Merge pull request #48 from VinciGit00/llama_new_models

Llama new models

scrapegraphai/graphs/abstract_graph.py

@@ -6,6 +6,7 @@ from typing import Optional
 from ..models import OpenAI, Gemini, Ollama, AzureOpenAI
 from ..helpers import models_tokens
 
+
 class AbstractGraph(ABC):
     """
     Abstract class representing a generic graph-based tool.
@@ -19,7 +20,8 @@ class AbstractGraph(ABC):
         self.source = source
         self.config = config
         self.llm_model = self._create_llm(config["llm"])
-        self.embedder_model = None if "embeddings" not in config else self._create_llm(config["embeddings"])
+        self.embedder_model = None if "embeddings" not in config else self._create_llm(
+            config["embeddings"])
         self.graph = self._create_graph()
 
     def _create_llm(self, llm_config: dict):
@@ -39,7 +41,7 @@ class AbstractGraph(ABC):
             except KeyError:
                 raise ValueError("Model not supported")
             return OpenAI(llm_params)
-        
+
         elif "azure" in llm_params["model"]:
             # take the model after the last dash
             llm_params["model"] = llm_params["model"].split("/")[-1]
@@ -48,23 +50,30 @@ class AbstractGraph(ABC):
             except KeyError:
                 raise ValueError("Model not supported")
             return AzureOpenAI(llm_params)
-        
+
         elif "gemini" in llm_params["model"]:
             try:
                 self.model_token = models_tokens["gemini"][llm_params["model"]]
             except KeyError:
                 raise ValueError("Model not supported")
             return Gemini(llm_params)
-        
+
         elif "ollama" in llm_params["model"]:
-            # take the model after the last dash
+            """ 
+            Avaiable models:
+            - llama2
+            - mistral
+            - codellama
+            - dolphin-mixtral
+            - mistral-openorca
+            """
             llm_params["model"] = llm_params["model"].split("/")[-1]
             try:
                 self.model_token = models_tokens["ollama"][llm_params["model"]]
             except KeyError:
                 raise ValueError("Model not supported")
             return Ollama(llm_params)
-        
+
         else:
             raise ValueError("Model not supported")
 

scrapegraphai/graphs/smart_scraper_graph.py

@@ -1,7 +1,6 @@
 """ 
 Module for creating the smart scraper
 """
-from ..models import OpenAI, Gemini
 from .base_graph import BaseGraph
 from ..nodes import (
     FetchNode,

scrapegraphai/graphs/speech_graph.py

@@ -2,7 +2,7 @@
 Module for converting text to speach
 """
 from scrapegraphai.utils.save_audio_from_bytes import save_audio_from_bytes
-from ..models import OpenAI, Gemini, OpenAITextToSpeech
+from ..models import OpenAITextToSpeech
 from .base_graph import BaseGraph
 from ..nodes import (
     FetchNode,
@@ -27,7 +27,7 @@ class SpeechGraph(AbstractGraph):
         super().__init__(prompt, config, source)
 
         self.input_key = "url" if source.startswith("http") else "local_dir"
-        
+
     def _create_graph(self):
         """
         Creates the graph of nodes representing the workflow for web scraping and summarization.

scrapegraphai/helpers/models_tokens.py

@@ -21,9 +21,11 @@ models_tokens = {
         "gemini-pro": 128000,
     },
 
-    "ollama":{
+    "ollama": {
         "llama2": 4096,
         "mistral": 8192,
+        "codellama": 16000,
+        "dolphin-mixtral": 32000,
+        "mistral-openorca": 32000,
     }
-    
 }

scrapegraphai/nodes/fetch_node.py

@@ -67,8 +67,8 @@ class FetchNode(BaseNode):
         # Fetching data from the state based on the input keys
         input_data = [state[key] for key in input_keys]
 
-        source = input_data[0]        
-        
+        source = input_data[0]
+
         # if it is a local directory
         if not source.startswith("http"):
             document = [Document(page_content=source, metadata={

scrapegraphai/nodes/rag_node.py

@@ -8,8 +8,8 @@ from langchain.retrievers import ContextualCompressionRetriever
 from langchain.retrievers.document_compressors import EmbeddingsFilter, DocumentCompressorPipeline
 from langchain_community.document_transformers import EmbeddingsRedundantFilter
 from langchain_community.vectorstores import FAISS
-from ..models import OpenAI, Gemini, Ollama, AzureOpenAI
 from langchain_openai import OpenAIEmbeddings, AzureOpenAIEmbeddings
+from ..models import OpenAI, Ollama, AzureOpenAI
 from langchain_community.embeddings import OllamaEmbeddings
 from .base_node import BaseNode
 
@@ -86,16 +86,18 @@ class RAGNode(BaseNode):
         embedding_model = self.embedder_model if self.embedder_model else self.llm_model
 
         if isinstance(embedding_model, OpenAI):
-            embeddings = OpenAIEmbeddings(api_key=embedding_model.openai_api_key)
+            embeddings = OpenAIEmbeddings(
+                api_key=embedding_model.openai_api_key)
         elif isinstance(embedding_model, AzureOpenAI):
             embeddings = AzureOpenAIEmbeddings()
         elif isinstance(embedding_model, Ollama):
             embeddings = OllamaEmbeddings()
         else:
             raise ValueError("Embedding Model missing or not supported")
-        
-        retriever = FAISS.from_documents(chunked_docs, embeddings).as_retriever()
-    
+
+        retriever = FAISS.from_documents(
+            chunked_docs, embeddings).as_retriever()
+
         redundant_filter = EmbeddingsRedundantFilter(embeddings=embeddings)
         # similarity_threshold could be set, now k=20
         relevant_filter = EmbeddingsFilter(embeddings=embeddings)

scrapegraphai/nodes/search_internet_node.py

@@ -94,7 +94,7 @@ class SearchInternetNode(BaseNode):
         # Execute the chain to get the search query
         search_answer = search_prompt | self.llm_model | output_parser
         search_query = search_answer.invoke({"user_prompt": user_prompt})[0]
-    
+
         print(f"Search Query: {search_query}")
         # TODO: handle multiple URLs
         answer = search_on_web(query=search_query, max_results=1)[0]