_dir=self.input_directory,
recursive=self.recursive,
required_exts=[ext.strip() for ext in str(self.extensions).split(",")],
)
else:
assert self.input_files is not None, "Must provide input files if input_dir is None"
reader = SimpleDirectoryReader(input_files=[str(f) for f in self.input_files])
llama_index_docs = reader.load_data(show_progress=True)
for llama_index_doc in llama_index_docs:
# TODO: add additional metadata?
# doc = Document(text=llama_index_doc.text, metadata=llama_index_doc.metadata)
# docs.append(doc)
yield llama_index_doc.text, llama_index_doc.metadata
def generate_passages(self, documents: List[Document], chunk_size: int = 1024) -> Iterator[Tuple[str, Dict]]: # -> Iterator[Passage]:
# use llama index to run embeddings code
# from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.node_parser import TokenTextSplitter
parser = TokenTextSplitter(chunk_size=chunk_size)
for document in documents:
llama_index_docs = [LlamaIndexDocument(text=document.text, metadata=document.metadata)]
nodes = parser.get_nodes_from_documents(llama_index_docs)
for node in nodes:
# passage = Passage(
# text=node.text,
# doc_id=document.id,
# )
yield node.text, None
class WebConnector(DirectoryConnector):
def __init__(self, urls: List[str] = None, html_to_text: bool = True):
self.urls = urls
self.html_to_text = html_to_text
def generate_documents(self) -> Iterator[Tuple[str, Dict]]: # -> Iterator[Document]:
from llama_index.readers.web import SimpleWebPageReader
documents = SimpleWebPageReader(html_to_text=self.html_to_text).load_data(self.urls)
for document in documents:
yield document.text, {"url": document.id_}
class VectorDBConnector(DataConnector):
# NOTE: this class has not been properly tested, so is unlikely to work
# TODO: allow loading multiple tables (1:1 mapping between Document and Table)
def __init__(
self,
name: str,
uri: str,
table_name: str,
text_column: str,
embedding_column: str,
embedding_dim: int,
):
self.name = name
self.uri = uri
self.table_name = table_name
self.text_column = text_column
self.embedding_column = embedding_column
self.embedding_dim = embedding_dim
# connect to db table
from sqlalchemy import create_engine
self.engine = create_engine(uri)
def generate_documents(self) -> Iterator[Tuple[str, Dict]]: # -> Iterator[Document]:
yield self.table_name, None
def generate_passages(self, documents: List[Document], chunk_size: int = 1024) -> Iterator[Tuple[str, Dict]]: # -> Iterator[Passage]:
from sqlalchemy import select, MetaData, Table, Inspector
from pgvector.sqlalchemy import Vector
metadata = MetaData()
# Create an inspector to inspect the database
inspector = Inspector.from_engine(self.engine)
table_names = inspector.get_table_names()
assert self.table_name in table_names, f"Table {self.table_name} not found in database: tables that exist {table_names}."
table = Table(self.table_name, metadata, autoload_with=self.engine)
# Prepare a select statement
select_statement = select(table.c[self.text_column], table.c[self.embedding_column].cast(Vector(self.embedding_dim)))
# Execute the query and fetch the results
# TODO: paginate results
with self.engine.connect() as connection:
result = connection.execute(select_statement).fetchall()
for text, embedding in result:
# assume that embeddings are the same model as in config
# TODO: don't re-compute embedding
yield text, {"embedding": embedding}
| [
"llama_index.readers.web.SimpleWebPageReader",
"llama_index.core.node_parser.TokenTextSplitter",
"llama_index.core.Document"
] | [((1472, 1505), 'memgpt.embeddings.embedding_model', 'embedding_model', (['embedding_config'], {}), '(embedding_config)\n', (1487, 1505), False, 'from memgpt.embeddings import embedding_model\n'), ((5412, 5452), 'llama_index.core.node_parser.TokenTextSplitter', 'TokenTextSplitter', ([], {'chunk_size': 'chunk_size'}), '(chunk_size=chunk_size)\n', (5429, 5452), False, 'from llama_index.core.node_parser import TokenTextSplitter\n'), ((7087, 7105), 'sqlalchemy.create_engine', 'create_engine', (['uri'], {}), '(uri)\n', (7100, 7105), False, 'from sqlalchemy import create_engine\n'), ((7506, 7516), 'sqlalchemy.MetaData', 'MetaData', ([], {}), '()\n', (7514, 7516), False, 'from sqlalchemy import select, MetaData, Table, Inspector\n'), ((7591, 7625), 'sqlalchemy.Inspector.from_engine', 'Inspector.from_engine', (['self.engine'], {}), '(self.engine)\n', (7612, 7625), False, 'from sqlalchemy import select, MetaData, Table, Inspector\n'), ((7823, 7882), 'sqlalchemy.Table', 'Table', (['self.table_name', 'metadata'], {'autoload_with': 'self.engine'}), '(self.table_name, metadata, autoload_with=self.engine)\n', (7828, 7882), False, 'from sqlalchemy import select, MetaData, Table, Inspector\n'), ((5520, 5586), 'llama_index.core.Document', 'LlamaIndexDocument', ([], {'text': 'document.text', 'metadata': 'document.metadata'}), '(text=document.text, metadata=document.metadata)\n', (5538, 5586), True, 'from llama_index.core import Document as LlamaIndexDocument\n'), ((6221, 6272), 'llama_index.readers.web.SimpleWebPageReader', 'SimpleWebPageReader', ([], {'html_to_text': 'self.html_to_text'}), '(html_to_text=self.html_to_text)\n', (6240, 6272), False, 'from llama_index.readers.web import SimpleWebPageReader\n'), ((8018, 8044), 'pgvector.sqlalchemy.Vector', 'Vector', (['self.embedding_dim'], {}), '(self.embedding_dim)\n', (8024, 8044), False, 'from pgvector.sqlalchemy import Vector\n')] |
from typing import Dict, List, Type
from llama_index.agent import OpenAIAgent, ReActAgent
from llama_index.agent.types import BaseAgent
from llama_index.llms import Anthropic, OpenAI
from llama_index.llms.llama_utils import messages_to_prompt
from llama_index.llms.llm import LLM
from llama_index.llms.replicate import Replicate
OPENAI_MODELS = [
"text-davinci-003",
"gpt-3.5-turbo-0613",
"gpt-4-0613",
]
ANTHROPIC_MODELS = ["claude-instant-1", "claude-instant-1.2", "claude-2", "claude-2.0"]
LLAMA_MODELS = [
"llama13b-v2-chat",
"llama70b-v2-chat",
]
REPLICATE_MODELS: List[str] = []
ALL_MODELS = OPENAI_MODELS + ANTHROPIC_MODELS + LLAMA_MODELS
AGENTS: Dict[str, Type[BaseAgent]] = {
"react": ReActAgent,
"openai": OpenAIAgent,
}
LLAMA_13B_V2_CHAT = (
"a16z-infra/llama13b-v2-chat:"
"df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5"
)
LLAMA_70B_V2_CHAT = (
"replicate/llama70b-v2-chat:"
"e951f18578850b652510200860fc4ea62b3b16fac280f83ff32282f87bbd2e48"
