Setting the model_kwargs at the object initialization uses the filters and size as default for all the subsequent retrievals.retriever = ArceeRetriever(    model="DALM-PubMed",    # arcee_api_key="ARCEE-API-KEY", # if not already set in the environment    arcee_api_url="https://custom-api.arcee.ai", # default is https://api.arcee.ai    arcee_app_url="https://custom-app.arcee.ai", # default is https://app.arcee.ai    model_kwargs={        "size": 5,        "filters": [            {                "field_name": "document",                "filter_type": "fuzzy_search",                "value": "Einstein"            }        ]    })Retrieving documents‚ÄãYou can retrieve relevant documents from uploaded contexts by providing a query. Here's an example:query = "Can AI-driven music therapy contribute to the rehabilitation of patients with disorders of consciousness?"documents = retriever.get_relevant_documents(query=query)Additional parameters‚ÄãArcee allows you to apply filters and set the size (in terms of count) of retrieved document(s). Filters help narrow down the results. Here's how to use these parameters:# Define filtersfilters = [    {        "field_name": "document",        "filter_type": "fuzzy_search",        "value": "Music"    },    {        "field_name": "year",        "filter_type": "strict_search",        "value": "1905"    }]# Retrieve documents with filters and size paramsdocuments = retriever.get_relevant_documents(query=query, size=5, filters=filters)PreviousAmazon KendraNextArxivSetupAdditional ConfigurationRetrieving documentsAdditional parametersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.
This notebook demonstrates how to use the ArceeRetriever class to retrieve relevant document(s) for Arcee's Domain Adapted Language Models (DALMs).
This notebook demonstrates how to use the ArceeRetriever class to retrieve relevant document(s) for Arcee's Domain Adapted Language Models (DALMs). ->: Setting the model_kwargs at the object initialization uses the filters and size as default for all the subsequent retrievals.retriever = ArceeRetriever(    model="DALM-PubMed",    # arcee_api_key="ARCEE-API-KEY", # if not already set in the environment    arcee_api_url="https://custom-api.arcee.ai", # default is https://api.arcee.ai    arcee_app_url="https://custom-app.arcee.ai", # default is https://app.arcee.ai    model_kwargs={        "size": 5,        "filters": [            {                "field_name": "document",                "filter_type": "fuzzy_search",                "value": "Einstein"            }        ]    })Retrieving documents‚ÄãYou can retrieve relevant documents from uploaded contexts by providing a query. Here's an example:query = "Can AI-driven music therapy contribute to the rehabilitation of patients with disorders of consciousness?"documents = retriever.get_relevant_documents(query=query)Additional parameters‚ÄãArcee allows you to apply filters and set the size (in terms of count) of retrieved document(s). Filters help narrow down the results. Here's how to use these parameters:# Define filtersfilters = [    {        "field_name": "document",        "filter_type": "fuzzy_search",        "value": "Music"    },    {        "field_name": "year",        "filter_type": "strict_search",        "value": "1905"    }]# Retrieve documents with filters and size paramsdocuments = retriever.get_relevant_documents(query=query, size=5, filters=filters)PreviousAmazon KendraNextArxivSetupAdditional ConfigurationRetrieving documentsAdditional parametersCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright ¬© 2023 LangChain, Inc.