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Merge branch 'pre/beta' into add-openai-supported-model-gpt-4o-mini
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‎CHANGELOG.md‎

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## [1.9.1](https://github.com/ScrapeGraphAI/Scrapegraph-ai/compare/v1.9.0...v1.9.1) (2024-07-12)
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### Bug Fixes
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* solve a burr integration ([881290b](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/881290b5066b39c505532656671fbf65f8fc312c))
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## [1.9.0](https://github.com/ScrapeGraphAI/Scrapegraph-ai/compare/v1.8.0...v1.9.0) (2024-07-09)
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### Features
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* add fireworks integration ([df0e310](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/df0e3108299071b849d7e055bd11d72764d24f08))
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* add integration for infos ([3bf5f57](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/3bf5f570a8f8e1b037a7ad3c9f583261a1536421))
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* add integrations for markdown files ([2804434](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/2804434a9ee12c52ae8956a88b1778a4dd3ec32f))
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* add vertexai integration ([119514b](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/119514bdfc2a16dfb8918b0c34ae7cc43a01384c))
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* improve md prompt recognition ([5fe694b](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/5fe694b6b4545a5091d16110318b992acfca4f58))
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### Bug Fixes
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* add test ([3a537ee](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/3a537eec6fef1743924a9aa5cef0ba2f8d44bf11))
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* fix pyproject.toml ([7570bf8](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/7570bf8294e49bc54ec9e296aaadb763873390ca))
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### chore
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* **Docker:** fix port number ([afeb81f](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/afeb81f77a884799192d79dcac85666190fb1c9d))
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* **CI:** fix pylint workflow ([583c321](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/583c32106e827f50235d8fc69511652fd4b07a35))
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* **rye:** rebuild lockfiles ([27c2dd2](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/27c2dd23517a7e4b14fafd00320a8b81f73145dc))
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### Docs
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* **roadmap:** fix urls ([14faba4](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/14faba4f00dd9f947f8dc5e0b51be49ea684179f))
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* **roadmap:** next steps ([3e644f4](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/3e644f498f05eb505fbd4e94b144c81567569aaa))
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### CI
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* **release:** 1.8.1-beta.1 [skip ci] ([8f9f96f](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/8f9f96f7e7ff41d2fff5bbbf18bf4fc85d4f98b3))
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* **release:** 1.9.0-beta.1 [skip ci] ([146432d](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/146432d476f775510441b062935adc47190141e2))
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* **release:** 1.9.0-beta.2 [skip ci] ([5cb5fbf](https://github.com/ScrapeGraphAI/Scrapegraph-ai/commit/5cb5fbf5503eec9b34a6691eb993716cc9a821d6))
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## [1.9.0-beta.2](https://github.com/ScrapeGraphAI/Scrapegraph-ai/compare/v1.9.0-beta.1...v1.9.0-beta.2) (2024-07-05)
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‎examples/anthropic/search_link_graph_haiku.py‎

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"""
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Example of Search Graph
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"""
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import os
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from dotenv import load_dotenv
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from scrapegraphai.graphs import SearchGraph
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from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
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from langchain_openai import AzureChatOpenAI
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from langchain_openai import AzureOpenAIEmbeddings
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# ************************************************
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# Define the configuration for the graph
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# ************************************************
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load_dotenv()
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llm_model_instance = AzureChatOpenAI(
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openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
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azure_deployment=os.environ["AZURE_OPENAI_CHAT_DEPLOYMENT_NAME"]
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)
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embedder_model_instance = AzureOpenAIEmbeddings(
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azure_deployment=os.environ["AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME"],
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openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
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)
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# ************************************************
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# Create the SmartScraperGraph instance and run it
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# ************************************************
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graph_config = {
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"llm": {"model_instance": llm_model_instance},
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"embeddings": {"model_instance": embedder_model_instance}
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}
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# ************************************************
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# Create the SearchGraph instance and run it
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# ************************************************
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search_graph = SearchGraph(
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prompt="List me the best escursions near Trento",
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config=graph_config
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)
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result = search_graph.run()
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print(result)
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# ************************************************
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# Get graph execution info
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# ************************************************
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graph_exec_info = search_graph.get_execution_info()
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print(prettify_exec_info(graph_exec_info))
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# Save to json and csv
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convert_to_csv(result, "result")
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convert_to_json(result, "result")

‎examples/azure/search_link_graph_azure.py‎

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"""
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Example of Search Graph
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"""
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import os
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from dotenv import load_dotenv
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from scrapegraphai.graphs import SearchGraph
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from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
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# ************************************************
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# Define the configuration for the graph
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# ************************************************
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load_dotenv()
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groq_key = os.getenv("GROQ_APIKEY")
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graph_config = {
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"llm": {
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"model": "groq/gemma-7b-it",
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"api_key": groq_key,
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"temperature": 0
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},
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"embeddings": {
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"model": "ollama/nomic-embed-text",
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"temperature": 0,
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# "base_url": "http://localhost:11434", # set ollama URL arbitrarily
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},
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"headless": False
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}
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# ************************************************
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# Create the SearchGraph instance and run it
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# ************************************************
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search_graph = SearchGraph(
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prompt="List me the best escursions near Trento",
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config=graph_config
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)
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result = search_graph.run()
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print(result)
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# ************************************************
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# Get graph execution info
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# ************************************************
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graph_exec_info = search_graph.get_execution_info()
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print(prettify_exec_info(graph_exec_info))
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# Save to json and csv
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convert_to_csv(result, "result")
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convert_to_json(result, "result")