)
def get_model(model: str) -> LLM:
llm: LLM
if model in OPENAI_MODELS:
llm = OpenAI(model=model)
elif model in ANTHROPIC_MODELS:
llm = Anthropic(model=model)
elif model in LLAMA_MODELS:
model_dict = {
"llama13b-v2-chat": LLAMA_13B_V2_CHAT,
"llama70b-v2-chat": LLAMA_70B_V2_CHAT,
}
replicate_model = model_dict[model]
llm = Replicate(
model=replicate_model,
temperature=0.01,
context_window=4096,
# override message representation for llama 2
messages_to_prompt=messages_to_prompt,
)
else:
raise ValueError(f"Unknown model {model}")
return llm
def is_valid_combination(agent: str, model: str) -> bool:
if agent == "openai" and model not in ["gpt-3.5-turbo-0613", "gpt-4-0613"]:
print(f"{agent} does not work with {model}")
return False
return True
| [
"llama_index.llms.OpenAI",
"llama_index.llms.Anthropic",
"llama_index.llms.replicate.Replicate"
] | [((1116, 1135), 'llama_index.llms.OpenAI', 'OpenAI', ([], {'model': 'model'}), '(model=model)\n', (1122, 1135), False, 'from llama_index.llms import Anthropic, OpenAI\n'), ((1186, 1208), 'llama_index.llms.Anthropic', 'Anthropic', ([], {'model': 'model'}), '(model=model)\n', (1195, 1208), False, 'from llama_index.llms import Anthropic, OpenAI\n'), ((1434, 1548), 'llama_index.llms.replicate.Replicate', 'Replicate', ([], {'model': 'replicate_model', 'temperature': '(0.01)', 'context_window': '(4096)', 'messages_to_prompt': 'messages_to_prompt'}), '(model=replicate_model, temperature=0.01, context_window=4096,\n messages_to_prompt=messages_to_prompt)\n', (1443, 1548), False, 'from llama_index.llms.replicate import Replicate\n')] |
import asyncio
import os
import shutil
from argparse import ArgumentParser
from glob import iglob
from pathlib import Path
from typing import Any, Callable, Dict, Optional, Union, cast
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex,
)
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.base.response.schema import (
RESPONSE_TYPE,
StreamingResponse,
Response,
)
from llama_index.core.bridge.pydantic import BaseModel, Field, validator
from llama_index.core.chat_engine import CondenseQuestionChatEngine
from llama_index.core.indices.service_context import ServiceContext
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.llms import LLM
from llama_index.core.query_engine import CustomQueryEngine
from llama_index.core.query_pipeline.components.function import FnComponent
from llama_index.core.query_pipeline.query import QueryPipeline
from llama_index.core.readers.base import BaseReader
from llama_index.core.response_synthesizers import CompactAndRefine
from llama_index.core.utils import get_cache_dir
def _try_load_openai_llm():
try:
from llama_index.llms.openai import OpenAI # pants: no-infer-dep
return OpenAI(model="gpt-3.5-turbo", streaming=True)
except ImportError:
raise ImportError(
"`llama-index-llms-openai` package not found, "
"please run `pip install llama-index-llms-openai`"
)
RAG_HISTORY_FILE_NAME = "files_history.txt"
def default_ragcli_persist_dir() -> str:
return str(Path(get_cache_dir()) / "rag_cli")
def query_input(query_str: Optional[str] = None) -> str:
return query_str or ""
class QueryPipelineQueryEngine(CustomQueryEngine):
query_pipeline: QueryPipeline = Field(
description="Query Pipeline to use for Q&A.",
)
def custom_query(self, query_str: str) -> RESPONSE_TYPE:
return self.query_pipeline.run(query_str=query_str)
async def acustom_query(self, query_str: str) -> RESPONSE_TYPE:
return await self.query_pipeline.arun(query_str=query_str)
class RagCLI(BaseModel):
"""
CLI tool for chatting with output of a IngestionPipeline via a QueryPipeline.
"""
ingestion_pipeline: IngestionPipeline = Field(
description="Ingestion pipeline to run for RAG ingestion."
)
verbose: bool = Field(
description="Whether to print out verbose information during execution.",
default=False,
)
persist_dir: str = Field(
description="Directory to persist ingestion pipeline.",
default_factory=default_ragcli_persist_dir,
)
llm: LLM = Field(
description="Language model to use for response generation.",
default_factory=lambda: _try_load_openai_llm(),
)
query_pipeline: Optional[QueryPipeline] = Field(
description="Query Pipeline to use for Q&A.",
default=None,
)
chat_engine: Optional[CondenseQuestionChatEngine] = Field(
description="Chat engine to use for chatting.",
default_factory=None,
)
file_extractor: Optional[Dict[str, BaseReader]] = Field(
description="File extractor to use for extracting text from files.",
default=None,
)
class Config:
arbitrary_types_allowed = True
@validator("query_pipeline", always=True)
def query_pipeline_from_ingestion_pipeline(
cls, query_pipeline: Any, values: Dict[str, Any]
) -> Optional[QueryPipeline]:
"""
If query_pipeline is not provided, create one from ingestion_pipeline.
"""
if query_pipeline is not None:
return query_pipeline
ingestion_pipeline = cast(IngestionPipeline, values["ingestion_pipeline"])
if ingestion_pipeline.vector_store is None:
return None
verbose = cast(bool, values["verbose"])
query_component = FnComponent(
fn=query_input, output_key="output", req_params={"query_str"}
)
llm = cast(LLM, values["llm"])
# get embed_model from transformations if possible
embed_model = None
if ingestion_pipeline.transformations is not None:
for transformation in ingestion_pipeline.transformations:
if isinstance(transformation, BaseEmbedding):
embed_model = transformation
break
service_context = ServiceContext.from_defaults(
llm=llm, embed_model=embed_model or "default"
)
retriever = VectorStoreIndex.from_vector_store(
ingestion_pipeline.vector_store, service_context=service_context
).as_retriever(similarity_top_k=8)
response_synthesizer = CompactAndRefine(
service_context=service_context, streaming=True, verbose=verbose
)
# define query pipeline
query_pipeline = QueryPipeline(verbose=verbose)
query_pipeline.add_modules(
{
"query": query_component,
"retriever": retriever,
"summarizer": response_synthesizer,
}
)
query_pipeline.add_link("query", "retriever")
query_pipeline.add_link("retriever", "summarizer", dest_key="nodes")
query_pipeline.add_link("query", "summarizer", dest_key="query_str")
return query_pipeline
@validator("chat_engine", always=True)
def chat_engine_from_query_pipeline(
cls, chat_engine: Any, values: Dict[str, Any]
) -> Optional[CondenseQuestionChatEngine]:
"""
If chat_engine is not provided, create one from query_pipeline.
