LangGraph x GPTR Deep Research

gpt_researcher/actions/retriever.py

@@ -10,62 +10,59 @@ def get_retriever(retriever: str):
         retriever: Retriever class
 
     """
-    match retriever:
-        case "google":
-            from gpt_researcher.retrievers import GoogleSearch
-
-            return GoogleSearch
-        case "searx":
-            from gpt_researcher.retrievers import SearxSearch
-
-            return SearxSearch
-        case "searchapi":
-            from gpt_researcher.retrievers import SearchApiSearch
-
-            return SearchApiSearch
-        case "serpapi":
-            from gpt_researcher.retrievers import SerpApiSearch
-
-            return SerpApiSearch
-        case "serper":
-            from gpt_researcher.retrievers import SerperSearch
-
-            return SerperSearch
-        case "duckduckgo":
-            from gpt_researcher.retrievers import Duckduckgo
-
-            return Duckduckgo
-        case "bing":
-            from gpt_researcher.retrievers import BingSearch
-
-            return BingSearch
-        case "arxiv":
-            from gpt_researcher.retrievers import ArxivSearch
-
-            return ArxivSearch
-        case "tavily":
-            from gpt_researcher.retrievers import TavilySearch
-
-            return TavilySearch
-        case "exa":
-            from gpt_researcher.retrievers import ExaSearch
-
-            return ExaSearch
-        case "semantic_scholar":
-            from gpt_researcher.retrievers import SemanticScholarSearch
-
-            return SemanticScholarSearch
-        case "pubmed_central":
-            from gpt_researcher.retrievers import PubMedCentralSearch
-
-            return PubMedCentralSearch
-        case "custom":
-            from gpt_researcher.retrievers import CustomRetriever
-
-            return CustomRetriever
-
-        case _:
-            return None
+    if retriever == "google":
+        from gpt_researcher.retrievers import GoogleSearch
+        return GoogleSearch
+    elif retriever == "searx":
+        from gpt_researcher.retrievers import SearxSearch
+        return SearxSearch
+    elif retriever == "searchapi":
+        from gpt_researcher.retrievers import SearchApiSearch
+        return SearchApiSearch
+    elif retriever == "serpapi":
+        from gpt_researcher.retrievers import SerpApiSearch
+        return SerpApiSearch
+    elif retriever == "serper":
+        from gpt_researcher.retrievers import SerperSearch
+        return SerperSearch
+    elif retriever == "duckduckgo":
+        from gpt_researcher.retrievers import Duckduckgo
+        return Duckduckgo
+    elif retriever == "bing":
+        from gpt_researcher.retrievers import BingSearch
+        return BingSearch
+    elif retriever == "arxiv":
+        from gpt_researcher.retrievers import ArxivSearch
+        return ArxivSearch
+    elif retriever == "tavily":
+        from gpt_researcher.retrievers import TavilySearch
+        return TavilySearch
+    elif retriever == "exa":
+        from gpt_researcher.retrievers import ExaSearch
+        return ExaSearch
+    elif retriever == "semantic_scholar":
+        from gpt_researcher.retrievers import SemanticScholarSearch
+        return SemanticScholarSearch
+    elif retriever == "pubmed_central":
+        from gpt_researcher.retrievers import PubMedCentralSearch
+        return PubMedCentralSearch
+    elif retriever == "custom":
+        from gpt_researcher.retrievers import CustomRetriever
+        return CustomRetriever
+    elif retriever == "brave":
+        from gpt_researcher.retrievers import BraveSearch
+        return BraveSearch
+    elif retriever == "you":
+        from gpt_researcher.retrievers import YouSearch
+        return YouSearch
+    elif retriever == "perplexity":
+        from gpt_researcher.retrievers import PerplexitySearch
+        return PerplexitySearch
+    elif retriever == "local_documents":
+        from gpt_researcher.retrievers import LocalDocumentRetriever
+        return LocalDocumentRetriever
+    else:
+        raise ValueError(f"Retriever {retriever} not found")
 
 
 def get_retrievers(headers: dict[str, str], cfg: Config):

gpt_researcher/config/config.py

@@ -61,21 +61,22 @@ def _handle_deprecated_attributes(self) -> None:
                 os.environ["EMBEDDING_PROVIDER"] or self.embedding_provider
             )
 