‎examples/bedrock/search_link_graph_bedrock.py‎

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"""
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Example of Search Graph
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"""
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import os
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from dotenv import load_dotenv
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from scrapegraphai.graphs import SearchGraph
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from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
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# ************************************************
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# Define the configuration for the graph
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# ************************************************
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graph_config = {
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"llm": {
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"client": "client_name",
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"model": "bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
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"temperature": 0.0
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},
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"embeddings": {
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"model": "bedrock/cohere.embed-multilingual-v3"
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}
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}
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# ************************************************
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# Create the SearchGraph instance and run it
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# ************************************************
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search_graph = SearchGraph(
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prompt="List me the best escursions near Trento",
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config=graph_config
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)
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result = search_graph.run()
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print(result)
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# ************************************************
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# Get graph execution info
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# ************************************************
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graph_exec_info = search_graph.get_execution_info()
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print(prettify_exec_info(graph_exec_info))
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# Save to json and csv
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convert_to_csv(result, "result")
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convert_to_json(result, "result")

‎examples/deepseek/search_link_graph_deepseek.py‎

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"""
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Example of Search Graph
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"""
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import os
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from dotenv import load_dotenv
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from scrapegraphai.graphs import SearchGraph
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from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
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# ************************************************
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# Define the configuration for the graph
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# ************************************************
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load_dotenv()
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deepseek_key = os.getenv("DEEPSEEK_APIKEY")
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graph_config = {
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"llm": {
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"model": "deepseek-chat",
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"openai_api_key": deepseek_key,
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"openai_api_base": 'https://api.deepseek.com/v1',
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},
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"embeddings": {
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"model": "ollama/nomic-embed-text",
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"temperature": 0,
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# "base_url": "http://localhost:11434", # set ollama URL arbitrarily
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},
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"verbose": True,
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}
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# ************************************************
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# Create the SearchGraph instance and run it
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# ************************************************
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search_graph = SearchGraph(
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prompt="List me the best escursions near Trento",
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config=graph_config
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)
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result = search_graph.run()
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print(result)
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# ************************************************
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# Get graph execution info
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# ************************************************
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graph_exec_info = search_graph.get_execution_info()
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print(prettify_exec_info(graph_exec_info))
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# Save to json and csv
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convert_to_csv(result, "result")
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convert_to_json(result, "result")

‎examples/ernie/search_graph_ernie.py‎

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# Define the configuration for the graph
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# ************************************************
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openai_key = os.getenv("OPENAI_APIKEY")
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graph_config = {
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"llm": {
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"api_key": openai_key,
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"model": "gpt-3.5-turbo",
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},
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"max_results": 2,
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"verbose": True,
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"model": "ernie-bot-turbo",
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"ernie_client_id": "<ernie_client_id>",
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"ernie_client_secret": "<ernie_client_secret>",
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"temperature": 0.1
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},
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"embeddings": {
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"model": "ollama/nomic-embed-text",
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"temperature": 0,
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"base_url": "http://localhost:11434"},
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"library": "beautifulsoup"
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}
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# ************************************************

‎examples/ernie/search_link_graph_ernie.py‎

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"""
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Example of Search Graph
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"""
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from scrapegraphai.graphs import SearchGraph
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from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
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# ************************************************
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# Define the configuration for the graph
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# ************************************************
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graph_config = {
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"llm": {
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"model": "ernie-bot-turbo",
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"ernie_client_id": "<ernie_client_id>",
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"ernie_client_secret": "<ernie_client_secret>",
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"temperature": 0.1
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},
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"embeddings": {
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"model": "ollama/nomic-embed-text",
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"temperature": 0,
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"base_url": "http://localhost:11434"},
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"library": "beautifulsoup"
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}
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# ************************************************
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# Create the SearchGraph instance and run it
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# ************************************************
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search_graph = SearchGraph(
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prompt="List me the best escursions near Trento",
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config=graph_config
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)
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result = search_graph.run()
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print(result)
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# ************************************************
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# Get graph execution info
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# ************************************************
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graph_exec_info = search_graph.get_execution_info()
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print(prettify_exec_info(graph_exec_info))
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# Save to json and csv
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convert_to_csv(result, "result")
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convert_to_json(result, "result")

‎examples/fireworks/search_link_graph_fireworks.py‎

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"""
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Example of Search Graph
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"""
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import os
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from dotenv import load_dotenv
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from scrapegraphai.graphs import SearchGraph
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from scrapegraphai.utils import convert_to_csv, convert_to_json, prettify_exec_info
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# ************************************************
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# Define the configuration for the graph
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# ************************************************
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load_dotenv()
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fireworks_api_key = os.getenv("FIREWORKS_APIKEY")
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graph_config = {
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"llm": {
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"api_key": fireworks_api_key,
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"model": "fireworks/accounts/fireworks/models/mixtral-8x7b-instruct"
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},
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"embeddings": {
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"model": "ollama/nomic-embed-text",
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"temperature": 0,
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# "base_url": "http://localhost:11434", # set ollama URL arbitrarily
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},
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"max_results": 2,
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"verbose": True,
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"headless": False,
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}
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# ************************************************
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# Create the SearchGraph instance and run it
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# ************************************************
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search_graph = SearchGraph(
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prompt="List me the best escursions near Trento",
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config=graph_config
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)
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result = search_graph.run()
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print(result)
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# ************************************************
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# Get graph execution info
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# ************************************************
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graph_exec_info = search_graph.get_execution_info()
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print(prettify_exec_info(graph_exec_info))
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# Save to json and csv
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convert_to_csv(result, "result")
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convert_to_json(result, "result")

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