"""
if chat_engine is not None:
return chat_engine
if values.get("query_pipeline", None) is None:
values["query_pipeline"] = cls.query_pipeline_from_ingestion_pipeline(
query_pipeline=None, values=values
)
query_pipeline = cast(QueryPipeline, values["query_pipeline"])
if query_pipeline is None:
return None
query_engine = QueryPipelineQueryEngine(query_pipeline=query_pipeline) # type: ignore
verbose = cast(bool, values["verbose"])
llm = cast(LLM, values["llm"])
return CondenseQuestionChatEngine.from_defaults(
query_engine=query_engine, llm=llm, verbose=verbose
)
async def handle_cli(
self,
files: Optional[str] = None,
question: Optional[str] = None,
chat: bool = False,
verbose: bool = False,
clear: bool = False,
create_llama: bool = False,
**kwargs: Dict[str, Any],
) -> None:
"""
Entrypoint for local document RAG CLI tool.
"""
if clear:
# delete self.persist_dir directory including all subdirectories and files
if os.path.exists(self.persist_dir):
# Ask for confirmation
response = input(
f"Are you sure you want to delete data within {self.persist_dir}? [y/N] "
)
if response.strip().lower() != "y":
print("Aborted.")
return
os.system(f"rm -rf {self.persist_dir}")
print(f"Successfully cleared {self.persist_dir}")
self.verbose = verbose
ingestion_pipeline = cast(IngestionPipeline, self.ingestion_pipeline)
if self.verbose:
print("Saving/Loading from persist_dir: ", self.persist_dir)
if files is not None:
documents = []
for _file in iglob(files, recursive=True):
_file = os.path.abspath(_file)
if os.path.isdir(_file):
reader = SimpleDirectoryReader(
input_dir=_file,
filename_as_id=True,
file_extractor=self.file_extractor,
)
else:
reader = SimpleDirectoryReader(
input_files=[_file],
filename_as_id=True,
file_extractor=self.file_extractor,
)
documents.extend(reader.load_data(show_progress=verbose))
await ingestion_pipeline.arun(show_progress=verbose, documents=documents)
ingestion_pipeline.persist(persist_dir=self.persist_dir)
# Append the `--files` argument to the history file
with open(f"{self.persist_dir}/{RAG_HISTORY_FILE_NAME}", "a") as f:
f.write(files + "\n")
if create_llama:
if shutil.which("npx") is None:
print(
"`npx` is not installed. Please install it by calling `npm install -g npx`"
)
else:
history_file_path = Path(f"{self.persist_dir}/{RAG_HISTORY_FILE_NAME}")
if not history_file_path.exists():
print(
"No data has been ingested, "
"please specify `--files` to create llama dataset."
)
else:
with open(history_file_path) as f:
stored_paths = {line.strip() for line in f if line.strip()}
if len(stored_paths) == 0:
print(
"No data has been ingested, "
"please specify `--files` to create llama dataset."
)
elif len(stored_paths) > 1:
print(
"Multiple files or folders were ingested, which is not supported by create-llama. "
"Please call `llamaindex-cli rag --clear` to clear the cache first, "
"then call `llamaindex-cli rag --files` again with a single folder or file"
)
else:
path = stored_paths.pop()
if "*" in path:
print(
"Glob pattern is not supported by create-llama. "
"Please call `llamaindex-cli rag --clear` to clear the cache first, "
"then call `llamaindex-cli rag --files` again with a single folder or file."
)
elif not os.path.exists(path):
print(
f"The path {path} does not exist. "
"Please call `llamaindex-cli rag --clear` to clear the cache first, "
"then call `llamaindex-cli rag --files` again with a single folder or file."
)
else:
print(f"Calling create-llama using data from {path} ...")
command_args = [
"npx",
"create-llama@latest",
"--frontend",
"--template",
"streaming",
"--framework",
"fastapi",
"--ui",
"shadcn",
"--vector-db",
"none",
"--engine",
"context",
f"--files {path}",
]
os.system(" ".join(command_args))
if question is not None:
await self.handle_question(question)
if chat:
await self.start_chat_repl()
async def handle_question(self, question: str) -> None:
if self.query_pipeline is None:
raise ValueError("query_pipeline is not defined.")
query_pipeline = cast(QueryPipeline, self.query_pipeline)
query_pipeline.verbose = self.verbose
chat_engine = cast(CondenseQuestionChatEngine, self.chat_engine)
response = chat_engine.chat(question)
if isinstance(response, StreamingResponse):
response.print_response_stream()
else:
response = cast(Response, response)
print(response)
async def start_chat_repl(self) -> None:
"""
Start a REPL for chatting with the agent.
"""
if self.query_pipeline is None:
raise ValueError("query_pipeline is not defined.")
chat_engine = cast(CondenseQuestionChatEngine, self.chat_engine)
chat_engine.streaming_chat_repl()
@classmethod
def add_parser_args(
cls,
parser: Union[ArgumentParser, Any],
instance_generator: Optional[Callable[[], "RagCLI"]],
) -> None:
if instance_generator:
parser.add_argument(
"-q",
"--question",
type=str,
help="The question you want to ask.",
required=False,
)
parser.add_argument(
"-f",
"--files",
type=str,
help=(
"The name of the file or directory you want to ask a question about,"
'such as "file.pdf".'
),
)
parser.add_argument(
"-c",
"--chat",
help="If flag is present, opens a chat REPL.",
action="store_true",
)
parser.add_argument(
"-v",
"--verbose",
help="Whether to print out verbose information during execution.",
action="store_true",
)
parser.add_argument(
"--clear",
help="Clears out all currently embedded data.",
action="store_true",
)
parser.add_argument(
"--create-llama",
help="Create a LlamaIndex application with your embedded data.",
required=False,
action="store_true",
)
parser.set_defaults(
func=lambda args: asyncio.run(
instance_generator().handle_cli(**vars(args))
)
)
def cli(self) -> None:
"""
Entrypoint for CLI tool.