-            match os.environ["EMBEDDING_PROVIDER"]:
-                case "ollama":
-                    self.embedding_model = os.environ["OLLAMA_EMBEDDING_MODEL"]
-                case "custom":
-                    self.embedding_model = os.getenv("OPENAI_EMBEDDING_MODEL", "custom")
-                case "openai":
-                    self.embedding_model = "text-embedding-3-large"
-                case "azure_openai":
-                    self.embedding_model = "text-embedding-3-large"
-                case "huggingface":
-                    self.embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
-                case "google_genai":
-                    self.embedding_model = "text-embedding-004"
-                case _:
-                    raise Exception("Embedding provider not found.")
+            # Replace match statement with if-elif for Python 3.9 compatibility
+            embedding_provider = os.environ["EMBEDDING_PROVIDER"]
+            if embedding_provider == "ollama":
+                self.embedding_model = os.environ["OLLAMA_EMBEDDING_MODEL"]
+            elif embedding_provider == "custom":
+                self.embedding_model = os.getenv("OPENAI_EMBEDDING_MODEL", "custom")
+            elif embedding_provider == "openai":
+                self.embedding_model = "text-embedding-3-large"
+            elif embedding_provider == "azure_openai":
+                self.embedding_model = "text-embedding-3-large"
+            elif embedding_provider == "huggingface":
+                self.embedding_model = "sentence-transformers/all-MiniLM-L6-v2"
+            elif embedding_provider == "google_genai":
+                self.embedding_model = "text-embedding-004"
+            else:
+                raise Exception("Embedding provider not found.")
 
         _deprecation_warning = (
             "LLM_PROVIDER, FAST_LLM_MODEL and SMART_LLM_MODEL are deprecated and "

