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feat: Add browse the internet or online functionality. (#291)

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This commit is contained in:
Taranjeet Singh
2023-07-16 18:59:58 -07:00
committed by GitHub
parent e8b3d53faf
commit 81c8cc62a2
1 changed files with 24 additions and 6 deletions
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30
embedchain/embedchain.py
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@@ -9,8 +9,7 @@ from langchain.docstore.document import Document
from langchain.memory import ConversationBufferMemory
from embedchain.config import AddConfig, ChatConfig, InitConfig, QueryConfig
from embedchain.config.QueryConfig import (CODE_DOCS_PAGE_PROMPT_TEMPLATE,
DEFAULT_PROMPT)
from embedchain.config.QueryConfig import CODE_DOCS_PAGE_PROMPT_TEMPLATE, DEFAULT_PROMPT
from embedchain.data_formatter import DataFormatter
gpt4all_model = None
@@ -37,6 +36,7 @@ class EmbedChain:
self.collection = self.config.db.collection
self.user_asks = []
self.is_code_docs_instance = False
self.online = False
def add(self, data_type, url, metadata=None, config: AddConfig = None):
"""
@@ -163,7 +163,10 @@ class EmbedChain:
contents = [result[0].page_content for result in results_formatted]
return contents
def generate_prompt(self, input_query, contexts, config: QueryConfig):
def _append_search_and_context(self, context, web_search_result):
return f"{context}\nWeb Search Result: {web_search_result}"
def generate_prompt(self, input_query, contexts, config: QueryConfig, **kwargs):
"""
Generates a prompt based on the given query and context, ready to be
passed to an LLM
@@ -175,6 +178,9 @@ class EmbedChain:
:return: The prompt
"""
context_string = (" | ").join(contexts)
web_search_result = kwargs.get("web_search_result", "")
if web_search_result:
context_string = self._append_search_and_context(context_string, web_search_result)
if not config.history:
prompt = config.template.substitute(context=context_string, query=input_query)
else:
@@ -193,6 +199,12 @@ class EmbedChain:
return self.get_llm_model_answer(prompt, config)
def access_search_and_get_results(self, input_query):
from langchain.tools import DuckDuckGoSearchRun
search = DuckDuckGoSearchRun()
logging.info(f"Access search to get answers for {input_query}")
return search.run(input_query)
def query(self, input_query, config: QueryConfig = None):
"""
Queries the vector database based on the given input query.
@@ -209,8 +221,11 @@ class EmbedChain:
if self.is_code_docs_instance:
config.template = CODE_DOCS_PAGE_PROMPT_TEMPLATE
config.number_documents = 5
k = {}
if self.online:
k["web_search_result"] = self.access_search_and_get_results(input_query)
contexts = self.retrieve_from_database(input_query, config)
prompt = self.generate_prompt(input_query, contexts, config)
prompt = self.generate_prompt(input_query, contexts, config, **k)
logging.info(f"Prompt: {prompt}")
answer = self.get_answer_from_llm(prompt, config)
@@ -245,7 +260,10 @@ class EmbedChain:
if self.is_code_docs_instance:
config.template = CODE_DOCS_PAGE_PROMPT_TEMPLATE
config.number_documents = 5
contexts = self.retrieve_from_database(input_query, config)
k = {}
if self.online:
k["web_search_result"] = self.access_search_and_get_results(input_query)
contexts = self.retrieve_from_database(input_query, config, **k)
global memory
chat_history = memory.load_memory_variables({})["history"]
@@ -253,7 +271,7 @@ class EmbedChain:
if chat_history:
config.set_history(chat_history)
prompt = self.generate_prompt(input_query, contexts, config)
prompt = self.generate_prompt(input_query, contexts, config, **k)
logging.info(f"Prompt: {prompt}")
answer = self.get_answer_from_llm(prompt, config)