"""
parser = ArgumentParser(description="LlamaIndex RAG Q&A tool.")
subparsers = parser.add_subparsers(
title="commands", dest="command", required=True
)
llamarag_parser = subparsers.add_parser(
"rag", help="Ask a question to a document / a directory of documents."
)
self.add_parser_args(llamarag_parser, lambda: self)
# Parse the command-line arguments
args = parser.parse_args()
# Call the appropriate function based on the command
args.func(args)
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from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Callable, List, Optional
if TYPE_CHECKING:
from llama_index.core.service_context import ServiceContext
from llama_index.core.base.embeddings.base import BaseEmbedding
from llama_index.core.callbacks.base import BaseCallbackHandler, CallbackManager
from llama_index.core.embeddings.utils import EmbedType, resolve_embed_model
from llama_index.core.indices.prompt_helper import PromptHelper
from llama_index.core.llms import LLM
from llama_index.core.llms.utils import LLMType, resolve_llm
from llama_index.core.node_parser import NodeParser, SentenceSplitter
from llama_index.core.schema import TransformComponent
from llama_index.core.types import PydanticProgramMode
from llama_index.core.utils import get_tokenizer, set_global_tokenizer
@dataclass
class _Settings:
"""Settings for the Llama Index, lazily initialized."""
# lazy initialization
_llm: Optional[LLM] = None
_embed_model: Optional[BaseEmbedding] = None
_callback_manager: Optional[CallbackManager] = None
_tokenizer: Optional[Callable[[str], List[Any]]] = None
_node_parser: Optional[NodeParser] = None
_prompt_helper: Optional[PromptHelper] = None
_transformations: Optional[List[TransformComponent]] = None
# ---- LLM ----
@property
def llm(self) -> LLM:
"""Get the LLM."""
if self._llm is None:
self._llm = resolve_llm("default")
if self._callback_manager is not None:
self._llm.callback_manager = self._callback_manager
return self._llm
@llm.setter
def llm(self, llm: LLMType) -> None:
"""Set the LLM."""
self._llm = resolve_llm(llm)
@property
def pydantic_program_mode(self) -> PydanticProgramMode:
"""Get the pydantic program mode."""
return self.llm.pydantic_program_mode
@pydantic_program_mode.setter
def pydantic_program_mode(self, pydantic_program_mode: PydanticProgramMode) -> None:
"""Set the pydantic program mode."""
self.llm.pydantic_program_mode = pydantic_program_mode
# ---- Embedding ----
@property
def embed_model(self) -> BaseEmbedding:
"""Get the embedding model."""
if self._embed_model is None:
self._embed_model = resolve_embed_model("default")
if self._callback_manager is not None:
self._embed_model.callback_manager = self._callback_manager
return self._embed_model
@embed_model.setter
def embed_model(self, embed_model: EmbedType) -> None:
"""Set the embedding model."""
self._embed_model = resolve_embed_model(embed_model)
# ---- Callbacks ----
@property
def global_handler(self) -> Optional[BaseCallbackHandler]:
"""Get the global handler."""
import llama_index.core
# TODO: deprecated?
return llama_index.core.global_handler
@global_handler.setter
def global_handler(self, eval_mode: str, **eval_params: Any) -> None:
"""Set the global handler."""
from llama_index.core import set_global_handler
# TODO: deprecated?
set_global_handler(eval_mode, **eval_params)
@property
def callback_manager(self) -> CallbackManager:
"""Get the callback manager."""
if self._callback_manager is None:
self._callback_manager = CallbackManager()
return self._callback_manager
@callback_manager.setter
def callback_manager(self, callback_manager: CallbackManager) -> None:
"""Set the callback manager."""
self._callback_manager = callback_manager
# ---- Tokenizer ----
@property
def tokenizer(self) -> Callable[[str], List[Any]]:
"""Get the tokenizer."""
import llama_index.core
if llama_index.core.global_tokenizer is None:
return get_tokenizer()
# TODO: deprecated?
return llama_index.core.global_tokenizer
@tokenizer.setter
def tokenizer(self, tokenizer: Callable[[str], List[Any]]) -> None:
"""Set the tokenizer."""
try:
from transformers import PreTrainedTokenizerBase # pants: no-infer-dep
if isinstance(tokenizer, PreTrainedTokenizerBase):
from functools import partial
tokenizer = partial(tokenizer.encode, add_special_tokens=False)
except ImportError:
pass
# TODO: deprecated?
set_global_tokenizer(tokenizer)
# ---- Node parser ----
@property
def node_parser(self) -> NodeParser:
"""Get the node parser."""
if self._node_parser is None:
self._node_parser = SentenceSplitter()
if self._callback_manager is not None:
self._node_parser.callback_manager = self._callback_manager
return self._node_parser
@node_parser.setter
def node_parser(self, node_parser: NodeParser) -> None:
"""Set the node parser."""
self._node_parser = node_parser
@property
def chunk_size(self) -> int:
"""Get the chunk size."""
if hasattr(self.node_parser, "chunk_size"):
return self.node_parser.chunk_size
else:
raise ValueError("Configured node parser does not have chunk size.")
@chunk_size.setter
def chunk_size(self, chunk_size: int) -> None:
"""Set the chunk size."""
if hasattr(self.node_parser, "chunk_size"):
self.node_parser.chunk_size = chunk_size
else:
raise ValueError("Configured node parser does not have chunk size.")
@property
def chunk_overlap(self) -> int:
"""Get the chunk overlap."""
if hasattr(self.node_parser, "chunk_overlap"):
return self.node_parser.chunk_overlap
else:
raise ValueError("Configured node parser does not have chunk overlap.")
@chunk_overlap.setter
def chunk_overlap(self, chunk_overlap: int) -> None:
"""Set the chunk overlap."""
if hasattr(self.node_parser, "chunk_overlap"):
self.node_parser.chunk_overlap = chunk_overlap
else:
raise ValueError("Configured node parser does not have chunk overlap.")