gpt_researcher/memory/embeddings.py

@@ -27,93 +27,92 @@
 class Memory:
     def __init__(self, embedding_provider: str, model: str, **embdding_kwargs: Any):
         _embeddings = None
-        match embedding_provider:
-            case "custom":
-                from langchain_openai import OpenAIEmbeddings
-
-                _embeddings = OpenAIEmbeddings(
-                    model=model,
-                    openai_api_key=os.getenv("OPENAI_API_KEY", "custom"),
-                    openai_api_base=os.getenv(
-                        "OPENAI_BASE_URL", "http://localhost:1234/v1"
-                    ),  # default for lmstudio
-                    check_embedding_ctx_length=False,
-                    **embdding_kwargs,
-                )  # quick fix for lmstudio
-            case "openai":
-                from langchain_openai import OpenAIEmbeddings
-
-                _embeddings = OpenAIEmbeddings(model=model, **embdding_kwargs)
-            case "azure_openai":
-                from langchain_openai import AzureOpenAIEmbeddings
-
-                _embeddings = AzureOpenAIEmbeddings(
-                    model=model,
-                    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
-                    openai_api_key=os.environ["AZURE_OPENAI_API_KEY"],
-                    openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
-                    **embdding_kwargs,
-                )
-            case "cohere":
-                from langchain_cohere import CohereEmbeddings
-
-                _embeddings = CohereEmbeddings(model=model, **embdding_kwargs)
-            case "google_vertexai":
-                from langchain_google_vertexai import VertexAIEmbeddings
-
-                _embeddings = VertexAIEmbeddings(model=model, **embdding_kwargs)
-            case "google_genai":
-                from langchain_google_genai import GoogleGenerativeAIEmbeddings
-
-                _embeddings = GoogleGenerativeAIEmbeddings(
-                    model=model, **embdding_kwargs
-                )
-            case "fireworks":
-                from langchain_fireworks import FireworksEmbeddings
-
-                _embeddings = FireworksEmbeddings(model=model, **embdding_kwargs)
-            case "ollama":
-                from langchain_ollama import OllamaEmbeddings
-
-                _embeddings = OllamaEmbeddings(
-                    model=model,
-                    base_url=os.environ["OLLAMA_BASE_URL"],
-                    **embdding_kwargs,
-                )
-            case "together":
-                from langchain_together import TogetherEmbeddings
-
-                _embeddings = TogetherEmbeddings(model=model, **embdding_kwargs)
-            case "mistralai":
-                from langchain_mistralai import MistralAIEmbeddings
-
-                _embeddings = MistralAIEmbeddings(model=model, **embdding_kwargs)
-            case "huggingface":
-                from langchain_huggingface import HuggingFaceEmbeddings
-
-                _embeddings = HuggingFaceEmbeddings(model_name=model, **embdding_kwargs)
-            case "nomic":
-                from langchain_nomic import NomicEmbeddings
-
-                _embeddings = NomicEmbeddings(model=model, **embdding_kwargs)
-            case "voyageai":
-                from langchain_voyageai import VoyageAIEmbeddings
-
-                _embeddings = VoyageAIEmbeddings(
-                    voyage_api_key=os.environ["VOYAGE_API_KEY"],
-                    model=model,
-                    **embdding_kwargs,
-                )
-            case "dashscope":
-                from langchain_community.embeddings import DashScopeEmbeddings
-
-                _embeddings = DashScopeEmbeddings(model=model, **embdding_kwargs)
-            case "bedrock":
-                from langchain_aws.embeddings import BedrockEmbeddings
-
-                _embeddings = BedrockEmbeddings(model_id=model, **embdding_kwargs)
-            case _:
-                raise Exception("Embedding not found.")
+        if embedding_provider == "custom":
+            from langchain_openai import OpenAIEmbeddings
+
+            _embeddings = OpenAIEmbeddings(
+                model=model,
+                openai_api_key=os.getenv("OPENAI_API_KEY", "custom"),
+                openai_api_base=os.getenv(
+                    "OPENAI_BASE_URL", "http://localhost:1234/v1"
+                ),  # default for lmstudio
+                check_embedding_ctx_length=False,
+                **embdding_kwargs,
+            )  # quick fix for lmstudio
+        elif embedding_provider == "openai":
+            from langchain_openai import OpenAIEmbeddings
+
+            _embeddings = OpenAIEmbeddings(model=model, **embdding_kwargs)
+        elif embedding_provider == "azure_openai":
+            from langchain_openai import AzureOpenAIEmbeddings
+
+            _embeddings = AzureOpenAIEmbeddings(
+                model=model,
+                azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
+                openai_api_key=os.environ["AZURE_OPENAI_API_KEY"],
+                openai_api_version=os.environ["AZURE_OPENAI_API_VERSION"],
+                **embdding_kwargs,
+            )
+        elif embedding_provider == "cohere":
+            from langchain_cohere import CohereEmbeddings
+
+            _embeddings = CohereEmbeddings(model=model, **embdding_kwargs)
+        elif embedding_provider == "google_vertexai":
+            from langchain_google_vertexai import VertexAIEmbeddings
+
+            _embeddings = VertexAIEmbeddings(model=model, **embdding_kwargs)
+        elif embedding_provider == "google_genai":
+            from langchain_google_genai import GoogleGenerativeAIEmbeddings
+
+            _embeddings = GoogleGenerativeAIEmbeddings(
+                model=model, **embdding_kwargs
+            )
+        elif embedding_provider == "fireworks":
+            from langchain_fireworks import FireworksEmbeddings
+
+            _embeddings = FireworksEmbeddings(model=model, **embdding_kwargs)
+        elif embedding_provider == "ollama":
+            from langchain_ollama import OllamaEmbeddings
+
+            _embeddings = OllamaEmbeddings(
+                model=model,
+                base_url=os.environ["OLLAMA_BASE_URL"],
+                **embdding_kwargs,
+            )
+        elif embedding_provider == "together":
+            from langchain_together import TogetherEmbeddings
+
+            _embeddings = TogetherEmbeddings(model=model, **embdding_kwargs)
+        elif embedding_provider == "mistralai":
+            from langchain_mistralai import MistralAIEmbeddings
+
+            _embeddings = MistralAIEmbeddings(model=model, **embdding_kwargs)
+        elif embedding_provider == "huggingface":
+            from langchain_huggingface import HuggingFaceEmbeddings
+
+            _embeddings = HuggingFaceEmbeddings(model_name=model, **embdding_kwargs)
+        elif embedding_provider == "nomic":
+            from langchain_nomic import NomicEmbeddings
+
+            _embeddings = NomicEmbeddings(model=model, **embdding_kwargs)
+        elif embedding_provider == "voyageai":
+            from langchain_voyageai import VoyageAIEmbeddings
+
+            _embeddings = VoyageAIEmbeddings(
+                voyage_api_key=os.environ["VOYAGE_API_KEY"],
+                model=model,
+                **embdding_kwargs,
+            )
+        elif embedding_provider == "dashscope":
+            from langchain_community.embeddings import DashScopeEmbeddings
+
+            _embeddings = DashScopeEmbeddings(model=model, **embdding_kwargs)
+        elif embedding_provider == "bedrock":
+            from langchain_aws.embeddings import BedrockEmbeddings
+
+            _embeddings = BedrockEmbeddings(model_id=model, **embdding_kwargs)
+        else:
+            raise ValueError(f"Embedding provider {embedding_provider} not found. Please install the missing library with `pip install [pip-package-name]`")
 
         self._embeddings = _embeddings