# ---- Node parser alias ----
@property
def text_splitter(self) -> NodeParser:
"""Get the text splitter."""
return self.node_parser
@text_splitter.setter
def text_splitter(self, text_splitter: NodeParser) -> None:
"""Set the text splitter."""
self.node_parser = text_splitter
@property
def prompt_helper(self) -> PromptHelper:
"""Get the prompt helper."""
if self._llm is not None and self._prompt_helper is None:
self._prompt_helper = PromptHelper.from_llm_metadata(self._llm.metadata)
elif self._prompt_helper is None:
self._prompt_helper = PromptHelper()
return self._prompt_helper
@prompt_helper.setter
def prompt_helper(self, prompt_helper: PromptHelper) -> None:
"""Set the prompt helper."""
self._prompt_helper = prompt_helper
@property
def num_output(self) -> int:
"""Get the number of outputs."""
return self.prompt_helper.num_output
@num_output.setter
def num_output(self, num_output: int) -> None:
"""Set the number of outputs."""
self.prompt_helper.num_output = num_output
@property
def context_window(self) -> int:
"""Get the context window."""
return self.prompt_helper.context_window
@context_window.setter
def context_window(self, context_window: int) -> None:
"""Set the context window."""
self.prompt_helper.context_window = context_window
# ---- Transformations ----
@property
def transformations(self) -> List[TransformComponent]:
"""Get the transformations."""
if self._transformations is None:
self._transformations = [self.node_parser]
return self._transformations
@transformations.setter
def transformations(self, transformations: List[TransformComponent]) -> None:
"""Set the transformations."""
self._transformations = transformations
# Singleton
Settings = _Settings()
# -- Helper functions for deprecation/migration --
def llm_from_settings_or_context(
settings: _Settings, context: Optional["ServiceContext"]
) -> LLM:
"""Get settings from either settings or context."""
if context is not None:
return context.llm
return settings.llm
def embed_model_from_settings_or_context(
settings: _Settings, context: Optional["ServiceContext"]
) -> BaseEmbedding:
"""Get settings from either settings or context."""
if context is not None:
return context.embed_model
return settings.embed_model
def callback_manager_from_settings_or_context(
settings: _Settings, context: Optional["ServiceContext"]
) -> CallbackManager:
"""Get settings from either settings or context."""
if context is not None:
return context.callback_manager
return settings.callback_manager
def node_parser_from_settings_or_context(
settings: _Settings, context: Optional["ServiceContext"]
) -> NodeParser:
"""Get settings from either settings or context."""
if context is not None:
return context.node_parser
return settings.node_parser
def transformations_from_settings_or_context(
settings: _Settings, context: Optional["ServiceContext"]
) -> List[TransformComponent]:
"""Get settings from either settings or context."""
if context is not None:
return context.transformations
return settings.transformations
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"llama_index.core.embeddings.utils.resolve_embed_model",
"llama_index.core.node_parser.SentenceSplitter",
"llama_index.core.callbacks.base.CallbackManager",
"llama_index.core.set_global_handler",
"llama_index.core.indices.prompt_helper.PromptHelper.from_llm_metadata",
"llama_index.core.utils.get_tokenizer",
"llama_index.core.llms.utils.resolve_llm",
"llama_index.core.indices.prompt_helper.PromptHelper",
"llama_index.core.utils.set_global_tokenizer"
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import asyncio
from llama_index.core.llama_dataset import download_llama_dataset
from llama_index.core.llama_pack import download_llama_pack
from llama_index.core import VectorStoreIndex
async def main():
# DOWNLOAD LLAMADATASET
rag_dataset, documents = download_llama_dataset("CovidQaDataset", "./data")
# BUILD BASIC RAG PIPELINE
index = VectorStoreIndex.from_documents(documents=documents)
query_engine = index.as_query_engine()
# EVALUATE WITH PACK
RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./pack")
rag_evaluator = RagEvaluatorPack(query_engine=query_engine, rag_dataset=rag_dataset)
############################################################################
# NOTE: If have a lower tier subscription for OpenAI API like Usage Tier 1 #
# then you'll need to use different batch_size and sleep_time_in_seconds. #
# For Usage Tier 1, settings that seemed to work well were batch_size=5, #
# and sleep_time_in_seconds=15 (as of December 2023.) #
############################################################################
benchmark_df = await rag_evaluator.arun(
batch_size=40, # batches the number of openai api calls to make
sleep_time_in_seconds=1, # number of seconds sleep before making an api call
)
print(benchmark_df)
if __name__ == "__main__":
loop = asyncio.get_event_loop()
loop.run_until_complete(main)
| [
"llama_index.core.llama_dataset.download_llama_dataset",
"llama_index.core.llama_pack.download_llama_pack",
"llama_index.core.VectorStoreIndex.from_documents"
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from enum import Enum
from typing import Any, AsyncGenerator, Generator, Optional, Union, List
from llama_index.core.bridge.pydantic import BaseModel, Field
from llama_index.core.constants import DEFAULT_CONTEXT_WINDOW, DEFAULT_NUM_OUTPUTS
class MessageRole(str, Enum):
"""Message role."""
SYSTEM = "system"
USER = "user"
ASSISTANT = "assistant"
FUNCTION = "function"
TOOL = "tool"
CHATBOT = "chatbot"
MODEL = "model"
# ===== Generic Model Input - Chat =====
class ChatMessage(BaseModel):
"""Chat message."""
role: MessageRole = MessageRole.USER
content: Optional[Any] = ""
additional_kwargs: dict = Field(default_factory=dict)
def __str__(self) -> str:
return f"{self.role.value}: {self.content}"
@classmethod
def from_str(
cls,
content: str,
role: Union[MessageRole, str] = MessageRole.USER,
**kwargs: Any,
) -> "ChatMessage":
if isinstance(role, str):
role = MessageRole(role)
return cls(role=role, content=content, **kwargs)
class LogProb(BaseModel):
"""LogProb of a token."""
token: str = Field(default_factory=str)
logprob: float = Field(default_factory=float)
bytes: List[int] = Field(default_factory=list)
# ===== Generic Model Output - Chat =====
class ChatResponse(BaseModel):
"""Chat response."""
message: ChatMessage
raw: Optional[dict] = None
delta: Optional[str] = None
logprobs: Optional[List[List[LogProb]]] = None
additional_kwargs: dict = Field(default_factory=dict)
def __str__(self) -> str:
return str(self.message)
ChatResponseGen = Generator[ChatResponse, None, None]
ChatResponseAsyncGen = AsyncGenerator[ChatResponse, None]
# ===== Generic Model Output - Completion =====
class CompletionResponse(BaseModel):
"""
Completion response.
Fields:
text: Text content of the response if not streaming, or if streaming,
the current extent of streamed text.
additional_kwargs: Additional information on the response(i.e. token
counts, function calling information).
raw: Optional raw JSON that was parsed to populate text, if relevant.
delta: New text that just streamed in (only relevant when streaming).
"""
text: str
additional_kwargs: dict = Field(default_factory=dict)
raw: Optional[dict] = None
delta: Optional[str] = None
def __str__(self) -> str:
return self.text
CompletionResponseGen = Generator[CompletionResponse, None, None]
CompletionResponseAsyncGen = AsyncGenerator[CompletionResponse, None]
class LLMMetadata(BaseModel):
context_window: int = Field(
default=DEFAULT_CONTEXT_WINDOW,
description=(
"Total number of tokens the model can be input and output for one response."
),
)
num_output: int = Field(
default=DEFAULT_NUM_OUTPUTS,
description="Number of tokens the model can output when generating a response.",
)
is_chat_model: bool = Field(
default=False,
description=(
"Set True if the model exposes a chat interface (i.e. can be passed a"
" sequence of messages, rather than text), like OpenAI's"
" /v1/chat/completions endpoint."
),
)
is_function_calling_model: bool = Field(
default=False,
# SEE: https://openai.com/blog/function-calling-and-other-api-updates
description=(
"Set True if the model supports function calling messages, similar to"
" OpenAI's function calling API. For example, converting 'Email Anya to"
" see if she wants to get coffee next Friday' to a function call like"
" `send_email(to: string, body: string)`."
),
)
model_name: str = Field(
default="unknown",
description=(
"The model's name used for logging, testing, and sanity checking. For some"
" models this can be automatically discerned. For other models, like"
" locally loaded models, this must be manually specified."
),
)
system_role: MessageRole = Field(
default=MessageRole.SYSTEM,
description="The role this specific LLM provider"
"expects for system prompt. E.g. 'SYSTEM' for OpenAI, 'CHATBOT' for Cohere",
)
| [
"llama_index.core.bridge.pydantic.Field"
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"""Base agent type."""
import uuid
from abc import abstractmethod
from typing import Any, Dict, List, Optional
from llama_index.core.base.base_query_engine import BaseQueryEngine
from llama_index.core.base.llms.types import ChatMessage
from llama_index.core.base.response.schema import RESPONSE_TYPE, Response
from llama_index.core.bridge.pydantic import BaseModel, Field
from llama_index.core.callbacks import CallbackManager, trace_method
from llama_index.core.chat_engine.types import (
BaseChatEngine,
StreamingAgentChatResponse,
)
from llama_index.core.memory.types import BaseMemory
from llama_index.core.prompts.mixin import PromptDictType, PromptMixin, PromptMixinType
from llama_index.core.schema import QueryBundle
class BaseAgent(BaseChatEngine, BaseQueryEngine):
"""Base Agent."""
def _get_prompts(self) -> PromptDictType:
"""Get prompts."""
# TODO: the ReAct agent does not explicitly specify prompts, would need a
# refactor to expose those prompts
return {}
def _get_prompt_modules(self) -> PromptMixinType:
"""Get prompt modules."""
return {}
def _update_prompts(self, prompts: PromptDictType) -> None:
"""Update prompts."""
# ===== Query Engine Interface =====
@trace_method("query")
def _query(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
agent_response = self.chat(
query_bundle.query_str,
chat_history=[],
)
return Response(
response=str(agent_response), source_nodes=agent_response.source_nodes
)
@trace_method("query")
async def _aquery(self, query_bundle: QueryBundle) -> RESPONSE_TYPE:
agent_response = await self.achat(
query_bundle.query_str,
chat_history=[],
)
return Response(
response=str(agent_response), source_nodes=agent_response.source_nodes
)
def stream_chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> StreamingAgentChatResponse:
raise NotImplementedError("stream_chat not implemented")
async def astream_chat(
self, message: str, chat_history: Optional[List[ChatMessage]] = None
) -> StreamingAgentChatResponse:
raise NotImplementedError("astream_chat not implemented")
class TaskStep(BaseModel):
"""Agent task step.
Represents a single input step within the execution run ("Task") of an agent
given a user input.
The output is returned as a `TaskStepOutput`.
"""
task_id: str = Field(..., diescription="Task ID")
step_id: str = Field(..., description="Step ID")
input: Optional[str] = Field(default=None, description="User input")
# memory: BaseMemory = Field(
# ..., type=BaseMemory, description="Conversational Memory"
# )
step_state: Dict[str, Any] = Field(
default_factory=dict, description="Additional state for a given step."
)
# NOTE: the state below may change throughout the course of execution
# this tracks the relationships to other steps
next_steps: Dict[str, "TaskStep"] = Field(
default_factory=dict, description="Next steps to be executed."
)
prev_steps: Dict[str, "TaskStep"] = Field(
default_factory=dict,
description="Previous steps that were dependencies for this step.",
)
is_ready: bool = Field(
default=True, description="Is this step ready to be executed?"
)
def get_next_step(
self,
step_id: str,
input: Optional[str] = None,
step_state: Optional[Dict[str, Any]] = None,
) -> "TaskStep":
"""Convenience function to get next step.
Preserve task_id, memory, step_state.
"""
return TaskStep(
task_id=self.task_id,
step_id=step_id,
input=input,
# memory=self.memory,
step_state=step_state or self.step_state,
)
def link_step(
self,
next_step: "TaskStep",
) -> None:
"""Link to next step.
Add link from this step to next, and from next step to current.
"""
self.next_steps[next_step.step_id] = next_step
next_step.prev_steps[self.step_id] = self
class TaskStepOutput(BaseModel):
"""Agent task step output."""
output: Any = Field(..., description="Task step output")
task_step: TaskStep = Field(..., description="Task step input")
next_steps: List[TaskStep] = Field(..., description="Next steps to be executed.")
is_last: bool = Field(default=False, description="Is this the last step?")
def __str__(self) -> str:
"""String representation."""
return str(self.output)
class Task(BaseModel):
"""Agent Task.
Represents a "run" of an agent given a user input.
"""
class Config:
arbitrary_types_allowed = True
task_id: str = Field(
default_factory=lambda: str(uuid.uuid4()), type=str, description="Task ID"
)
input: str = Field(..., type=str, description="User input")
# NOTE: this is state that may be modified throughout the course of execution of the task
memory: BaseMemory = Field(
...,
type=BaseMemory,
description=(
"Conversational Memory. Maintains state before execution of this task."
),
)
callback_manager: CallbackManager = Field(
default_factory=CallbackManager,
exclude=True,
description="Callback manager for the task.",
)
extra_state: Dict[str, Any] = Field(
default_factory=dict,
description=(
"Additional user-specified state for a given task. "
"Can be modified throughout the execution of a task."
),
)
class BaseAgentWorker(PromptMixin):
"""Base agent worker."""
class Config:
arbitrary_types_allowed = True
def _get_prompts(self) -> PromptDictType:
"""Get prompts."""
# TODO: the ReAct agent does not explicitly specify prompts, would need a
# refactor to expose those prompts
return {}
def _get_prompt_modules(self) -> PromptMixinType:
"""Get prompt modules."""
return {}
def _update_prompts(self, prompts: PromptDictType) -> None:
"""Update prompts."""
@abstractmethod
def initialize_step(self, task: Task, **kwargs: Any) -> TaskStep:
"""Initialize step from task."""
@abstractmethod
def run_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
"""Run step."""
@abstractmethod
async def arun_step(
self, step: TaskStep, task: Task, **kwargs: Any
) -> TaskStepOutput:
"""Run step (async)."""
raise NotImplementedError
@abstractmethod
def stream_step(self, step: TaskStep, task: Task, **kwargs: Any) -> TaskStepOutput:
"""Run step (stream)."""
# TODO: figure out if we need a different type for TaskStepOutput
raise NotImplementedError
@abstractmethod
async def astream_step(
self, step: TaskStep, task: Task, **kwargs: Any
) -> TaskStepOutput:
"""Run step (async stream)."""
raise NotImplementedError
@abstractmethod
def finalize_task(self, task: Task, **kwargs: Any) -> None:
"""Finalize task, after all the steps are completed."""
def set_callback_manager(self, callback_manager: CallbackManager) -> None:
"""Set callback manager."""
# TODO: make this abstractmethod (right now will break some agent impls)
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"llama_index.core.bridge.pydantic.Field",
"llama_index.core.callbacks.trace_method"
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Can be modified throughout the execution of a task.'\n )\n", (5591, 5740), False, 'from llama_index.core.bridge.pydantic import BaseModel, Field\n'), ((4975, 4987), 'uuid.uuid4', 'uuid.uuid4', ([], {}), '()\n', (4985, 4987), False, 'import uuid\n')] |
import json
from abc import abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Dict, Optional, Type
if TYPE_CHECKING:
from llama_index.core.bridge.langchain import StructuredTool, Tool
from deprecated import deprecated
from llama_index.core.bridge.pydantic import BaseModel
class DefaultToolFnSchema(BaseModel):
"""Default tool function Schema."""
input: str
@dataclass
class ToolMetadata:
description: str
name: Optional[str] = None
fn_schema: Optional[Type[BaseModel]] = DefaultToolFnSchema
def get_parameters_dict(self) -> dict:
if self.fn_schema is None:
parameters = {
"type": "object",
"properties": {
"input": {"title": "input query string", "type": "string"},
},
"required": ["input"],
}
else:
parameters = self.fn_schema.schema()
parameters = {
k: v
for k, v in parameters.items()
if k in ["type", "properties", "required", "definitions"]
}
return parameters
@property
def fn_schema_str(self) -> str:
"""Get fn schema as string."""
if self.fn_schema is None:
raise ValueError("fn_schema is None.")
parameters = self.get_parameters_dict()
return json.dumps(parameters)
def get_name(self) -> str:
"""Get name."""
if self.name is None:
raise ValueError("name is None.")
return self.name
@deprecated(
"Deprecated in favor of `to_openai_tool`, which should be used instead."
)
def to_openai_function(self) -> Dict[str, Any]:
"""Deprecated and replaced by `to_openai_tool`.
The name and arguments of a function that should be called, as generated by the
model.
"""
return {
"name": self.name,
"description": self.description,
"parameters": self.get_parameters_dict(),
}
def to_openai_tool(self) -> Dict[str, Any]:
"""To OpenAI tool."""
return {
"type": "function",
"function": {
"name": self.name,
"description": self.description,
"parameters": self.get_parameters_dict(),
},
}
class ToolOutput(BaseModel):
"""Tool output."""
content: str
tool_name: str
raw_input: Dict[str, Any]
raw_output: Any
def __str__(self) -> str:
"""String."""
return str(self.content)
class BaseTool:
@property
@abstractmethod
def metadata(self) -> ToolMetadata:
pass
@abstractmethod
def __call__(self, input: Any) -> ToolOutput:
pass
def _process_langchain_tool_kwargs(
self,
langchain_tool_kwargs: Any,
) -> Dict[str, Any]:
"""Process langchain tool kwargs."""
if "name" not in langchain_tool_kwargs:
langchain_tool_kwargs["name"] = self.metadata.name or ""
if "description" not in langchain_tool_kwargs:
langchain_tool_kwargs["description"] = self.metadata.description
if "fn_schema" not in langchain_tool_kwargs:
langchain_tool_kwargs["args_schema"] = self.metadata.fn_schema
return langchain_tool_kwargs
def to_langchain_tool(
self,
**langchain_tool_kwargs: Any,
) -> "Tool":
"""To langchain tool."""
from llama_index.core.bridge.langchain import Tool
langchain_tool_kwargs = self._process_langchain_tool_kwargs(
langchain_tool_kwargs
)
return Tool.from_function(
func=self.__call__,
**langchain_tool_kwargs,
)
def to_langchain_structured_tool(
self,
**langchain_tool_kwargs: Any,
) -> "StructuredTool":
"""To langchain structured tool."""
from llama_index.core.bridge.langchain import StructuredTool
langchain_tool_kwargs = self._process_langchain_tool_kwargs(
langchain_tool_kwargs
)
return StructuredTool.from_function(
func=self.__call__,
**langchain_tool_kwargs,
)
class AsyncBaseTool(BaseTool):
"""
Base-level tool class that is backwards compatible with the old tool spec but also
supports async.
"""
def __call__(self, *args: Any, **kwargs: Any) -> ToolOutput:
return self.call(*args, **kwargs)
@abstractmethod
def call(self, input: Any) -> ToolOutput:
"""
This is the method that should be implemented by the tool developer.
"""
@abstractmethod
async def acall(self, input: Any) -> ToolOutput:
"""
This is the async version of the call method.
Should also be implemented by the tool developer as an
async-compatible implementation.
"""
class BaseToolAsyncAdapter(AsyncBaseTool):
"""
Adapter class that allows a synchronous tool to be used as an async tool.
"""
def __init__(self, tool: BaseTool):
self.base_tool = tool
@property
def metadata(self) -> ToolMetadata:
return self.base_tool.metadata
def call(self, input: Any) -> ToolOutput:
return self.base_tool(input)
async def acall(self, input: Any) -> ToolOutput:
return self.call(input)
def adapt_to_async_tool(tool: BaseTool) -> AsyncBaseTool:
"""
Converts a synchronous tool to an async tool.
"""
if isinstance(tool, AsyncBaseTool):
return tool
else:
return BaseToolAsyncAdapter(tool)
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"llama_index.core.bridge.langchain.Tool.from_function",
"llama_index.core.bridge.langchain.StructuredTool.from_function"
] | [((1581, 1670), 'deprecated.deprecated', 'deprecated', (['"""Deprecated in favor of `to_openai_tool`, which should be used instead."""'], {}), "(\n 'Deprecated in favor of `to_openai_tool`, which should be used instead.')\n", (1591, 1670), False, 'from deprecated import deprecated\n'), ((1395, 1417), 'json.dumps', 'json.dumps', (['parameters'], {}), '(parameters)\n', (1405, 1417), False, 'import json\n'), ((3690, 3753), 'llama_index.core.bridge.langchain.Tool.from_function', 'Tool.from_function', ([], {'func': 'self.__call__'}), '(func=self.__call__, **langchain_tool_kwargs)\n', (3708, 3753), False, 'from llama_index.core.bridge.langchain import Tool\n'), ((4149, 4222), 'llama_index.core.bridge.langchain.StructuredTool.from_function', 'StructuredTool.from_function', ([], {'func': 'self.__call__'}), '(func=self.__call__, **langchain_tool_kwargs)\n', (4177, 4222), False, 'from llama_index.core.bridge.langchain import StructuredTool\n')] |
"""Generate SQL queries using LlamaIndex."""
import argparse
import json
import logging
import os
import re
from typing import Any, cast
from llama_index import LLMPredictor, SQLDatabase
from llama_index.indices import SQLStructStoreIndex
from llama_index.llms.openai import OpenAI
from sqlalchemy import create_engine, text
from tqdm import tqdm
logging.getLogger("root").setLevel(logging.WARNING)
_spaces = re.compile(r"\s+")
_newlines = re.compile(r"\n+")
def _generate_sql(
llama_index: SQLStructStoreIndex,
nl_query_text: str,
) -> str:
"""Generate SQL query for the given NL query text."""
query_engine = llama_index.as_query_engine()
response = query_engine.query(nl_query_text)
if (
response.metadata is None
or "sql_query" not in response.metadata
or response.metadata["sql_query"] is None
):
raise RuntimeError("No SQL query generated.")
query = response.metadata["sql_query"]
# Remove newlines and extra spaces.
query = _newlines.sub(" ", query)
query = _spaces.sub(" ", query)
return query.strip()
def generate_sql(llama_indexes: dict, examples: list, output_file: str) -> None:
"""Generate SQL queries for the given examples and write them to the output file."""
with open(output_file, "w") as f:
for example in tqdm(examples, desc=f"Generating {output_file}"):
db_name = example["db_id"]
nl_query_text = example["question"]
try:
sql_query = _generate_sql(llama_indexes[db_name], nl_query_text)
except Exception as e:
print(
f"Failed to generate SQL query for question: "
f"{example['question']} on database: {example['db_id']}."
)
print(e)
sql_query = "ERROR"
f.write(sql_query + "\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Generate SQL queries using LlamaIndex."
)
parser.add_argument(
"--input", type=str, required=True, help="Path to the spider dataset directory."
)
parser.add_argument(
"--output",
type=str,
required=True,
help="Path to the output directory of generated SQL files,"
" one query on each line, "
"to be compared with the *_gold.sql files in the input directory.",
)
parser.add_argument(
"--model",
type=str,
choices=["gpt-4", "gpt-3.5-turbo", "text-davinci-003", "code-davinci-002"],
required=True,
help="The model to use for generating SQL queries.",
)
args = parser.parse_args()
# Create the output directory if it does not exist.
if not os.path.exists(args.output):
os.makedirs(args.output)
# Load the Spider dataset from the input directory.
with open(os.path.join(args.input, "train_spider.json")) as f:
train_spider = json.load(f)
with open(os.path.join(args.input, "train_others.json")) as f:
train_others = json.load(f)
with open(os.path.join(args.input, "dev.json")) as f:
dev = json.load(f)
# Create all necessary SQL database objects.
databases = {}
for db in train_spider + train_others + dev:
db_name = db["db_id"]
if db_name in databases:
continue
db_path = os.path.join(args.input, "database", db_name, db_name + ".sqlite")
engine = create_engine("sqlite:///" + db_path)
databases[db_name] = (SQLDatabase(engine=engine), engine)
# Create the LlamaIndexes for all databases.
llm = OpenAI(model=args.model, temperature=0)
llm_predictor = LLMPredictor(llm=llm)
llm_indexes = {}
for db_name, (db, engine) in databases.items():
# Get the name of the first table in the database.
# This is a hack to get a table name for the index, which can use any
# table in the database.
with engine.connect() as connection:
table_name = cast(
Any,
connection.execute(
text("select name from sqlite_master where type = 'table'")
).fetchone(),
)[0]
llm_indexes[db_name] = SQLStructStoreIndex.from_documents(
documents=[],
llm_predictor=llm_predictor,
sql_database=db,
table_name=table_name,
)
# Generate SQL queries.
generate_sql(
llama_indexes=llm_indexes,
examples=train_spider + train_others,
output_file=os.path.join(args.output, "train_pred.sql"),
)
generate_sql(
llama_indexes=llm_indexes,
examples=dev,
output_file=os.path.join(args.output, "dev_pred.sql"),
)
| [
"llama_index.LLMPredictor",
"llama_index.SQLDatabase",
"llama_index.indices.SQLStructStoreIndex.from_documents",
"llama_index.llms.openai.OpenAI"
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"""Utilities for Spider module."""
import json
import os
from typing import Dict, Tuple
from llama_index import LLMPredictor, SQLDatabase
from llama_index.indices import SQLStructStoreIndex
from llama_index.llms.openai import OpenAI
from sqlalchemy import create_engine, text
def load_examples(spider_dir: str) -> Tuple[list, list]:
"""Load examples."""
with open(os.path.join(spider_dir, "train_spider.json")) as f:
train_spider = json.load(f)
with open(os.path.join(spider_dir, "train_others.json")) as f:
train_others = json.load(f)
with open(os.path.join(spider_dir, "dev.json")) as f:
dev = json.load(f)
return train_spider + train_others, dev
def create_indexes(spider_dir: str, llm: OpenAI) -> Dict[str, SQLStructStoreIndex]:
"""Create indexes for all databases."""
# Create all necessary SQL database objects.
databases = {}
for db_name in os.listdir(os.path.join(spider_dir, "database")):
db_path = os.path.join(spider_dir, "database", db_name, db_name + ".sqlite")
if not os.path.exists(db_path):
continue
engine = create_engine("sqlite:///" + db_path)
databases[db_name] = SQLDatabase(engine=engine)
# Test connection.
with engine.connect() as connection:
connection.execute(
text("select name from sqlite_master where type = 'table'")
).fetchone()
llm_predictor = LLMPredictor(llm=llm)
llm_indexes = {}
for db_name, db in databases.items():
llm_indexes[db_name] = SQLStructStoreIndex(
llm_predictor=llm_predictor,
sql_database=db,
)
return llm_indexes
| [
"llama_index.indices.SQLStructStoreIndex",
"llama_index.SQLDatabase",
"llama_index.LLMPredictor"
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